Adobe Inc.Download PDFPatent Trials and Appeals BoardDec 6, 20212020006058 (P.T.A.B. Dec. 6, 2021) 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. 14/938,690 11/11/2015 Zeke Koch P2287-US 8959 108982 7590 12/06/2021 FIG. 1 Patents 116 W. Pacific Avenue Suite 200 Spokane, WA 99201 EXAMINER DAVANLOU, SOHEILA ART UNIT PAPER NUMBER 2153 NOTIFICATION DATE DELIVERY MODE 12/06/2021 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): Fig1Docket@fig1patents.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte ZEKE KOCH, GAVIN STUART PETER MILLER, JONATHAN W. BRANDT, NATHAN A. CARR, RADOMIR MECH, WALTER WEI-TUH CHANG, SCOTT D. COHEN, and HAILIN JIN ___________ Appeal 2020-006058 Application 14/938,690 Technology Center 2100 __________ Before JAMES B. ARPIN, HUNG H. BUI, and SCOTT RAEVSKY, 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 1–20, all of the pending claims. Final Act. 2.2 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. Appellant identifies the real party-in-interest as Adobe Inc. Appeal Br. 2. 2 In this Decision, we refer to Appellant’s Appeal Brief (“Appeal Br.,” filed May 11, 2020) and Reply Brief (“Reply Br.,” filed August 12, 2020); the Final Office Action (“Final Act.,” mailed December 23, 2019) and the Examiner’s Answer (“Ans.,” mailed July 8, 2020); and the Specification (“Spec.,” filed November 11, 2015). Rather than repeat the Examiner’s findings and Appellant’s contentions in their entirety, we refer to these documents. Appeal 2020-006058 Application 14/938,690 2 STATEMENT OF THE CASE The claimed methods and systems relate to “[c]ontent creation collection and navigation techniques and systems.” Spec. ¶ 4. As noted above, claims 1–20 are pending. Claims 1, 9, and 15 are independent. Appeal Br. 27 (claim 1), 29 (claim 9), 31 (claim 15) (Claims App.). Claims 2–8 depend directly or indirectly from claim 1, claims 10–14 depend directly or indirectly from claim 9, and claims 16–20 depend directly or indirectly from claim 15. Id. at 27–32. Claims 1 and 15, reproduced below with the disputed limitations emphasized, are illustrative. 1. In a digital medium environment for image search result configuration and navigation control, a method implemented by one or more computing devices comprising: obtaining, by the one or more computing devices, a search result including a plurality of licensable images available for licensing from a content sharing service, the plurality of licensable images identified by the content sharing service in response to a search request; generating, by the one or more computing devices, clusters of the plurality of licensable images based on a first metric to measure similarity of the plurality of licensable images, the clusters including images determined to be similar based on the first metric; identifying, by the one or more computing devices and after generating the clusters, at least a second metric to differentiate between clustered images of an individual cluster that have previously been clustered based on the first metric; determining, by the one or more computing devices, a representative licensable image for each of the clusters; generating, by the one or more computing devices, a user interface to display the representative licensable images of the clusters and an image navigation control, the image navigation Appeal 2020-006058 Application 14/938,690 3 control supporting user navigation through the licensable images of a selected cluster based on the at least second metric and responsive to a user selection of the representative licensable image of the selected cluster; receiving, by the one or more computing devices, a request initiated via the user interface to license at least one of the licensable images; and licensing, by the one or more computing devices, the requested licensable image for use. Id. at 27 (emphases added). Independent claim 9 recites limitations corresponding to the disputed limitations of claim 1. Id. at 29; see Ans. 6; Reply Br. 6. 15. In a digital medium environment for image sharing, a method implemented by one or more computing devices of a content sharing service comprising: receiving a plurality of images by the one or more computing devices of the content sharing service; grouping the plurality of images responsive to a determination by the one or more computing devices that the plurality of images exhibit at least a threshold amount of similarity, one to another; identifying, after the grouping of the plurality of images, one or more differentiation metrics that differentiate between the similarity exhibited by the plurality of images previously grouped based on the threshold amount of similarity; and responsive to locating at least one of the plurality of images as corresponding to a search request received by the content sharing service, configuring a search result by the one or more computing devices that includes the at least one said image as representative of a grouping as a whole in the search result, and further configuring the search result with functionality that is selectable to cause output of one or more other said images of the grouping and to enable user navigation through the grouping, the one or more other said images not being output absent the selection, and the user navigation through the grouping enabled Appeal 2020-006058 Application 14/938,690 4 based on the one or more differentiation metrics to differentiate between the similarity exhibited by the one or more images of the grouping. Id. at 31 (emphases added). REFERENCES AND REJECTIONS The Examiner relies upon the following references: Name3 Reference Publ’d/Issued Filed Ciorba US 9,219,830 B1 Dec. 22, 2015 Apr. 3, 2015 Moseley US 2013/0246277 A1 Sept. 19, 2013 Mar. 13, 2012 Howard US 2014/0108931 A1 Apr. 17, 2014 Dec. 30, 2013 The Examiner rejects: (1) claims 1–14 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard, Ciorba, and Moseley (Final Act. 2–16); and (2) claims 15–20 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard and Ciorba (id. at 16–24). 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 the disputed limitations of independent claims 1, 9, and 15 (see Appeal Br. 8, 16, 22; Ans. 3, 6, 7); so we do as well. Arguments not made are forfeited.4 Unless otherwise indicated, we adopt 3 All reference citations are to the first named inventor only. 4 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) (2019) (“Except as provided for in §§ 41.41, 41.47 and 41.52, any arguments Appeal 2020-006058 Application 14/938,690 5 the Examiner’s findings in the Final Office Action and the Answer as our own and add any additional findings of fact for emphasis. We address the rejections below. ANALYSIS A. Obviousness over Howard, Ciorba, and Moseley As noted above, the Examiner rejects claims 1–14 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard, Ciorba, and Moseley. Final Act. 2–16. In particular, the Examiner finds Howard teaches or suggests the majority of the limitations recited in independent claim 1. Id. at 2–4 (citing Howard ¶¶ 9, 19, 32, 34, 35, Figs. 2B–2D). Nevertheless, the Examiner finds, Howard does not specifically teach, “identifying, by the one or more computing devices and after generating the clusters, at least a second metric to differentiate between clustered images of an individual cluster that have previously been clustered based on the first metric.” Id. at 4; see Appeal Br. 27 (Claims App.). The Examiner finds, however, that Ciorba teaches or suggests this limitation (Final Act. 4–5 (citing Ciorba, 27:26–35); see Ciorba, 9:18–20 (defining “Saliency”)) and that a person of ordinary skill in the relevant art would have had reason to combine the teachings of Howard and Ciorba to achieve this limitation (Final Act. 5 (citing Ciorba, 2:32–35)). or authorities not included in the appeal brief will be refused consideration by the Board for purposes of the present appeal.”). Appeal 2020-006058 Application 14/938,690 6 In addition, the Examiner finds Howard and Ciorba together teach or suggest, determining, by the one or more computing devices, a representative licensable image for each of the clusters; generating, by the one or more computing devices, a user interface to display the representative licensable images of the clusters and an image navigation control, the image navigation control supporting user navigation through the licensable images of a selected cluster based on the at least second metric and responsive to a user selection of the representative licensable image of the selected cluster. Final Act. 5–7 (citing Howard ¶¶ 32, 35, 36, Figs. 2A–2M and Ciorba, 11:15–23, 30:14–16). The reason to combine Howard’s teachings with those of Ciorba, cited above, applies equally here. See Ciorba, 2:32–35. The Examiner further finds that Howard and Ciorba fail to teach or suggest, receiving, by the one or more computing devices, a request initiated via the user interface to license at least one of the licensable images; and licensing, by the one or more computing devices, the requested licensable image for use. Final Act. 7; see Appeal Br. 27 (Claims App.). Nevertheless, the Examiner finds that Moseley teaches or suggests these missing limitations (Final Act. 7–8 (citing Moseley ¶ 26, Fig. 2)) and that a person of ordinary skill in the relevant art would have had reason to combine the teachings of Howard and Ciorba with those of Moseley to achieve the methods of claim 1 (id. at 8 (citing Moseley ¶ 7)). Appellant contends the Examiner errs in rejecting independent claim 1 for three reasons. Appeal Br. 8–15. On this record, Appellant does not persuade us the Examiner errs. Appeal 2020-006058 Application 14/938,690 7 1. “generating” limitation First, independent claim 1 recites, “generating . . . clusters of the plurality of licensable images based on a first metric to measure similarity of the plurality of licensable images, the clusters including images determined to be similar based on the first metric.” Appeal Br. 27 (Claims App.) (emphasis added). Appellant contends, “the Examiner asserts Howard for generating ‘clusters of the plurality of licensable images.’ However, Howard is missing generating ‘clusters of the plurality of licensable images,’ in the particular manner claimed.” Id. at 8. For the reasons given below, we disagree with Appellant. The Examiner finds Howard discloses, “[s]earch base modules may include account-based modules such as Flickr 202, mentioned above, and Picasa 203 that present an authentication interface allowing a user to connect to the user’s public and/or private photos at a third party hosting service.” Howard ¶ 34; see Final Act. 3; Ans. 4; see also Howard, Fig. 2E (including “media credits” with license information). Thus, the Examiner finds Howard teaches or suggests clusters of the plurality of licensable images based on a first metric, for example, a search term, such as “sunflowers.” See Final Act. 3 (citing Howard ¶ 35, Figs. 2B–2D). Appellant contends, however, that Howard merely describes clustering images that match a particular search query. The asserted portion of Howard, for example, describes that “typing in ‘sunflowers’ in a Flickr search query results in pictures of sunflowers or photos from Flickr being presented on the user’s [graphical user interface (GUI)]” such that the pictures of sunflowers may be viewed “as photo clusters.” Appeal Br. 9 (quoting Howard ¶ 35). Appellant further contends: Appeal 2020-006058 Application 14/938,690 8 Matching a search term is not a similarity measure though. Indeed, matching a textual search term by two images is not the same as a metric to measure similarity between two different images, such that the two different images may be determined to be similar or dissimilar based on the metric. Id. (emphasis added); see Reply Br. 3. We disagree with Appellant. The Specification discloses: In the illustrated example, images 206, 208, 210 exhibit similarity as including matching objects, with differences being movement of an object (e.g., a basketball) from one image to another. This similarity may be determined in a variety of ways, such as through use of predefined metrics including color, contrast, saturation, use of similar image processing as part of a content creation service 102. . . . In another example, the similarity determination is made through comparison of the images, one to another, such as to determine whether the images 204 have similar objects, perspectives, lighting, and so forth and thus exhibit at least at threshold amount of similarity, one to another. Other examples are also contemplated. . . . Real time examples are also contemplated in which this determination is performed as part of processing of a search result generated in response to a search request. Spec. ¶ 35 (emphases added). Thus, we understand the recited “first metric to measure similarity” broadly encompasses a variety of metrics including a search request for similar objects based on a search term, such as “sunflower.” See id., Figs. 2 (images 206, 208, and 210 of a “basketball”), 4 (images 420, 422, 424, and 426 of a “man” with a “briefcase”); see also id. ¶ 39 (“For instance, a user may perform a search for a ‘businessman’ for use in marketing materials and view image 414.”). Appellant also contends, “[t]here is no mention in Howard of similar images or metrics to measure similarity between images.” Appeal Br. 9. However, Howard’s Figures 2B and 2C depict similar images of Appeal 2020-006058 Application 14/938,690 9 “sunflowers” collected from different sources, i.e., Flickr and Google. Howard ¶ 35 (“A result of the aforementioned search query in Flickr is shown in FIG. 2B. The collected media (sunflowers) may be viewed individually as shown in FIG. 2C or as photo clusters as shown in FIG. 2D.”). Moreover, those images may be clustered as depicted in Howard’s Figure 2D. Id. Thus, Appellant does not persuade us that Howard fails to teach or suggest this disputed limitation. Consequently, we are not persuaded of Examiner error by this first reason. 2. “identifying” limitation Second, independent claim 1 recites, “identifying, by the one or more computing devices and after generating the clusters, at least a second metric to differentiate between clustered images of an individual cluster that have previously been clustered based on the first metric.” Appeal Br. 27 (Claims App.) (emphasis added). In particular, Appellant contends Ciorba fails to teach or suggest, “identifying . . . at least a second metric.” Id. at 11. For the reasons given below, we disagree with Appellant. Initially, Appellant contends, “Ciorba does not first cluster images according to a measure of similarity and then identify a second metric to differentiate between the images in a given cluster of ‘images determined to be similar based on the first metric . . . .’” Id. (emphasis added). As noted above, however, the Examiner relies on Howard, not Ciorba, to teach or suggest the emphasized limitation. See supra Section A.1. 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, Appeal 2020-006058 Application 14/938,690 10 426 (CCPA 1981). Thus, this contention is not persuasive of Examiner error. The Examiner further finds that: As shown above Howard teaches showing clusters of images of the individual cluster (e.g. sunflowers) to differentiate between clustered images based on first metric, Howard does not specifically teach identifying after the generating of the clusters. In addition Ciorba in col. 27, lines 26-35 teaches at least a second metric [Saliency] to differentiate between clustered images of the plurality of licensable images, the at least second metric differentiating between the clustered images of an individual cluster (e.g. specific heuristics image analysis and saliency analysis) that have previously been clustered based on the first metric (e.g. similarity of images). Ans. 4–5. In particular, the Examiner finds the first metric, used to gather and cluster images, may be a search term or an author and time similarity (Howard ¶¶ 35, 37) and the second metric may be “use-case specific heuristics” (Ciorba, 27:26–35). See Ciorba, 9:18–20 (“‘Saliency’ refers to a most relevant and/or most important part of an image as determined by image analysis or user action analysis (user inputs).”). Specifically, Ciorba discloses such heuristics may comprise “grouping images having identical or similar faces (based on the faces that were previously identified), grouping images having identical objects (based on the objects that were previously identified).” Id. at 27:26–31 (emphases added). Thus, the Examiner finds that Howard and Ciorba together teach or suggest gathering and clustering images based on a first metric and differentiating previously clustered images based on a second metric. See Final Act. 4–5, 24–25. We agree with the Examiner. Therefore, Appellant does not persuade us that Ciorba in view of Howard fails to teach or suggest this disputed limitation. Appeal 2020-006058 Application 14/938,690 11 Consequently, we are not persuaded of Examiner error by this second reason. 3. “image navigation control” limitation Third, independent claim 1 recites, “an image navigation control, the image navigation control supporting user navigation through the licensable images of a selected cluster based on the at least second metric and responsive to a user selection of the representative licensable image of the selected cluster.” Appeal Br. 27 (Claims App.) (emphases added). In particular, Appellant contends, the Examiner argues that Ciorba’s “dragging and dropping of photos” is user navigation through a plurality of licensable images. This is incorrect, however. Dragging or dropping is operating on or moving photos. It is not navigating through images, e.g., to view them. Additionally, the Examiner completely divorces the claimed “image navigation control” from the feature that the navigation supported by the control is “based on the at least second metric.” Indeed, dragging and dropping is user based. In other words, dragging and dropping is based on a user’s decision to select a photo, then drag the photo to a location determined by the user, and then drop it. No identification of a second metric is involved in enabling such user interaction. Id. at 12–13 (emphases added). Moreover, Appellant contends that, unlike Ciorba, slider 708, depicted in Appellant’s Figure 7, shows navigation through images ordered based on a “meaningful metric,” as recited in claim 1. Id. at 14–15. We disagree with Appellant. The Examiner finds that “Howard in paragraph [0035] teaches search query results in pictures of sunflowers or photos presented on the user’s GUI or media page and may be viewed (e.g. navigate) individually as shown in FIG. 2C or as photo clusters as shown in FIG. 2D.” Ans. 5; see Final Appeal 2020-006058 Application 14/938,690 12 Act. 3–4. Further, the Examiner finds Ciorba teaches identifying a second metric to differentiate between the clustered images (see supra Section A.2.) and the image navigation control supporting user navigation [e.g. dragging and dropping of photos] through the licensable images [e.g. applied image analysis on analyzed photos] (Ciorba, col. 11, lines 15-23, photo editing or correction may be automatically performed by using an image analysis engine that is capable of extracting photo metadata and applying image heuristics/use cases on analyzed photos . . . a photobook is produced by dragging and dropping of photos) of a selected cluster based on the at least second metric [e.g. analyzed detecting areas of saliency, face detection] (Ciorba, col. 30, lines 14-16, after using the automatic image information derived by image analysis (detecting areas of saliency, face detection), the server outputs a default design preview) (Final Act. 6–7 (italics added)). Thus, the Examiner finds after an initial clustering based on a first metric, e.g., a search term, as taught by Howard, the clustered images may be further clustered and navigated, e.g., analyzed for saliency (e.g., a second metric) and then may be dragged and dropped based on that analysis. Moreover, we understand the “selected cluster [is] based on the at least second metric,” not the image navigation control. Appeal Br. 27 (Claims App.) (emphases added); Spec. ¶ 39 (“Through selection of the representation of this image 414, however, images having different orientations of the businessman are output and selectable by a user for licensing and thus a user may then navigate [these] similar images to locate one of interest.”). Therefore, the Examiner finds Howard and Ciorba together teach or suggest this disputed limitation. Final Act. 6–7. In response to Appellant’s contentions regarding the disclosure of Appellant’s Figure 7, the Examiner further finds, “[w]hile appreciated that Appeal 2020-006058 Application 14/938,690 13 the appellant specification’s has a particular slider to navigate through the various clustered images (see Fig. 7), the claimed user navigation merely recites image navigation control, in other words, which is given [its] plain meaning definition of a interface control to navigate images.” Ans. 5. We agree with the Examiner. Although we may determine the scope of the claims in patent applications not solely on the basis of the claim language, we are careful not to read a particular embodiment appearing in the specification into the claim when the claim language is broader than the embodiment. See SuperGuide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“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, the claim language does not describe the structure of an image navigation control, only that the control functions to “support[] user navigation through the licensable images of a selected cluster based on the at least second metric,” and the Specification does not limit the control to that depicted in Appellant’s Figure 7. See Appeal Br. 27 (Claims App.) (emphases added); Spec. ¶¶ 9 (“FIG. 7 depicts an example implementation of user interfaces in which a user interface image navigation control is configured to support user navigation through images based on one or more metrics.” (emphasis added)); 49 (“Additional examples [to that depicted in Figure 7] are also contemplated as described in further detail below.”). Therefore, we are not persuaded the broad language of claim 1 should be limited to the particular embodiment of Figure 7, and the absence of slider 708 or a similar control from Ciorba does not distinguish this disputed limitation over the combined teachings of Howard and Ciorba. Appeal 2020-006058 Application 14/938,690 14 Consequently, we are not persuaded of Examiner error by this third reason. On this record, we are not persuaded the Examiner errs in rejecting independent claim 1 as obvious over the combined teachings of Howard, Ciorba, and Moseley. As noted above, claim 9 recites limitations corresponding to the disputed limitations of claim 1, and Appellant challenges the rejection of independent claim 9 for substantially the same reasons as claim 1. See Ans. 6; Reply Br. 6. Thus, we sustain the rejection of claims 1 and 9. Appellant does not challenge the obviousness rejection of dependent claims 2–8 and 10–14 separately from their base claims. See Appeal Br. 14. Therefore, we also sustain the rejection of those claims. B. Obviousness over Howard and Ciorba As noted above, the Examiner rejects claims 15–20 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard and Ciorba. Final Act. 16–24. In particular, the Examiner finds Howard teaches or suggests the majority of the limitations recited in independent claim 15. Id. at 16–18 (citing Howard ¶¶ 19, 34–37, Figs. 2B–2E). Nevertheless, the Examiner finds, Howard does not specifically teach, identifying after the grouping of the plurality of images one or more differentiation metrics that differentiate between the similarity exhibited by the plurality of images previously grouped based on the threshold amount of similarity; and the one or more other said images not being output absent the selection the user navigation through the grouping enabled based on the one or more differentiation metrics to differentiate between the similarity exhibited by the one or more images of the grouping. Appeal 2020-006058 Application 14/938,690 15 Id. at 18–19; see Appeal Br. 31 (Claims App.). The Examiner finds, however, that Ciorba teaches or suggests these missing limitations (Final Act. 19–20 (citing Ciorba, 9:18–20, 11:15–23, 27:26–35)) and that a person of ordinary skill in the relevant art would have had reason to combine the teachings of Howard and Ciorba to achieve the methods of claim 15 (id. at 20–21 (citing Ciorba, 2:32–35)). Appellant contends the Examiner errs in rejecting independent claim 15 for two reasons. Appeal Br. 22–25. On this record, Appellant does not persuade us the Examiner errs. 1. “grouping” limitation First, independent claim 15 recites, “grouping the plurality of images responsive to a determination by the one or more computing devices that the plurality of images exhibit at least a threshold amount of similarity, one to another.” Appeal Br. 31 (Claims App.) (emphases added). Appellant contends: Matching a search term, as described in Howard, is not a threshold amount of similarity though. In addition, the mere presentation of pictures that match a search term (e.g., “sunflowers”) in clusters, as described in Howard, does not correspond to “grouping the plurality of images responsive to a determination by the one or more computing devices that the plurality of images exhibit at least a threshold amount of similarity, one to another,” as recited in claim 15. This is because matching or not matching a search term is different from images having at least a threshold amount of similarity between them. Moreover, the only mention of the term “similar” or “similarity” in Howard pertains to “similar search services.” There is no mention in Howard of similar images or amounts of similarity between images. Appeal 2020-006058 Application 14/938,690 16 Id. at 23 (emphasis added; footnote omitted; citing Howard ¶ 44). For the reasons given below, we disagree with Appellant. The Examiner finds, “Howard in paragraph [0037] teaches the further images clustering similarity (time and author) collected within a predetermined time frame (e.g. threshold amount of similarity) by the same author may be clustered together.” Ans. 7; see Final Act. 16–17 (“As such, in one embodiment, those photos collected within a predetermined time frame by the same author may be clustered together (threshold amount of similarity).”). In particular, Howard discloses: If consecutive photos fall within the window they are clustered together. The window duration is chosen based on a heuristic, generally, between for example, 10 seconds and 15 minutes are use based on the search provider. For example, as shown in FIG. 2E, each media item includes credits such as date created, source (Flickr.com), and author (Sankova.O). As such, in one embodiment, those photos collected within a predetermined time frame by the same author may be clustered together. Howard ¶ 37 (emphases added). Thus, the Examiner finds that images obtained by the same search term based on a request from the same source and created by the same author and collected within a particular time frame teach or suggest, “exhibit[ing] at least a threshold amount of similarity, one to another.” Ans. 7; Appeal Br. 31 (Claims App.). Appellant responds that a window of time (e.g., predetermined or otherwise) as described in Howard is not a “threshold amount of similarity” as claimed. This is at least in part because a window of time does not provide a basis for an “amount of similarity,” such that two images may be more or less similar depending on the amount of similarity. Reply Br. 7 (emphasis added). A reference, however, “must be considered for all that it teaches to those of ordinary skill in the art, not just the Appeal 2020-006058 Application 14/938,690 17 embodiments disclosed therein.” In re Arora, 369 F. App’x 120, 122 (Fed. Cir. 2010); see In re Inland Steel Co., 265 F.3d 1354, 1361 (Fed. Cir. 2001); In re Fritch, 972 F.2d 1260, 1264 (Fed. Cir. 1992). Here, the Examiner relies on all that Howard teaches in paragraph 37, specifically, the use of the same search term, the same source, the same author, and the limited time frame to teach or suggest the threshold amount of similarity. Ans. 7. We are not persuaded the Examiner errs in finding Howard teaches or suggests this disputed limitation. Consequently, we are not persuaded of Examiner error by this first reason. 2. “identifying” limitation Second, independent claim 15 recites, “identifying, after the grouping of the plurality of images, one or more differentiation metrics that differentiate between the similarity exhibited by the plurality of images previously grouped based on the threshold amount of similarity.” Appeal Br. 31 (Claims App.). Appellant contends, the Examiner asserts that “grouping images having identical objects (based on the objects that were previously identified)” corresponds to a second metric. Initially, it is asserted that identification of an object is not identification of “a second metric.” The mere use of different known aspects of images to group images does not correspond to “identifying” metrics. Id. at 24 (footnote omitted; quoting Final Act. 19). For the reasons given below, we disagree with Appellant. The Specification discloses: A meaningful metric is also identified to differentiate the images 710, 712, 714, which in this case is orientation of the object in the images. Accordingly, the images 710, 712, 714 are Appeal 2020-006058 Application 14/938,690 18 then ordered based on this identified meaningful metric (e.g., similarity of orientations, one to another) and the slider 708 is used to navigate through this order. In this example, the meaningful metric (e.g., orientation) is chosen to differentiate the basis of the similarity determination (e.g., inclusion of a particular object), although other examples are also contemplated. In this way, similar images may be efficiently located and the meaningful metric utilized to navigate through differences between these similar images. Additional examples are also contemplated as described in further detail below. Spec. ¶ 49 (emphases added). Thus, although “orientation” may be used to differentiate between similar images, other metrics may be used to differentiate between similar images. See id. ¶ 53 (“In this way, rather than solely rely on a user’s ability to arrive at keywords that accurately reflect the user’s desires, additional information from the images themselves is leveraged as part of the search.”). The Examiner finds that Howard teaches grouping clusters of images based on a search term, i.e., sunflowers. Ans. 7. This clustering is based on previously identified objects, such as the search term. Id.; see Final Act. 16– 17 (“For example, selecting the ‘full text’ search criteria and typing in ‘sunflowers’ in a Flickr search query results in pictures of sunflowers or photos from Flickr being presented on the user’s GUI or media page.”). Further, the Examiner finds, Ciorba in col. 27, lines 26-35 teaches at least a second metric (e.g. Saliency) to differentiate between clustered images of the plurality of licensable images, the at least second metric differentiating between the clustered images of an individual cluster (specific heuristics image analysis and saliency analysis) that have previously been clustered based on the first metric (e.g. similarity of images). Ans. 7–8; see Final Act. 19. Specifically, Ciorba discloses Appeal 2020-006058 Application 14/938,690 19 The use-case specific heuristics comprise: grouping images that have previously been found to be similar, grouping images having identical or similar faces (based on the faces that were previously identified), grouping images having identical objects (based on the objects that were previously identified) . . . . Ciorba, 27:26–31 (emphases added); see id. at 9:18–20 (defining “Saliency”). Thus, the Examiner finds that Howard and Ciorba together teach or suggest initially grouping images based on a threshold amount of similarity and then identifying one or more differentiation metrics, such as identical or similar faces or identical objects, to differentiate between the previously grouped images. See Final Act. 20. We agree with the Examiner.5 Consequently, we are not persuaded of Examiner error by this second reason. On this record, we are not persuaded the Examiner errs in rejecting independent claim 15 as obvious over the combined teachings of Howard and Ciorba. Thus, we sustain the rejection of claim 15. Appellant does not challenge the obviousness rejection of dependent claims 16–20 separately. See Appeal Br. 15, 22. Therefore, we also sustain the rejection of those claims. DECISION 1. The Examiner does not err in rejecting: a. claims 1–14 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard, Ciorba, and Moseley; and 5 In the Reply Brief, Appellant does not address the Examiner’s response (Ans. 7–8) to Appellant’s contentions regarding this “identifying” limitation (Appeal Br. 24–25). Appeal 2020-006058 Application 14/938,690 20 b. claims 15–20 under 35 U.S.C. § 103 as obvious over the combined teachings of Howard and Ciorba. 2. Thus, on this record, claims 1–20 are not patentable. CONCLUSION We affirm the Examiner’s rejections of claims 1–20. DECISION SUMMARY In summary: Claim(s) Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 1–14 103 Howard, Ciorba, Moseley 1–14 15–20 103 Howard, Ciorba 15–20 Overall Outcome 1–20 TIME PERIOD FOR RESPONSE 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). AFFIRMED Copy with citationCopy as parenthetical citation