NEC EUROPE LTD.

Patent Trials and Appeals Board

Jan 13, 2022

2020006397 (P.T.A.B. Jan. 13, 2022)

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. 15/110,409 07/08/2016 Salvatore LONGO 815272 2138 95683 7590 01/13/2022 Leydig, Voit & Mayer, Ltd. (Frankfurt office) Two Prudential Plaza, Suite 4900 180 North Stetson Avenue Chicago, IL 60601-6731 EXAMINER HOOVER, BRENT JOHNSTON ART UNIT PAPER NUMBER 2127 NOTIFICATION DATE DELIVERY MODE 01/13/2022 ELECTRONIC 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. Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the following e-mail address(es): chgpatent@leydig.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte SALVATORE LONGO and MIQUEL MARTIN LOPEZ ___________ Appeal 2020-006397 Application 15/110,409 Technology Center 2100 ____________ Before JEAN R. HOMERE, JAMES B. ARPIN, and ADAM J. PYONIN, Administrative Patent Judges. ARPIN, Administrative Patent Judge. DECISION ON APPEAL Appellant1 appeals under 35 U.S.C. § 134(a) from the Examiner’s decision rejecting claims 1-18, all of the pending claims. Final Act. 1.2 We have jurisdiction under 35 U.S.C. § 6(b). We affirm. 1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42 (2012). Appellant identifies the real party-in-interest as NEC Laboratories Europe GmbH. Appeal Br. 1. 2 In this Decision, we refer to Appellant’s Appeal Brief (“Appeal Br.,” filed June 9, 2020) and Reply Brief (“Reply Br.,” filed September 14, 2020); the Final Office Action (“Final Act.,” mailed February 5, 2020), the Advisory Action (“Adv. Act.,” mailed April 9, 2020), and the Examiner’s Answer (“Ans.,” mailed July 23, 2020); and the Specification (“Spec.,” filed July 8, 2016). Rather than repeat the Examiner’s findings and Appellant’s contentions in their entirety, we refer to these documents. Appeal 2020-006397 Application 15/110,409 2 STATEMENT OF THE CASE The claimed methods and systems “relate[] to a method for crowd detection in an area.” Spec., 1:3.3 As noted above, claims 1-18 are pending. Claims 1 and 9 are independent. Appeal Br. 12 (claim 1), 13 (claim 9) (Claims App.). Claims 2-8 depend directly or indirectly from claim 1, and claims 10-18 depend directly or indirectly from claim 9. Id. at 12-14. Claim 1, reproduced below with the disputed limitation emphasized, is representative. 1. A method for crowd detection in an area, comprising: determining, based upon data collected from one or more non-camera sensors, moving patterns of persons in the area and the number of persons within, moving from, and/or to the area over a predetermined time period to obtain model training data sets; assigning each model training data set to represent one of one or more predefined crowd levels in the area; generating a crowd detection model based on the model training data sets; and estimating an actual crowd level for the area using the generated crowd detection model with actual data of moving profiles and/or the actual number of persons within, moving from, and/or to the area over the predetermined time period. Appeal Br. 12 (Claim App.) (emphasis added). Independent claim 9 recites a limitation corresponding to the disputed limitation of claim 1. Id. at 13; see id. at 5; Reply Br. 2. 3 When citing to the Specification, Appellant identifies the numbered paragraphs of Patent Application Publication No. US 2016/0335552 A1, which corresponds to the instant application. Appeal 2020-006397 Application 15/110,409 3 REFERENCES AND REJECTIONS The Examiner relies upon the following references: Name4 Reference Published Filed/Submitted Hua US 2009/0222388 A1 Sept. 3, 2009 Nov. 17, 2008 Xu US 2010/0316257 A1 Dec. 16, 2010 Feb. 19, 2009 Meyn “A Sensor-Utility- Network Method for Estimation of Occupancy in Buildings,” Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, pp. 1494- 1500 Dec. 16-18, 2009 NA Drews “Crowd Behavior Analysis under Cameras Network Fusion using Probabilistic Methods,” 2010 13th Conference on Information Fusion, IEEE, pp. 1-8 July 26, 2010 NA Nakashima “Development of privacy-preserving sensor for person detection,” 2(1) Procedia Social and Behavioral Sciences, pp. 213-17 2010 Oct. 26, 2009 4 All reference citations are to the first named inventor or author only. Appeal 2020-006397 Application 15/110,409 4 The Examiner rejects: (1) claims 1-3, 7, 9, 13, and 18 under 35 U.S.C. § 103 as obvious over the combined teachings of Hua, Drews, and Meyn (Final Act. 3-9); (2) claims 4-6, 10, and 11 under 35 U.S.C. § 103 over the combined teachings of Hua, Drews, Meyn, and Xu (id. at 9-12); (3) claims 8, 12, 15, and 17 under 35 U.S.C. § 103 over the combined teachings of Hua, Drews, Meyn, and Nakashima (id. at 12-14, 15-16); and (4) claims 14 and 16 under 35 U.S.C. § 103 over the combined teachings of Hua, Drews, Meyn, Xu, and Nakashima (id. at 14-15). We review the appealed rejections for error based upon the issues identified by Appellant, and in light of the contentions and evidence produced thereon. Ex parte Frye, 94 USPQ2d 1072, 1075 (BPAI 2010) (precedential). The Examiner and Appellant focus their findings and contentions, respectively, on claim 1; so we do as well. See Final Act 7; Appeal Br. 9-10; Ans. 20-21; Reply Br. 6. Arguments not made are forfeited.5 Unless otherwise indicated, we adopt the Examiner’s findings in the Final Office Action, the Advisory Action, and the Answer as our own and add any additional findings of fact for emphasis. We address the rejections below. 5 See In re Google Tech. Holdings LLC, 980 F.3d 858, 863 (Fed. Cir. 2020) (“Because Google failed to present these claim construction arguments to the Board, Google forfeited both arguments.”); 37 C.F.R. § 41.37(c)(1)(iv) (2012) (“Except as provided for in §§ 41.41, 41.47 and 41.52, any arguments or authorities not included in the appeal brief will be refused consideration by the Board for purposes of the present appeal.”). Appeal 2020-006397 Application 15/110,409 5 ANALYSIS A. Obviousness over Hua, Drews, and Meyn As noted above, the Examiner rejects independent claim 1 as obvious over the combined teachings of Hua, Drews, and Meyn. Final Act. 3-6. The Examiner finds Hua teaches or suggests the majority of the limitations of claim 1. Id. at 3-5 (citing Hua ¶¶ 2, 53, 66-69, 74, 75, 81). In particular, the Examiner finds Hua discloses, “a method of and system for computer automated hierarchical human and crowd characterization and crowd behavioral detection using a video, audio, and a variety of environmental sensors and external information for use with in security monitoring and crowd management.” Hua ¶ 2 (emphasis added); see id., Abstr. (“The method comprises receiving video data but can also include audio data and sensor data.” (emphasis added); see also id. ¶ 85 (“The sensor data includes but is not limited to weather, location, GPS, time data, or any combination thereof.”). Thus, Hua teaches or suggests the use of the non-camera sensors, e.g., environmental sensors, in combination with video and audio sensors.6 The Examiner finds, “Hua fails to explicitly disclose [(1)] assigning each training data set to represent one of one or more predefined crowd levels in the area; [and (2)] based upon data collected from one or more non-camera sensors.” Final Act. 5. Nevertheless, the Examiner finds 6 Pending claim 8 recites that “[t]he method according to claim 1, wherein a privacy preserving sensor is provided in a form of one or more of an environmental sensor . . . , the one or more non-camera sensors comprising the privacy-preserving sensor.” Appeal Br. 13 (Claims App.) (emphasis added); see also Spec., 6:9-19 (describing non-camera environmental and location sensors). Thus, we interpret the non-camera sensors of claim 1 to encompass environmental sensors, such as those disclosed in Hua. See Vitronics Corp. v. Conceptronic, Inc., 90 F.3d 1576, 1582 (Fed. Cir. 1996). Appeal 2020-006397 Application 15/110,409 6 Drews teaches or suggests the step of “assigning each training data set to represent one of one or more predefined crowd levels in the area.” Id. (citing Drews, pg. 4, col. 2; pg. 5, col. 1) (emphasis omitted). Further, the Examiner finds a person of ordinary skill in the relevant art would have had reason to combine the teachings of Hua with those of Drews “to detect behaviors in a crowd using crowd size and activity information.” Id. at 5-6 (citing Drews, pg. 6, cols. 1, 2). In addition, the Examiner finds Meyn teaches or suggests that determining the moving patterns and numbers of persons in an area may be “based upon data collected from one or more non- camera sensors.” Id. at 6 (citing Meyn, pg. 1494, cols. 1, 2; pg. 1497, col. 1, Fig. 3) (emphasis omitted). Further, the Examiner finds a person of ordinary skill in the relevant art would have had reason to combine the teachings of Hua and Drews with those of Meyn “to detect occupancy in a building” and, thereby, to achieve the methods of claim 1. Id. at 6 (citing Meyn, Abstr.). Appellant contends the Examiner errs for two reasons. Appeal Br. 5- 9; Ans. 17. First, Appellant contends Meyn fails to teach or suggest the limitation missing from the combined teachings of Hua and Drews. Appeal Br. 5-7. Second, Appellant contends the Examiner fails to show a prima facie case for obviousness for the combined teachings of Hua, Drews, and Meyn with respect to the methods of claim 1. Id. at 7-9. Neither reason persuades us of Examiner error. 1. Meyn Supplies the Limitation Missing from Hua and Drews First, Appellant contends that, unlike conventional crowd detection methods, which use video surveillance cameras, the methods of claim 1 do not use cameras. Appeal Br. 5. In particular, Appellant notes: Appeal 2020-006397 Application 15/110,409 7 [“]Embodiments of the present invention provide a method and a system for crowd detection ensuring privacy more economical and less error prone than conventional methods and systems.[”] See present specification, at paragraph [0015].7 Costs are reduced by embodiments of the present invention because, for example, [“]the number of persons can be easily determined without expensive cameras.” See present specification, at paragraph [0019] (emphasis added). Privacy is preserved by embodiments of the present invention because, for example, “moving patterns of persons and a number of persons do not require an identification of privacy concerning features of persons,” such as images of their face. See present specification, at paragraphs at [0021] and [0044]. Appeal Br. 5-6; see Reply Br. 4-5. Based on the language of the disputed limitation and the cited disclosures of the Specification, Appellant interprets claim 1 “to require that the data collected from one or more non-camera sensors must be sufficient for determining the moving patterns of persons in the area and the number of persons.” Reply Br. 3 (italics added); see id. at 4 (proposing a narrow definition of “based upon”). Appellant further contends that, unlike the methods of claim 1, both Hua and Drews are camera based systems, that is video data from cameras is a required input for determining crowd size. See, e.g., Hua, at Abstract, paragraphs [0009] and [0048]; see, e.g., Drews, at Abstract, Section 2. While these systems may consider the possibility of other sensors (e.g., weather, audio) their systems work only in the case the image/video data is present. See, e.g., Hua, at Abstract, paragraphs [0009] and [0048]. On the other hand, independent claim 1 and claim 9 specify that it is “non-camera sensors” that determine moving patterns and the number of persons. Appeal Br. 6; see id. at 7 (“To be sure, Meyn mentions non-camera sensors, but Meyn never states or suggest[s] that these non-camera bases sensors are 7 The text of paragraph 15 does not appear in the application as filed. Appeal 2020-006397 Application 15/110,409 8 alone sufficient to perform occupancy detection. This is what the Office is required to establish.”). The Examiner interprets the disputed limitation differently, concluding that “[i]t is important to note that, while Meyn does disclose the use of camera-based sensors in conjunction with non-camera based sensors, the claims do not require that exclusively non-camera-based sensors are used to determine moving patterns and a number of persons.” Ans. 18 (emphasis added). We agree with the Examiner’s interpretation because claim 1 does not recite a negative limitation or otherwise limit the methods to using only “one or more non-camera sensors.” Further, as noted above, the Examiner finds Meyn discloses, “[s]ensors may include CO2, passive infrared (PIR), video, sound, badge counters, and even measurements of the number of cars in the parking lot.” Id. at 17 (quoting Meyn, pg. 1494, col. 1; bolding added); see Meyn, Abstr. (“The newly proposed method was evaluated via experiments in an office building environment. Estimation accuracy is shown to improve significantly when all available data is incorporated in the estimator.”), pg. 1497, col. 1 (“Three classes of sensors were used in the experiments: (i) Digital Video Cameras, (ii) Passive Infrared (PIR) Detectors, and (iii) CO2 sensors.”). Thus, although Meyn expresses a preference for using data from multiple sensor types, Meyn teaches or suggests that non-camera sensors may be used alone or in combination with other sensors. See Ans. 18; cf. In re Gurley, 27 F.3d 551, 553 (Fed. Cir. 1994) (“A known or obvious composition does not become patentable simply because it has been described as somewhat inferior to some other product for the same use.”). We agree with the Examiner’s interpretation of claim 1, and, thus, Appellant’s contention is not persuasive of Examiner error. Appeal 2020-006397 Application 15/110,409 9 The Office applies to the verbiage of the proposed claims the broadest reasonable meaning of the words in their ordinary usage as they would be understood by one of ordinary skill in the art, taking into account whatever enlightenment by way of definitions or otherwise that may be afforded by the written description contained in the applicant’s specification. In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997). We are careful, however, not to read a particular embodiment appearing in the written description into the claim if the claim language is broader than the embodiment. See SuperGuide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by the explanations contained in the written description, it is important not to import into a claim limitations that are not a part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment.”). Here, claim 1 recites, “determining, based upon data collected from one or more non-camera sensors, moving patterns of persons in the area and the number of persons within, moving from, and/or to the area over a predetermined time period to obtain model training data sets.” Appeal Br. 12 (Claims App.) (emphasis added). The plain meaning of the emphasized language is that the determination of moving patterns and numbers requires data collected from a non-camera sensor or sensors. See Spec., 6:9-19 (describing a plurality of non-camera sensor types). However, this language does not prohibit the use of other data from other sensors, especially, where, as here, the claim preamble uses the transitional word “comprising.” Genentech, Inc. v. Chiron Corp., 112 F.3d 495, 501 (Fed. Appeal 2020-006397 Application 15/110,409 10 Cir. 1997) (“‘Comprising’ is a term of art used in claim language which means that the named elements are essential, but other elements may be added and still form a construct within the scope of the claim.”). Although Appellant identifies several disclosures that suggest that reliance on cameras is not preferred, e.g., may be inaccurate and/or expensive, the Specification also discloses at least one embodiment in which cameras and non-camera sensors may be used together. In particular, the Specification discloses: According to the invention it has been further recognized that the model training data sets can be obtained with high accuracy over a predetermined period of time therefore resulting in lesser costs, since for example expensive video cameras for obtaining a model data set can be lent for some days which is much more cost- effective than buying and maintaining the cameras. A complete camera system would take at least as many cameras as there are accesses to an area while a camera system needed temporarily for training would only have to cover selected areas. Spec., 3:30-4:3 (emphasis added).8 Thus, as the Examiner finds, the claim language does not require that the determination of moving patterns and numbers of persons in the area must be based solely on data from non- camera sensors, and this interpretation is consistent with the Specification. Ans. 18; see Adv. Act. 2 (“Nowhere in Meyn is it suggested that the method of Meyn must use a camera-based sensor in combination with other non- camera based sensors. The recitation of [CO2] sensors with other types of sensors were merely illustrative, not required.”). Consequently, we are not persuaded that the Examiner errs in interpreting the disputed limitation of claim 1. Further, we are not persuaded that the Examiner errs in finding (1) that Meyn teaches the use of 8 This text appears at paragraph 20 of the printed publication. See supra note 3. Appeal 2020-006397 Application 15/110,409 11 non-camera sensors, alone or in combination with cameras, or (2) that Meyn teaches or suggests the portion of the disputed limitation of claim 1, which the Examiner relies upon Meyn to teach or suggest. 2. The Examiner Shows a Prima Facie Case for Obviousness Second, Appellant contends the Examiner fails “to satisfy its burden on providing the factual support for its prima facie conclusions of obviousness.” Appeal Br. 7. In particular, “the Office’s proffered reasons to combine the prior art do not have a rational connection between facts and the conclusion that the invention as a whole is obvious. In particular, Appellant respectfully submits that the rationale offered by the Office has unresolved gaps in its logic.” Id. at 8. Specifically, Appellant contends: Hua is said to disclose obtaining the model training data sets, but admittedly not based on non-camera data. See Detailed Action, at pages 3 and 5. Drews is not used to supply using non-camera data to obtain model training data sets. See Detailed Action, at page 6. Meyn is said only to use non-camera data to determine occupancy estimates, but not to determine moving patterns or to obtain model training data sets. Thus, while (as discussed above) Meyn’s teachings are ultimately camera dependent, even taking the Office’s statement of the disclosure as a given, the prior art still lacks the teaching or suggestion for using non- camera data to determine moving patterns or for obtaining model training data sets. Id. Initially, we note that, under the proper interpretation of claim 1, the claim language does not exclude the use of camera data in determining, “moving patterns of persons in the area and the number of persons within, moving from, and/or to the area over a predetermined time period to obtain model training data sets.” See supra Section A.1. Moreover, the Examiner relies on Hua to teach or suggest the majority of the disputed limitation Appeal 2020-006397 Application 15/110,409 12 (Final Act. 3-4; see Hua ¶ 2 (“crowd characterization and crowd behavioral detection using a video, audio, and a variety of environmental sensors”)) and on Meyn to teach or suggest that the recited “determination” may be based in whole or part on non-camera sensors (Final Act. 6; see Meyn, pg. 1494, col. 1 (“Sensors may include CO2 . . . ,”)). Figure 3 depicts Digital Video Cameras, Passive Infrared (PIR) detectors, and CO2 sensors deployed to detect the occupancy of and occupant flow in a building. Meyn, pg. 1497-1500. We are persuaded the Examiner shows sufficient reason to combine the teachings of Hua with those of Meyn to achieve the disputed limitation of claim 1, as properly interpreted. Final Act. 6 (citing Meyn, Abstr.); see Meyn, Abstr. (quoted above). Appeal 2020-006397 Application 15/110,409 13 The Examiner relies on the combined teachings of Hua and Meyn to teach or suggest the disputed limitation. Here, the Examiner finds Hua suggests determining moving patterns of persons and the number or rate of people moving from or going to an area from sensors for a predetermined time period and that model training data is obtained from such sensors. Final Act. 3-4 (citing Hua ¶¶ 67, 74, 75, 81); see Hua ¶ 2 (quoted above). The Examiner finds that Meyn’s teachings in combination with those of Hua teach or suggest that such determinations and training data may be based on data from non-camera sensors. Final Act. 6 (citing Meyn, Abstr., pg. 1494, cols. 1, 2; pg. 1497, col. 1, Fig. 3). Appellant cannot show nonobviousness by attacking references individually when the rejection is based on the references’ combined teachings. See In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986); In re Keller, 642 F.2d 413, 426 (CCPA 1981). For the above reasons, we are not persuaded the Examiner errs in rejecting independent claim 1 as obvious over the combined teachings of Hua, Drews, and Meyn, and we sustain the Examiner’s rejection of claim 1. Appellant does not challenge the rejection of independent claim 9 or of the claims dependent from independent claim 1 or 9, separately from the challenge to claim 1. Appeal Br. 9-10; Reply Br. 6. Consequently, we also sustain the rejection of claims 2, 3, 7, 9, 13, and 18. B. Remaining Rejections As noted above, the Examiner also rejects claims 4-6, 10, and 11 over the combined teachings of Hua, Drews, Meyn, and Xu; claims 8, 12, 15, and 17 over the combined teachings of Hua, Drews, Meyn, and Nakashima; and claims 14 and 16 over the combined teachings of Hua, Drews, Meyn, Xu and Nakashima. Final Act. 9-16. Each of claims 4-6, 8, 10-12, and 14-17 Appeal 2020-006397 Application 15/110,409 14 is dependent from one of independent claims 1 and 9. Appeal Br. 12-14 (Claims App.). Appellant does not challenge the rejections of these dependent claims separately from the rejection of independent claims 1 and 9. Appeal Br. 9-10; Reply Br. 6; see Ans. 20-21. Because we are not persuaded the Examiner errs in rejecting claim 1 or 9 (see supra Section A), on this record, we also are not persuaded the Examiner errs in rejecting dependent claims 4-6, 8, 10-12, and 14-17. Consequently, we sustain the rejections of those dependent claims. DECISION 1. The Examiner does not err in rejecting: a. claims 1-3, 7, 9, 13, and 18 as obvious over the combined teachings of Hua, Drews, and Meyn; b. claims 4-6, 10, and 11 over the combined teachings of Hua, Drews, Meyn, and Xu; c. claims 8, 12, 15, and 17 over the combined teachings of Hua, Drews, Meyn, and Nakashima; and d. claims 14 and 16 over the combined teachings of Hua, Drews, Meyn, Xu, and Nakashima. 2. Thus, on this record, claims 1-18 are not patentable. Appeal 2020-006397 Application 15/110,409 15 CONCLUSION We affirm the Examiner’s rejections of claims 1-18. In summary: Claim(s) Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 1-3, 7, 9, 13, 18 103 Hua, Drews, Meyn 1-3, 7, 9, 13, 18 4-6, 10, 11 103 Hua, Drews, Meyn, Xu 4-6, 10, 11 8, 12, 15, 17 103 Hua, Drews, Meyn, Nakashima 8, 12, 15, 17 14, 16 103 Hua, Drews, Meyn, Xu, Nakashima 14, 16 Overall Outcome 1-18 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. § 1.136(a)(1)(iv) (2013). AFFIRMED