Ex Parte Nandakumar et alDownload PDFPatent Trials and Appeals BoardJun 7, 201913755767 - (D) (P.T.A.B. Jun. 7, 2019) Copy Citation UNITED STA TES p A TENT AND TRADEMARK OFFICE APPLICATION NO. FILING DATE 13/755,767 01/31/2013 89885 7590 06/07/2019 FERENCE & ASSOCIATES LLC 409 BROAD STREET PITTSBURGH, PA 15143 FIRST NAMED INVENTOR Vikrant Nandakumar 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 ATTORNEY DOCKET NO. CONFIRMATION NO. IN920120197US 1(790.182) 1512 EXAMINER JAMI,HARES ART UNIT PAPER NUMBER 2162 MAIL DATE DELIVERY MODE 06/07/2019 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 VIKRANT NANDAKUMAR, NA VEEN K. PRATHAP ANENI, NITHYA RAJAMANI, and L. VENKATA SUBRAMANIAM Appeal2018-007248 Application 13/755,767 1 Technology Center 2100 Before JOSEPH L. DIXON, JAMES W. DEJMEK, and STEPHEN E. BELISLE, Administrative Patent Judges. DEJMEK, Administrative Patent Judge. DECISION ON APPEAL Appellants appeal under 35 U.S.C. § 134(a) from a Final Rejection of claims 1, 3, 8, 10, 11, 13, 18, 20-24, and 26. Appellants have canceled claims 2, 4--7, 9, 12, 14--17, 19, and 25. App. Br. 25. We have jurisdiction over the remaining pending claims under 35 U.S.C. § 6(b ). We affirm. 1 Appellants identify International Business Machines Corporation as the real party in interest. App. Br. 3. Appeal2018-007248 Application 13/755,767 STATEMENT OF THE CASE Introduction Appellants' disclosed and claimed invention generally relates to "gathering and managing crowd-sourced information." Spec. ,r 2. As an example, crowd-sourced information may relate to an event such as a car accident. Spec. ,r 26. If a car accident occurs, crowd-sourced information may provide information such as the location of the accident, the number of cars involved, effects of the accident ( e.g., traffic disruption), from a plurality of different individuals (i.e., members of the crowd). Spec. ,r 26. Crowd-sourced information may take the form of text, speech, audio, image, or video. Spec. ,r 29. The disclosed approach for gathering and managing crowd-sourced information comprises the four steps of (i) event detection; (ii) event classification; (iii) parts identification; and (iv) event completion. Spec. ,r 26. More particularly, in the event detection step, crowd-sourced information is received and, for example, keywords are extracted from received text messages. Spec. ,r 29. The extracted keywords are aggregated to detect that an event has occurred (i.e., determining a number of messages from a particular location mentioning keywords such as "car" and "accident"). Spec. ,r,r 26, 29. During event classification, a rule- or learning-based classifier classifies the messages into one of many known classes and a semantic model is determined. Spec. ,r,r 26, 31. Additional information to complete the semantic model is identified during the parts identification step. Spec. ,r,r 26, 32. In the event completion step, individuals may be identified and questions directed to those identified individuals to elicit more complete information. Spec. ,r,r 26, 33. 2 Appeal2018-007248 Application 13/755,767 Figure 7 is illustrative of a semantic ( event) model and associated parts identification and is reproduced below: .................................................. ' . ' '. t;::'t~ ,~ ... ~:::~hi(~t1$ ~ :.:~w,":;:~v!<:{~ ' ...................... _.'!t .. ···· 137 . -• ............. ·-·. ~· ·- ..................... . ' ' ~HJ~~~l'X..~:C' ~){ ~~":},.::·('::$ : FIG .. 7 ,.- ...... - .................. . ' ' Figure 7 illustrates "an event model [(also referred to as a semantic model)] and associated parts identification" for an event classified as a "Road Accident." Spec. ,r,r 13, 32. In the embodiment depicted in Figure 7, the associated parts that make up the model related to the event "Road Accident" are shown in dotted lines ( e.g., type of injury, level of traffic disruption, and location). According to the Specification, "event models can be predefined or they can be developed over time by learning important facets of information for a given event." Spec. ,r 32. Additionally, the Specification describes that facet discovery relates to the process of identifying different facets of an event. Spec. ,r 32. Claim 1 is representative of the subject matter on appeal and is reproduced below with the disputed limitations emphasized in italics: 3 Appeal2018-007248 Application 13/755,767 1. A method of gathering and managing crowd-sourced information, said method comprising: rece1vmg, at an electronic device, crowd-sourced information, wherein the crowd-sourced information comprises text content and at least one of: audio content, image content, and video content; and utilizing a processor to execute computer code configured to perform the steps of: identifying an event using the crowd-sourced information, wherein the identifying comprises extracting keywords from the text content of the crowd-sourced information and using at least one of: audio processing on the audio content, image processing on the image content, and video processing on the video content; based on said identifying, classifying the event into a predefined class using the extracted keywords; thereafter, invoking a semantic model which is related to the predefined class, wherein the semantic model identifies predefined event characteristics, wherein the predefined event characteristic comprise characteristics corresponding to an event of the predefined class; using the semantic model to identify missing information by: identifying, from the crowd-sourced information, event characteristics matching the predefined event characteristics of the semantic model for the predefined class of the identified event; and identifying the missing information by identifying the predefined event characteristics of the semantic model that are not matched to any event characteristics from the crowd-sourced information; locating one or more individuals in proximity to the event, and harvesting from the located individuals additional crowd- sourced information on the event related to the identified missing information; 4 Appeal2018-007248 Application 13/755,767 said harvesting comprising sending directed questions to the individuals, the directed questions relating to the identified missing information; and completing identification of the event using the harvested missing information, wherein the completing comprises filling in the missing information using the harvested information into the semantic model and identifying the event when the semantic model is complete. The Examiner's Rejection Claims 1, 3, 8, 10, 11, 13, 18, 20-24, and 26 stand rejected under pre- AIA 35 U.S.C. § I03(a) as being unpatentable over Scofield et al. (US 2013/0132434 Al; May 23, 2013 (filed Nov. 22, 2011)) ("Scofield"); Schultz et al. (US 2013/0222133 Al; Aug. 29, 2013 (filed Feb. 29, 2012)) ("Schultz"); and Runciman (US 2006/0282291 Al; Dec. 14, 2006). Final Act. 6-15. 2 ANALYSIS 3 Appellants assert the Examiner erred in finding the cited references teach or reasonably suggest invoking a semantic model related to a 2 We note in the body of the rejection, the Examiner mistakenly identifies the claims are rejected under pre-AIA 35 U.S.C. § I02(e). See Final Act. 6. From the section heading ("Claim Rejections - 35 USC§ 103") and body of the rejection, it is clear the Examiner intended the obviousness rejection to be under 35 U.S.C. § I03(a). See Final Act. 5. Further, Appellants do not allege any prejudice due to the error and acknowledge the claims stand rejected under 35 U.S.C. § I03(a). See App. Br. 25. Accordingly, we treat the Examiner's typographical error as harmless. 3 Throughout this Decision, we have considered the Appeal Brief, filed February 19, 2018 ("App. Br."); the Reply Brief, filed July 9, 2018 ("Reply Br."); the Examiner's Answer, mailed May 8, 2018 ("Ans."); and the Final 5 Appeal2018-007248 Application 13/755,767 predefined class into which an event has been classified and wherein the semantic model identifies predefined event characteristics that comprise characteristics corresponding to an event of the predefined class. App. Br. 28-31. In particular, Appellants argue Scofield "makes no mention of classifying the event and invoking a semantic model." App. Br. 29-30. Rather, Appellants argue Scofield is limited to identifying characteristics about a user, telemetry details, and requesting clarification regarding a previously input location condition. App. Br. 29-30 (citing Scofield ,r,r 38, 40-41 ). Regarding Schultz, Appellants assert that although Schultz teaches using models to assign tags that represent the provided data, Schultz does not describe the model as being a "semantic model" or that the model is related to the predefined class. App. Br. 30. Additionally, Appellants dispute the Examiner's interpretation of the claimed semantic model "as a series of inquiries" about an incident. App. Br. 29 ( quoting Final Act. 4). Rather, Appellants assert a semantic model ( also referred to as an event model) "is a model that includes a hub and associated parts that make up the model." App. Br. 29 (citing Spec. ,r,r 28, 32, Fig. 7). Moreover, Appellants assert a semantic model "is a specific model used to identify the necessary parts to provide a complete picture of the event and to identify which parts are unknown." App. Br. 29. As an initial matter, we note that Appellants' arguments and description of a semantic model are not inconsistent with the Examiner's interpretation. When construing claim terminology during prosecution before the Office, claims are to be given their broadest reasonable Office Action, mailed September 18, 201 7 ("Final Act."), from which this Appeal is taken. 6 Appeal2018-007248 Application 13/755,767 interpretation consistent with the Specification, reading claim language in light of the Specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). We are mindful, however, that limitations are not to be read into the claims from the Specification. In re Van Geuns, 988 F.2d 1181, 1184 (Fed. Cir. 1993). As discussed above, and as set forth in the Specification, an event may be identified and classified into a predefined class using extracted keywords from received crowd-sourced information. Spec. ,r 31, Fig. 6. For example, based on received crowd-sourced information, it may be determined that an accident (i.e., a general class) is being reported and, further, based on extracted words from the information, the type of accident is determined to be a road accident (i.e., a predetermined class of accidents). Spec. ,r 31, Fig. 6. Once classified as a road accident, an associated semantic model is invoked. Spec. ,r 32, Fig. 7. Appellants assert (and the Specification describes) the semantic model (i.e., an event model) comprises a hub classification and associated parts. Spec. ,r 32, Fig. 7; App. Br. 29. In the example embodiments illustrated in Figures 6 and 7, the class into which the event is classified (i.e., "road accident") serves as the "hub classification" of the semantic model. See Spec., Figs. 6, 7. Surrounding the hub are "different parts that are needed to provide a complete picture of the event." App. Br. 29. The Specification describes these different parts as different facets of an event. Spec. ,r 32. For the example of a "road accident" event, facets may include "the type of accident, the number of vehicles involved, whether there are injuries, etc." Spec. ,r 32. The process of identifying these different facets for an event is referred to as facet discovery and, in facet 7 Appeal2018-007248 Application 13/755,767 discovery, "user inputs are received and processed" so as to identify the particular information relevant to that facet. Spec. ,r 32. Consistent with the claim language, a semantic model is associated with the predefined class into which the event has been classified and further comprises one or more characteristics (i.e., facets) that correspond to an event of the predefined class. The characteristics provide additional details describing the event and may be determined from user input. Thus, invoking a series of inquiries and questions about an event, as interpreted by the Examiner, corresponds to identifying appropriate facets (i.e., characteristics) relevant to a particular event. Accordingly, the Examiner's interpretation is consistent with the Specification and how one of ordinary skill in the art would understand related characteristics of the event would be gathered. We next provide a brief review of Scofield and Schultz. Scofield generally relates to receiving and gathering user reports of location-based conditions. Scofield ,r 4, Abstract. Scofield describes that information related to location-based conditions may be received from users and parsed to extract the desired information (i.e., related to the conditions of the location). Scofield ,r,r 4, 24. Additionally, Scofield describes a server receiving such information "may be configured to confirm, clarify, or identify additional details about a location condition by generating and presenting queries to users in the proximity of the location." Scofield ,r 4. Scofield also describes the location-based conditions may include natural and/or weather conditions ( e.g., fog, visibility, ice formation), natural or artificial inanimate objects ( e.g., potholes, landslides, oil, downed power lines), animals, and individuals (e.g., condition of those involved in an 8 Appeal2018-007248 Application 13/755,767 accident). Scofield ,r 33. Further, Scofield discloses that the system may confirm, clarify, or supplement the information received from the user. Scofield ,r 41. Scofield teaches detecting various keywords that have a "known semantic meaning[]" and further detecting and extracting other keywords necessary to infer a location condition. Scofield ,r 43. Additionally, Scofield teaches the use of query template sets for confirming and gathering additional information regarding a location condition. Scofield ,r 44, Fig. 8. Schultz generally relates to generating emergency notification based on aggregate event data. 4 Schultz ,r 1. Schultz receives information ( e.g., text, audio, and video) from a plurality of user devices. Schultz ,r,r 1, 14. Schultz describes an event processor that is configured to correlate the event data received from the different user devices. Schultz ,r 14. The event processor also determines an event type based on the received data. Schultz ,r 19. To recognize an event type from the received data, Schultz teaches the event processor uses one or more of "pattern detection, heuristic analysis, inference or deductive processing, object matching, ontological schemes, and other analysis models." Schultz ,r,r 19, 39. Schultz teaches that event types may include a broad category of an event, a sub-category of an event, a specific object type, and any other tag for characterizing the event. Schultz ,r 20. The tagging of an event may continue as more data is received and event data is correlated and aggregated. Schultz ,r 20. "Tags are generated to represent an event type to which the event data relates or a description of 4 Schultz also contemplates "embodiments [that] have applicability to non- emergency events as well." Schultz ,r 13. 9 Appeal2018-007248 Application 13/755,767 one or more elements of the event data." Schultz ,r 38. Additionally, Schultz teaches providing the results of the analysis (i.e., event tag determination) to the user and may further query the user for additional information, such as whether to send help. Schultz ,r,r 69-70. We do not find Appellants' arguments persuasive of Examiner error. The Examiner finds, and we agree, Scofield teaches extracting keywords from the text content of crowd-sourced information to identify an event. Final Act. 7; see also Scofield ,r,r 24, 33 (extracting keywords from location condition reports to identify a particular condition (e.g., weather condition or inanimate objects affecting the roadway). Moreover, after identifying the incident based on extracted keywords, Scofield teaches the use of (i.e., invoking) a query template to solicit information related to the identified incident from users near the incident. See Scofield ,r,r 24, 40, 41, 44, Fig. 8; see also Final Act. 7. Although Scofield's "query template" may suggest the claimed semantic model, the Examiner relies on Schultz to teach presenting a series of questions and inquiries related to an event (i.e., event characteristics) to a user. Final Act. 7-8 (citing Schultz ,r,r 39, 69-70, Figs. 5A, 5B); see also Ans. 20 (the Examiner explaining "Schultz discloses the feature that based on class/type of an event (e.g.[,] a car accident), event characteristics (e.g.[,] Location of the event, identity of users, or time of the event) are identified and a series of questions and inquiries are determined and presented to a user.") As discussed above, this corresponds to invoking a semantic model related to a predefined class into which an event has been classified and wherein the semantic model identifies predefined event characteristics that comprise characteristics corresponding to an event of the predefined class, as recited in Appellants' claims. 10 Appeal2018-007248 Application 13/755,767 Appellants also dispute the Examiner's proffered reasoning to combine the references as articulated in the Final Rejection. App. Br. 26- 28. In particular, Appellants argue the Examiner has not provided articulated reasoning with rational underpinnings-but rather mere conclusory statements-in support of the proffered motivation to combine the cited references. App. Br. 26-28 ( citing KSR Int 'l v. Teleflex Inc., 550 U.S. 398, 418 (2007)). Moreover, Appellants argue "Schultz, Runciman, and Scofield are directed to completely separate problems and therefore are directed to completely separate solutions" and, accordingly, an ordinarily skilled artisan would not have been motivated to combine the references as proposed by the Examiner. App. Br. 28. Our reviewing court guides it is irrelevant that the prior art and the present invention may have different purposes. See Nat 'l Steel Car, Ltd. v. Canadian Pac. Ry., Ltd., 357 F.3d 1319, 1339 (Fed. Cir. 2004) ("A finding that two inventions were designed to resolve different problems ... is insufficient to demonstrate that one invention teaches away from another."). That is, it is sufficient that references suggest doing what Appellants did, although the Appellants' particular purpose was different from that of the references. In re Heck, 699 F.2d 1331, 1333 (Fed. Cir. 1983) (citing In re Gershon, 372 F.2d 535, 538-39 (CCPA 1967)). "Obviousness is not to be determined on the basis of purpose alone." In re Graf, 343 F.2d 774, 777 (CCPA 1965). Thus, Appellants' arguments that the cited references have different purposes (i.e., "are directed to completely separate solutions") are not persuasive of Examiner error. The key to supporting any prima facie conclusion of obviousness under 35 U.S.C. § 103 is the clear articulation of the reason(s) why the 11 Appeal2018-007248 Application 13/755,767 claimed invention would have been obvious. The Supreme Court in KSR (550 U.S. at 418) noted that the analysis supporting a rejection under 35 U.S.C. § 103 should be made explicit. The Federal Circuit has stated that "rejections on obviousness grounds cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness." In re Kahn, 441 F.3d 977, 988 (Fed. Cir. 2006), cited with approval in KSR, 550 U.S. at 418. "The presence or absence of a motivation to combine references in an obviousness determination is a pure question of fact." Alza Corp. v. Mylan Labs., Inc., 464 F.3d 1286, 1289 (Fed. Cir. 2006) (quoting In re Gartside, 203 F.3d 1305, 1316 (Fed. Cir. 2000)). Moreover, the law does not require that the references expressly articulate a motivation to combine. See, e.g., Arctic Cat Inc. v. Bombardier Recreational Prods. Inc., 876 F.3d 1350, 1359 (Fed. Cir. 2017). The "[m]otivation to combine may be found in many different places and forms .... " Allergan, Inc. v. Sandoz, Inc., 726 F.3d 1286, 1292 (Fed. Cir. 2013); see also Alza, 464 F.3d at 1294 (stating that the motivation to combine does not have to be explicitly stated in the prior art). "[ A ]ny need or problem known in the field of endeavor at the time of invention and addressed by the patent can provide a reason for combining" references. KSR, 550 U.S. at 420; see also DyStar Textilfarben GmbH v. C.H. Patrick Co., 464 F.3d 1356, 1368 (Fed. Cir. 2006) ("[A]n implicit motivation to combine" may result from a desire to make a product or process "stronger, cheaper, cleaner, faster, lighter, smaller, more durable, or more efficient."). 12 Appeal2018-007248 Application 13/755,767 Here, the Examiner finds an ordinarily skilled artisan would have been motivated to combine the teachings of Schultz with those of Scofield "to improve classifying of events based on event information and messages using an event model." Final Act. 8. Additionally, the Examiner finds an ordinarily skilled artisan would have been motivated to combine the teachings of Runciman with Scofield and Schultz "to improve efficiency of the method by ensuring that sufficient amount of event information to identify an incident has been gathered." Final Act. 9. Moreover, in response to Appellants' arguments, the Examiner notes Schultz expressly discloses "'a knowledge base may be maintained for enabling the ongoing and subsequent analysis of events to support response action improvement, event classification improvement, situational/event model development and the like."' Ans. 15 ( quoting Schultz ,r 7 6 ( emphasis omitted)). The Examiner explains the advantage of classifying events using a data model would be to enable real-time analysis of event data to support response action improvement. Ans. 15. The Examiner notes the motivation to combine Schultz and Scofield, therefore, is in the reference itself (i.e., Schultz) as well as in the knowledge generally available to one of ordinary skill in the art. Ans. 15 ( citing Schultz ,r,r 14, 34, 44, 68, 7 6). Similarly, regarding Runciman, the Examiner explains the motivation to combine Runciman with Scofield and Schultz comes from the teachings of Runciman (as well as the knowledge of one skilled in the art). Ans. 16. In particular, the Examiner finds Runciman teaches in order to properly classify an event, related data may be collected by asking a series of questions. Ans. 16 ( citing Runciman ,r,r 34, 145, 148, 149). As the Examiner explains, "selecting 13 Appeal2018-007248 Application 13/755,767 questions and collecting [a] sufficient amount of data and answers, increase the chance of proper classification of the incident." Ans. 16. Scofield teaches "receiving and aggregating reports of location-based conditions received from users, either spontaneously ('I just witnessed an accident') or in response to a query (e.g., 'did you encounter road ice one kilometer ago?')." Scofield, Abstract. As identified by the Examiner, Schultz teaches a data analysis module that "employs various pattern detection, heuristic analysis, inference or deductive processing, object matching, ontological schemes, and other analysis models .... " Schultz ,r 39; see also Final Act. 8. Schultz describes that the data analysis module executes in connection with one or more models for enabling the event to be recognized. Schultz ,r 39. Moreover, as identified by the Examiner, Schultz teaches using a knowledge base (of the determined data) for use in improving responses to events or "defining more accurate event models." Schultz ,r 44; see also Ans. 15. Thus, the Examiner has set forth articulated reasoning with rational underpinning that one of ordinary skill in the art would have been motivated to combine Schultz's teaching of identifying event characteristics with Scofield' s aggregation of location-based condition reports to "improve classifying of events based on event information and messages using an event model." Ans. 15; see also KSR, 550 U.S. at 418. Runciman, as identified by the Examiner (see, e.g., Ans. 16), describes a system "to facilitate incident data collection and classification .... " Runciman ,r 145. Runciman describes a classification model and hierarchy comprising levels of questions (i.e., Main Questions at a high level followed by "specific questions to achieve the desired level of classification"). Runciman ,r,r 148-149. We agree with the Examiner's 14 Appeal2018-007248 Application 13/755,767 explanation that one of ordinary skill in the art would have been motivated to combine Runciman's teaching of "selecting questions and collecting [a] sufficient amount of data" with the combination of Schultz and Scofield "to improve the efficiency of [incident classification] by ensuring that [a] sufficient amount of event information to identify an incident has been gathered." Ans. 16. For the reasons discussed supra, we are unpersuaded of Examiner error. Accordingly, we sustain the Examiner's rejection of representative independent claim 1. For similar reasons, we also sustain the Examiner's rejection of independent claims 10, 11, and 20, which recite commensurate limitations and were not argued separately. See App. Br. 26-31; see also 37 C.F.R. § 4I.37(c)(l)(iv) (2017). Additionally, we sustain the Examiner's rejection of claims 3, 8, 13, 18, 21-24, and 26, which depend directly or indirectly therefrom and were not argued separately. See 37 C.F.R. § 4I.37(c)(l)(iv). DECISION We affirm the Examiner's decision rejecting claims 1, 3, 8, 10, 11, 13, 18, 20-24, and 26 under pre-AIA 35 U.S.C. § 103(a). No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R. § 4I.50(f). AFFIRMED 15 Copy with citationCopy as parenthetical citation