VMware, Inc.Download PDFPatent Trials and Appeals BoardDec 21, 20202020003854 (P.T.A.B. Dec. 21, 2020) 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. 14/080,661 11/14/2013 Dani Matzlavi B344 1059 152606 7590 12/21/2020 Olympic Patent Works PLLC 4979 Admiral Street Gig Harbor, WA 98332 EXAMINER GOLDBERG, IVAN R ART UNIT PAPER NUMBER 3619 MAIL DATE DELIVERY MODE 12/21/2020 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 DANI MATZLAVI and MEIDAD ETZ-HADAR ___________ Appeal 2020-003854 Application 14/080,661 Technology Center 3600 ____________ Before JASON V. MORGAN, ERIC B. CHEN, and JEREMY J. CURCURI, Administrative Patent Judges. CHEN, Administrative Patent Judge. DECISION ON APPEAL Appeal 2020-003854 Application 14/080,661 2 STATEMENT OF THE CASE Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the Examiner’s decision to reject claims 1–24. We have jurisdiction under 35 U.S.C. § 6(b). We AFFIRM. CLAIMED SUBJECT MATTER The claims are directed to aiding an enterprise in deciding whether to execute an application entirely within a private cloud or a hybrid combination of the private cloud and public cloud services. (Abstract.) Claim 1, reproduced below, is illustrative of the claimed subject matter, with disputed limitations in italics: 1. A system for aiding an enterprise in deciding to execute an application in a private cloud or in a hybrid private cloud and public cloud, the system comprising: one or more processors; one or more data-storage devices; and machine-readable instructions stored in the data-storage devices that when executed using the one or more processors performs operations comprising: receiving a set of quantitative parameters associated with running the application using computational services provided by a public cloud service provider; receiving a set of quantitative organizational parameters associated with the enterprise; 1 We use the word “Appellant” to refer to “applicant” as defined in 37 C.F.R. § 1.42(a). Appellant identifies the real party in interest as VMWARE, INC. (Appeal Br. 1.) Appeal 2020-003854 Application 14/080,661 3 dividing each of the quantitative parameters by a square root of a sum of squares of the quantitative parameters to generate corresponding normalized quantitative parameters; dividing each of the quantitative organizational parameters by a square root of a sum of squares of the quantitative organizational parameters to generate corresponding normalized quantitative organizational parameters; training a decision model based on historical quantitative parameters and historical quantitative organizational parameters; using the decision model to compute a recommendation that indicates exclusive use of a private cloud or use of a hybrid private cloud and public cloud to execute the application based on the normalized quantitative parameters and the normalized quantitative organizational parameters; and executing the application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation. REFERENCES Name Reference Date Ferris et al. US 2011/0295999 A1 Dec. 1, 2011 JIAWEI HAN ET AL., DATA MINING: CONCEPTS AND TECHNIQUES 228–231 (3rd ed. 2012). Ake Edlund & Maarten Koopmans, Practical Cloud Evaluation from a Nordic eScience User Perspective, VIRTUALIZATION TECHNOLOGIES IN DISTRIBUTED COMPUTING 29–37 (2011). David Bazell & Yuan Peng, A Comparison of Neural Network Algorithms and Preprocessing Methods for Star-Galaxy Discrimination, THE ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 47–55 (1998). Appeal 2020-003854 Application 14/080,661 4 REJECTIONS Claims 1–5, 7–11, 13–17, and 19–24 stand rejected under 35 U.S.C. § 103 as being unpatentable over Ferris, Han, and Bazell.2 Claims 6, 12, and 18 stand rejected under 35 U.S.C. § 103 as being unpatentable over Ferris, Han, Bazell, and Edlund.3 OPINION § 103 Rejection—Ferris, Han, and Bazell First, we are unpersuaded by Appellant’s arguments (Appeal Br. 10– 11; see also Reply Br. 2) that the combination of Ferris, Han, and Bazell would not have rendered obvious independent claim 1, which includes the limitation “receiving a set of quantitative parameters associated with running the application using computational services provided by a public cloud service provider.” The Examiner found that deployment rules 334 of Ferris, including requirements for software programs (e.g., cloud resources, processor capacity, or bandwidth capacity), correspond to the limitation “receiving a set of quantitative parameters associated with running the application using computational services provided by a public cloud service provider.” (Final Act. 5–6; see also Ans. 3–5.) We agree with the Examiner’s findings. Ferris relates to cloud computing, in particular, “analyzing cloud deployment solutions taking into account the relative importance of various 2 The Examiner inadvertently omitted some dependent claims from the statement of the rejection. 3 Appellant does not present any arguments with respect to the rejection of dependent claims 6, 12, and 18 under 35 U.S.C. §103. Thus, any such arguments are deemed to be waived. Appeal 2020-003854 Application 14/080,661 5 cloud resources.” (¶ 1.) Figure 3 of Ferris illustrates decision system 302, which communicates with clouds 304 and 306, via networks 308. (¶ 42.) Ferris explains that “decision system [302] can be configured to obtain cloud resource usage data representing cloud service consumption by a specific user” (¶ 16) and includes customization module 332 that maintains a set of deployment rules 334 (¶ 62). Moreover, Ferris explains that “deployment rules 334 can be configured to store rules, algorithms, decision logic, or best practices for deploying resources in a computing cloud environment such as the clouds 304 and 306.” (¶ 62.) In particular, Ferris explains that “a rule can specify that deployment architecture must include cloud resources sufficient to meet the peak requirement for each resource that is recorded in utilization data 338” or “a rule can specify that if an application’s processor capacity utilization over the course of a period of time is less than ten percent, then the application is an excellent candidate for virtualization.” (¶ 63.) For example, Ferris provides the following: “baseline requirement for a user’s computation-intensive application(s) is 7.0 Gbytes of memory capacity, processor capacity equivalent to two 2.0 GHz, Intel™ Core 2™ T7200 processors, 700 Gbytes of storage capacity, and 1 Gbit/s of network throughput capacity.” (¶ 89.) Because Ferris explains that decision system 302 obtains cloud resource usage data and includes deployment rules 334, such as determining if cloud resources meet the peak requirement or process capability utilization, Ferris teaches the limitation “receiving a set of quantitative parameters associated with running the application using computational services provided by a public cloud service provider.” Appellant argues that “the first step describes an operation in which a set of quantitative parameters are received as input” (Appeal Br. 10) and Appeal 2020-003854 Application 14/080,661 6 “[t]he term ‘quantitative parameters’ in claim 1 is referring numerical quantities or values and the first step of claim 1 describes receiving these quantitative parameters” (id. at 11 (emphasis omitted); see also Reply Br. 2). However, Ferris explains that “the decision system [302] can be configured to obtain cloud resource usage data representing cloud service consumption by a specific user.” (¶ 16; see also ¶¶ 62–63.) Thus, we agree with the Examiner that the combination of Ferris, Han, and Bazell would have rendered obvious independent claim 1, which includes the limitation “receiving a set of quantitative parameters associated with running the application using computational services provided by a public cloud service provider.” Second, we are unpersuaded by Appellant’s arguments (Appeal Br. 16–17; see also Reply Br. 2–3) that the combination of Ferris, Han, and Bazell would not have rendered obvious independent claim 1, which includes the limitation “receiving a set of quantitative organizational parameters associated with the enterprise.” The Examiner found that resource importance data 335 of Ferris, which assigns weighting factors 9 on a scale of 1–10, correspond to the limitation “receiving a set of quantitative organizational parameters associated with the enterprise.” (Final Act. 6–7; see also Ans. 5–9.) We agree with the Examiner’s findings. Ferris explains that “the set of resource importance data 335 can include weighting data for cloud resources needed by the applications or processes associated with a user, where the weighting data associated with a cloud resource indicates that resource’s relative importance among the set of needed resources.” (¶ 64; see also id. ¶ 25.) In particular, Ferris explains Appeal 2020-003854 Application 14/080,661 7 that “the resource importance data for this case indicates high importance for processing capacity resources (e.g., a weighting factor of 9 on a scale of 1– 10, ten being the most important), low importance for storage capacity resources (e.g., a weighting factor of 2), and average importance for memory capacity resources and throughput capacity resources (e.g., a weighting factor of 5).” (¶ 89.) Because Ferris explains that resource importance data 335 is provided with a weighting factor on a scale of 1–10 for cloud resources, Ferris teaches the limitation “receiving a set of quantitative organizational parameters associated with the enterprise.” Appellant argues that “the second step describes an operation in which a set of quantitative organizational parameters associated with the enterprise are received as input” and “none of the paragraphs the Examiner cites in Ferris describes an operation in which a set of parameters are received and that the set of parameters satisfy the limitations described in the second step.” (Appeal Br. 16; see also Reply Br. 2–3.) Again, as discussed previously, Ferris explains that “the decision system [302] can be configured to obtain cloud resource usage data representing cloud service consumption by a specific user.” (¶ 16; see also ¶¶ 62–63.) Appellant further argues that “Ferris mentions parameters in paragraph [0025], but the parameters refer to user instantiation requests” and “[t]here is no teaching or suggestion that the quantitative parameters of paragraph [0025] are . . . associated with how the user is organized.” (Appeal Br. 17.) However, Appellant’s arguments are not commensurate in scope with claim 1, because the claim does not require “associated with how the user is organized.” As discussed previously, the limitation “organizational parameters associated with the enterprise” is broad enough Appeal 2020-003854 Application 14/080,661 8 to encompass resource importance data 335 of Ferris, which provides a weighting factor for cloud resources. Thus, we agree with the Examiner that the combination of Ferris, Han, and Bazell would have rendered obvious independent claim 1, which includes the limitation “receiving a set of quantitative organizational parameters associated with the enterprise.” Third, we are unpersuaded by Appellant’s arguments (Appeal Br. 19– 21; see also Reply Br. 4–5) that the combination of Ferris, Han, and Bazell would not have rendered obvious independent claim 1, which includes the limitations “dividing each of the quantitative parameters by a square root of a sum of squares of the quantitative parameters to generate corresponding normalized quantitative parameters” and “dividing each of the quantitative organizational parameters by a square root of a sum of squares of the quantitative organizational parameters to generate corresponding normalized quantitative organizational parameters.” The Examiner found that normalizing input data of Bazell by dividing each component by the square root of the sum of squares of all components, corresponds to the limitation “dividing each . . . by a square root of a sum of squares . . . to generate corresponding normalized quantitative organizational parameters.” (Final Act. 8.) The Examiner concluded that it would have been obvious to combine Ferris and Bazell because: The application of the known technique in Bazell would have yielded no more than the predictable outcome which one of ordinary skill would have expected to achieve, i.e. the ability to utilize the particular kind of normalizing in Bazell for the data of Ferris . . . by dividing each component by the square root of the sum of squares of all the components and using this normalized data. Appeal 2020-003854 Application 14/080,661 9 (Final Act. 14.) We agree with the Examiner’s findings and conclusions. Bazell relates to the “automatic processing of data.” (P. 47, col. 1, § 1.) In particular, Bazell explains that “[t]he input data were normalized by dividing each component of the input vector by the square root of the sum of squares of all components.” (P. 50, col. 1, § 3.2.) Thus, the combination of Ferris and Bazell is nothing more than applying the known data processing method of Bazell, in which input data are normalized by dividing each component of the data by the square root of the sum of squares, with the known deployment rules 334 and resource importance data 335 of Ferris, to yield predictable results. See KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”). This combination of Ferris and Bazell would result in the processing deployment rules 334 and resource importance data 335 of Ferris using the known normalization method of Bazell, in which deployment rules 334 and resource importance data 335 are divided by the square root of the sum of squares of the data. Accordingly, the combination of Ferris and Bazell teaches the limitations “dividing each of the quantitative parameters by a square root of a sum of squares of the quantitative parameters to generate corresponding normalized quantitative parameters” and “dividing each of the quantitative organizational parameters by a square root of a sum of squares of the quantitative organizational parameters to generate corresponding normalized quantitative organizational parameters.” Thus, we agree with the Examiner (Final Act. 14) that modifying Ferris with Bazell would have been obvious. Appeal 2020-003854 Application 14/080,661 10 Appellant argues the following: [T]he Examiner has not provided an explanation of how the normalization process described in the cited passages of Bazell would be applied to the cited passages of Ferris. (Appeal Br. 19.) By not providing an explanation of how Bazell’s teaching of normalization is applied to the descriptions in the cited passages in paragraphs [0025], [0062], [0063], [0064], and [0089] of Ferris to obtain the third and fourth steps of claim 1, the Examiner appears to done nothing more than search prior art reference for common terms and limitations without regard to whether or the teachings of the reference can actually be combined with a reasonable expectation of success. (Id. at 21; see also Reply Br. 4–5.) Contrary to Appellant’s arguments, as discussed previously, the combination of Ferris with Bazell is based on the combination of familiar elements. (Final Act. 14.) Thus, the Examiner has provided sufficient articulated reasoning with some rational underpinning for combining Ferris and Bazell to support a conclusion of obviousness. Thus, we agree with the Examiner that the combination of Ferris, Han, and Bazell would have rendered obvious independent claim 1, which includes the limitations “dividing each of the quantitative parameters by a square root of a sum of squares of the quantitative parameters to generate corresponding normalized quantitative parameters” and “dividing each of the quantitative organizational parameters by a square root of a sum of squares of the quantitative organizational parameters to generate corresponding normalized quantitative organizational parameters.” Fourth, we are unpersuaded by Appellant’s arguments (Appeal Br. 23–24; see also Reply Br. 8–11) that the combination of Ferris, Han, and Bazell would not have rendered obvious independent claim 1, which Appeal 2020-003854 Application 14/080,661 11 includes the limitation “training a decision model based on historical quantitative parameters and historical quantitative organizational parameters.” The Examiner found that the data classification and decision tree of Han corresponds to the limitation “training a decision model.” (Final Act. 9.) The Examiner concluded that it would have been obvious to combine Ferris and Han because: Han improves upon Ferris by explicitly disclosing having learning/training of a decision model such as a decision tree. One of ordinary skill in the art would be motivated to further include training data and utilizing information gain to select the best choice for efficiently traversing a decision system of Ferris to use information gain to improve the decision model tree. (Id. at 13 (citations omitted).) We agree with the Examiner’s findings and conclusions. As discussed previously, Figure 3 of Ferris illustrates decision system 302 which communicates with clouds 304 and 306, via networks 308 (¶ 42) and explains that “deployment rules 334 can be configured to store rules, algorithms, decision logic, or best practices for deploying resources in a computing cloud environment such as the clouds 304 and 306” (¶ 62). Han explains the following: Decision tree induction is the learning of decision trees from class-labeled training tuples. A decision tree is a flowchart-like tree structure, where each internal node (nonleaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a class label. (P. 330, § 8.2 (emphasis omitted).) Decision trees can handle multidimensional data. Their representation of acquired knowledge in tree form is intuitive and generally easy to assimilate by humans. The learning and Appeal 2020-003854 Application 14/080,661 12 classification steps of decision tree induction are simple and fast. In general, decision tree classifiers have good accuracy. However, successful use may depend on the data at hand. Decision tree induction algorithms have been used for classification in many application areas such as medicine, manufacturing and production, financial analysis, astronomy, and molecular biology. (Id. at 331.) A person of ordinary skill in the art would have recognized that incorporating the decision tree of Han, with decision system 302 of Ferris, would improve Ferris by providing the ability to handle multidimensional data, with simple and fast learning and classification steps, and good accuracy. See KSR, 550 U.S. at 417 (“[I]f a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill.”). Alternatively, combining Ferris and Han is nothing more than incorporating the known decision tree of Han, with decision system 302 of Ferris, to yield predictable results. See id. at 416 (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”). This combination of Ferris and Hall would result in applying the decision tree of Han to deployment rules 334 and resource importance data 335 of Ferris for deploying computing resources, and accordingly, teaches the limitation “training a decision model based on historical quantitative parameters and historical quantitative organizational parameters.” Thus, we agree with the Examiner (Final Act. 13) that modifying Ferris to include the decision tree induction of Han would have been obvious. Appeal 2020-003854 Application 14/080,661 13 Appellant argues the following: [H]ow exactly the decision tree described by Han is trained by the information provided in Ferris is not explained by the Examiner. Ferris does not teach or suggest use of a decision tree. Ferris does not teach or suggest use of neural network. So how exactly would one skilled in the art modify Ferris with the decision tree of Han is unknown. (Appeal Br. 23; see also Reply Br. 8–11.) Contrary to Appellant’s arguments, as discussed previously, the combination of Ferris and Hall is based on the improvement of a similar device in the same way as in the prior art. (Final Act. 13.) Thus, the Examiner has provided sufficient articulated reasoning with some rational underpinning for combining Ferris and Han to support a conclusion of obviousness. Appellant further argues that Han does not teach or suggest using historical quantitative parameters and historical quantitative organizational parameters or the equivalents of such parameters to train a decision tree. Ferris also does not teach or suggest using historical quantitative parameters and the historical quantitative organizational parameters to train a decision tree. (Appeal Br. 23–24 (emphasis omitted).) However, the Examiner cited to Ferris for teaching the limitations “quantitative parameters” (Final Act. 5–6) and “quantitative organizational (id. at 6–7) parameters,” and cited Han for teaching the limitation “training a decision model” (id. at 9). The rejection of claim 1 is based on the combination of Ferris and Han, and Appellant cannot show non-obviousness by attacking the references individually. See In re Keller, 642 F.2d 413, 426 (CCPA 1981). Thus, we agree with the Examiner that the combination of Ferris, Han, and Bazell would have rendered obvious independent claim 1, which includes the limitation “training a decision model based on historical Appeal 2020-003854 Application 14/080,661 14 quantitative parameters and historical quantitative organizational parameters.” Last, we are unpersuaded by Appellant’s arguments (Appeal Br. 26) that the combination of Ferris, Han, and Bazell would not have rendered obvious independent claim 1, which includes the limitation “executing the application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation.” The Examiner found that decision system 302 of Ferris, which can generate customized deployment architectures, corresponds to the limitation “executing the application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation.” (Final Act. 11.) We agree with the Examiner’s findings. Ferris explains that: decision system 302 . . . generate[s] customized deployment architectures and to account for the relative importance of cloud resources, such as an identifier of a running application, a start date and time, a requester identifier, and data parameters such as a configuration of a current deployment architecture (e.g., a number of machines), a time duration, a number of time intervals, a processor utilization, a network traffic level, a storage utilization, a software license information. (¶ 66.) Moreover, Ferris explains that decision system 302 “advise[s] the user of resource usage information, deployment architecture options” and “[d]eployment options can include computing resources, such as, for example, one or more physical machines, one or more virtual machines, one or more public clouds, one or more private clouds, or any combination thereof.” (¶ 70.) Because Ferris explains that decision system 302 generates customized deployment architectures options, for example, the combination of public and private clouds, Ferris teaches the limitation “executing the Appeal 2020-003854 Application 14/080,661 15 application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation.” Appellant argues the following: Ferris does not teach the extra step of executing the applications for which recommendations or deployment architecture have been generated. Ferris teaches generating the recommendation or deployment architectures only. . . . Paragraph [0092] describes “assigning the determined deployment architecture to the user's application(s)/process(es) such that they run on the determined deployment architecture.” (Appeal Br. 26.) However, the Examiner also cited to paragraphs 66 and 70 of Ferris for teaching the limitation “executing the application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation,” and Appellant has not provided any persuasive evidence and arguments as to why the Examiner’s findings are in error. Thus, we agree with the Examiner that the combination of Ferris, Han, and Bazell would have rendered obvious independent claim 1, which includes the limitation “executing the application in the private cloud or the hybrid private cloud and public cloud based at least in part on the recommendation.” Accordingly, we sustain the rejection of independent claim 1 under 35 U.S.C. § 103. Claims 2–5 depend from claim 1, and Appellant has not presented any additional substantive arguments with respect to these claims. Therefore, we sustain the rejection of claims 2–5 under 35 U.S.C. § 103 for the same reasons discussed with respect to independent claim 1. Independent claims 7 and 13 recite limitations similar to those discussed with respect to independent claim 1, and Appellant has not presented any additional substantive arguments with respect to these claims. Appeal 2020-003854 Application 14/080,661 16 We sustain the rejection of claims 7 and 13, as well as dependent claims 8– 11, 14–17, and 19–24 for the same reasons discussed with respect to claim 1. CONCLUSION We affirm the Examiner’s decision rejecting claims 1–24 under 35 U.S.C. § 103. Because we have affirmed at least one ground of rejection with respect to each claim on appeal, the Examiner’s decision is affirmed. See 37 C.F.R. § 41.50(a)(1). DECISION In summary: Claims Rejected 35 U.S.C. § References Affirmed Reversed 1–5, 7–11, 13–17, 19–24 103 Ferris, Han, Bazell 1–5, 7–11, 13–17, 19–24 6, 12, 18 103 Ferris, Han, Bazell, Edlund 6, 12, 18 Overall Outcome 1–24 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)(1)(iv). AFFIRMED Copy with citationCopy as parenthetical citation