The Boeing CompanyDownload PDFPatent Trials and Appeals BoardApr 28, 20212020005835 (P.T.A.B. Apr. 28, 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. 15/690,909 08/30/2017 Nils KNEUPER 17-1110-US-NP 1040 116502 7590 04/28/2021 Perman & Green, LLP 99 Hawley Lane Stratford, CT 06614 EXAMINER LINHARDT, LAURA E ART UNIT PAPER NUMBER 3663 MAIL DATE DELIVERY MODE 04/28/2021 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 NILS KNEUPER and RALF RENÉ SHU-ZHONG CABOS ____________ Appeal 2020-005835 Application 15/690,909 Technology Center 3600 ____________ Before MICHAEL J. FITZPATRICK, WILLIAM A. CAPP, and LEE L. STEPINA, Administrative Patent Judges. CAPP, Administrative Patent Judge. DECISION ON APPEAL STATEMENT OF THE CASE Appellant1 seeks our review under 35 U.S.C. § 134(a) of the final rejection of claims 1 and 3–21 under 35 U.S.C. § 103 over Bailey (US 2011/0054718 A1, pub. Mar. 3, 2011), and of claim 4 over Bailey and a Wikipedia article on Machine Learning. We have jurisdiction under 35 U.S.C. § 6(b). 1 We use the word “Appellant” to refer to “applicant” as defined in 37 C.F.R. § 1.42(a). Appellant identifies The Boeing Company as the Applicant and real party in interest. Appeal Br. 1. Appeal 2020-005835 Application 15/690,909 2 We AFFIRM. THE INVENTION Appellant’s invention relates to computer software used to generate aircraft flight plans. Spec. ¶ 1. Claim 1, reproduced below, is illustrative of the subject matter on appeal. 1. An aircraft flight planning apparatus comprising: a database including a plurality of forecasting models configured to generate predictions of a predetermined characteristic on which at least a portion of an aircraft flight plan is based, where the predetermined characteristic includes at least a portion of a weather forecast, and at least one data matrix of test predictions for the predetermined characteristic generated by each of the plurality of forecasting models, each of the at least one data matrix of test prediction includes a plurality of test prediction data points; and an aircraft flight planning controller coupled to the database, the aircraft flight planning controller being configured to receive analysis forecast data having at least one analysis data point, select a forecasting model, from the plurality of forecasting models, based on a comparison between the at least one analysis data point and the plurality of test prediction data points of a respective forecasting model, and provide a prediction of the predetermined characteristic generated with the forecasting model, selected from the plurality of forecasting models, that corresponds to a test prediction data point that is representative of the at least one analysis data point. Appeal 2020-005835 Application 15/690,909 3 OPINION Eligible Subject Matter Before delving into the merits of the art rejection, we would be remiss if we failed to mention that Appellant’s claims appear to recite little more than using computer software for data collection, analysis, and display. Such is generally considered an abstract idea in the form of a mental process under our Guidelines for analysis under 35 U.S.C. § 101. See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019). It is now well settled that mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention. Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 223 (2014). Merely stating an abstract idea while adding the words “apply it” is not enough to confer eligibility. Id. Although the preamble of claim 1 recites an apparatus, the only structural element in the body of the claim is a “controller,” which is claimed in the most generic way possible. Claims App. We can also infer structure in the form of computer memory by recitation in claim 1 of a “database.” Id. The remainder of the claim language is highly conceptual in nature and is directed to functions that are performed by software. Id. However, in view of the strength of the Examiner’s art rejection, we decline to exercise our discretion to enter a new ground of rejection under Section 101. The Board enters a new ground of rejection at its discretion, and no inference should be drawn from a failure to exercise that discretion. Appeal 2020-005835 Application 15/690,909 4 See 37 C.F.R. § 41.50(b); see also MPEP § 1213.02. In the event that Appellant continues prosecution after resolution of this appeal, the Examiner may want to evaluate the eligibility of this application under Section 101. Unpatentability of Claims 1, 3, and 5–21 over Bailey Claim 1 The Examiner finds that Bailey discloses the invention substantially as claimed except for comparing an analysis data point to a plurality of test prediction data points of a forecasting model. Final Act. 3–4. The Examiner concludes that it would have been obvious to a person of ordinary skill in the art at the time of the invention to compare an analysis data point to test data points. Id. at 5. According to the Examiner, a person of ordinary skill in the art would have done this to predict how an aircraft’s trajectory would be affected by weather. Id. Appellant first argues that Bailey fails to disclose “forecasting models” as claimed. Appeal Br. 6. Appellant’s Specification defines a weather forecasting model as computer software that generates a weather forecast based on raw weather data and re-analysis data. Spec. ¶ 2. In the Answer, the Examiner finds, correctly, that Bailey’s “predicted weather” is equivalent to Appellant’s claimed “weather forecasts,” as both terms relate to estimates of possible future weather conditions. Ans. 4. Bailey is directed to a system that dynamically selects weather information for use in planning and updating flight trajectories. Bailey ¶ 3. Bailey identifies a flight trajectory for an aircraft and then identifies weather information for the flight trajectory. Id. ¶ 10. Such weather information may be formatted as “weather band” information. Id. In Bailey, “weather Appeal 2020-005835 Application 15/690,909 5 band” information is weather data that is specific to a geospatial point. Id. A geospatial point can be a particular latitude and longitudinal, at a particular altitude, and at a particular time. “Weather band” information may include temperature, pressure, humidity, turbulence, wind speed, and wind direction. Id. ¶ 7. Bailey’s weather band information may be based on current or predicted weather. Id. ¶¶ 8, 63. Bailey explicitly contemplates that an aircraft’s flight trajectory may be affected when actual weather differs from the predicted weather that is used to calculate a flight trajectory. Id. ¶ 8. Bailey continually evaluates incoming and updated weather information to dynamically select weather for a flight plan. Id. ¶¶ 61–62. Bailey generates aircraft predictions 418, which includes information associated with a number of points in time based on predicted weather. Id. ¶ 68. Bailey’s weather band processor 402 gathers weather information from ground and aircraft sources and passes such information through data filtering 422 and then on to selection module 426. Id. ¶ 69. Selection module 426 processes filtered information 424 and applies selection criteria 427 to an aircraft trajectory. Id. ¶ 71. Selection module 426 predicts how the aircraft trajectory may change from its flight plan based on weather information 414 received in filtered information 424. Id. A person of ordinary skill in the art, having read Bailey and Appellant’s disclosure, would immediately recognize that both inventions merely attempt to use modern computer and telecommunications technology to automate a flight planning process that has been performed manually for decades prior to the date of invention.2 An aircraft operator will select a route of flight from point A to point B and then calculate how long it will 2 See EB6 aviation slide rule article at https://en.wikipedia.org/wiki/E6B. Appeal 2020-005835 Application 15/690,909 6 take to fly such route at airspeeds and altitudes that will optimize fuel consumption and other economic considerations relevant to a flight, such as cost per passenger mile per unit of time. A person of ordinary skill in the art also understands that weather considerations will affect the flight. If there is a head wind or a tail wind, such will affect fuel consumption and its attendant costs. Such will also affect the aircraft’s arrival time, which may affect ground facilities at the destination, such as terminal gate availability. A person of ordinary skill in the art also understands that there is an interaction between an aircraft’s performance, its flight trajectory, and weather. For example, when faced with a strong head wind at cruise altitude, an aircraft might descend to a lower altitude to encounter less head wind, but may then experience an increased rate of fuel consumption at the lower altitude. A person of ordinary skill in the art also understands that a flight plan initially begins purely as a forecast or prediction, but that, after takeoff, the flight plan is updated during the flight to account for, among other things, differences between what was estimated or predicted in the original flight plan and what the aircraft actually experiences during the course of a flight. Bailey, among other things, relies on forecast weather information and such information would have been produced using a “forecasting model” within the meaning of claim 1. The invention of Bailey is almost identical to what is described and claimed in Appellant’s disclosure. Owing to the highly conceptual nature of Appellant’s claim language, coupled with the variability of expression made possible by the flexibility of the English language, we view Bailey and Appellant’s invention as little more than two different ways of describing the Appeal 2020-005835 Application 15/690,909 7 same thing. We view Appellant’s arguments for patentability, for the most part, as an effort to mischaracterize and distort the meaning of words in order to seemingly create differences between the prior art and the claimed invention where no differences, in fact, exist. With the foregoing in mind, we find Appellant’s “forecasting model” argument unpersuasive. A weather forecast that forecasts a particular wind direction and velocity, at a particular altitude, and a particular time amounts to a “prediction of [a] predetermined characteristic” within the meaning of claim 1. See Spec. ¶ 33 (explaining that a “predetermined characteristic” may be wind speed or any other suitable meteorological variable). The Examiner does not err in finding that the forecasting model limitation of claim 1 is met by Bailey. Appellant next argues that there is nothing in Bailey that discloses or suggests any part of the system of Bailey that generates weather forecasts or “predicted weather.” Appeal Br. 6. We disagree. Figure 3 of Bailey is an illustration of a weather selection environment. Bailey ¶¶ 17, 55, Fig. 3. A person of ordinary skill in the art would understand that weather forecasts are generated from weather information 318 in weather selection system 306 of Bailey. Id. Claim 1 merely recites an apparatus that has: (1) a database; and (2) a flight planning controller. Claims App. As correctly found by the Examiner, there is no recital in claim 1 that limits where the various physical components of the apparatus are located. Ans. 5. The Examiner does not err in finding that Bailey’s system includes the generation of weather forecasts. Appellant next argues that Bailey fails to disclose a data matrix of test predictions generated by the forecasting models as claimed. Appeal Br. 7. We understand a “test prediction” to be little more than an item of data from Appeal 2020-005835 Application 15/690,909 8 a weather forecast, which is substantially identical to Bailey’s “weather band.” Bailey ¶ 7. Appellant construes the claim language “at least one data matrix of test predictions” as a plurality of forecast data points. Id. In response, the Examiner finds that Bailey teaches a number of databases. Ans. 6 (citing Bailey ¶ 55). The Examiner interprets “matrix” as a mathematical term for two or more dimensional information. Id. The Examiner finds that current and predicted weather band information for various waypoints constitutes a multi-dimensional data set that reads on the definition of a matrix. Id. Therefore, according to the Examiner, “the weather band’s predicted weather for waypoints along a flight path includes at least one data matrix of test predictions.” Id. Bailey’s processor 314 accesses a “number of databases 316.” Bailey ¶ 55. The processor receives “weather information” using a number of databases which information is obtained from a number of operation centers 302 and a number of aircraft 304. Id. Weather information 317 is also received by weather selection system 306 from a number of other ground systems 301, such as weather reporting stations and weather monitoring stations. Id. ¶¶ 51, 55, Fig. 3. Bailey features weather selection system 306. Id. ¶ 53. Weather selection system 306 interacts with a number of operations centers and a number of aircraft 304. Id. Bailey’s weather band selection system continually evaluates information received in order to dynamically select weather for a flight plan. Id. ¶¶ 60–61. Given the foregoing context, a person of ordinary skill in the art would understand that Bailey receives computer generated weather forecast information from a plurality of sources. Thus, a person of ordinary skill in the art would understand that, in selecting information for a weather “band,” Bailey’s Appeal 2020-005835 Application 15/690,909 9 system selects from among data that is received from a plurality of sources. Data from this plurality of sources is stored in databases 316. Id. ¶ 55. A person of ordinary skill in the art would understand that weather forecast data stored in databases 316 constitutes a data matrix within the meaning of claim 1. The Examiner does not err in finding that Bailey discloses a data matrix of test predictions as claimed. Next, Appellant argues that Bailey fails to disclose a comparison between an analysis data point and a test prediction data point. Appeal Br. 8. According to Appellant: [T]here is nothing in Bailey that in any way, shape, or form that [sic] discloses or suggests “analysis data points” and “test prediction data points” and to assert otherwise is to blatantly create disclosure in Bailey where no such disclosure exists. Id. at 9. In response, the Examiner states that Bailey’s processor, which makes weather band selections, reads on Appellant’s controller. Ans. 7. According to the Examiner, Bailey’s weather band selection process accesses the most recently acquired weather obtained by the aircraft to determine the weather information that is pertinent to the aircraft. Id. The Examiner finds such recently acquired weather information to satisfy the “analysis data point” limitation of claim 1. Id. (citing Bailey ¶ 80). Current weather data obtained by the aircraft is used to rank the weather bands and the selected weather band then anticipates the weather for the upcoming flight path. Id. (citing Bailey ¶¶ 73, 80). The Examiner finds that the selected weather band is selected based on: (1) the most recently acquired weather measured by aircraft — analysis data points; and (2) the predicted weather for the waypoints — test prediction data points. Id. Appeal 2020-005835 Application 15/690,909 10 In reply, Appellant argues that Bailey merely filters data without performing a comparison of one item of data with another. Reply Br. 4. Appellant goes so far as to argue that Bailey does not use the word “compare” or any of its derivatives. Id. Appellant’s argument based on the literal word “compare” and its derivatives is unpersuasive. It is well settled that a prior art reference need not satisfy an ipsissimis verbis test. In re Gleave, 560 F.3d 1331, 1334 (Fed. Cir. 2009). Bailey uses the word “select” or a derivative thereof more than 100 times. See generally Bailey. Reading how the word “select” is used in context throughout Bailey, it is understood that a selection is made based on comparing an item of data to a criterion or another item of data. According to Appellant’s Specification, “analysis forecast data” is obtained from data sources such as airline operation control centers, aircraft onboard sensors, or any other suitable weather data center. Spec. ¶ 35. In claim 1, analysis forecast data is compared with test prediction data points obtained from forecasting models. Claims App. Appellant’s argument that Bailey merely filters data cannot be sustained from the record before us. Reply Br. 4. In Bailey, a weather band for waypoint 512 might include a head wind component of X knots. Bailey ¶ 79. When Bailey’s aircraft 506 reaches waypoint 512, two things happen: (1) current weather conditions are obtained for waypoint 5123; and (2) the remainder of the flight plan that is down range of waypoint 512 is updated based on forecast head wind data along the route. Id. ¶ 80. Forecast weather band data for the remainder of the flight plan that is down range of waypoint 512 is obtained from sources 3 This information could be obtained from aircraft’s onboard navigation/ sensor system which can measure, among other things, current head wind component, or it could be obtained from sources external to the aircraft. Appeal 2020-005835 Application 15/690,909 11 such as other aircraft along the same route or weather forecasting stations. Id. When aircraft 506 follows trajectory 502 along flight path 500 at a later time than aircraft 504, aircraft 506 may receive the benefit of the weather information detected by aircraft 504 as well as the current weather detected by aircraft 506. The current weather detected by aircraft 506 may also be used to update the dated weather in the onboard computer of aircraft 506. The dated weather may be . . . weather information uploaded preflight into the onboard computer of aircraft 506 . . . The information obtained by aircraft 504 along trajectory 502 may be used to anticipate the weather aircraft 506 will encounter on points 508, 510, 512, and 514 of trajectory 502 for flight path 500. Id. Bailey’s system evaluates weather information from a plurality of sources, processes such information through section module 426, and updates the flight plan with weather band data that is selected by the selection module. Id. ¶¶ 69–71. “Selection module 426 processes filtered information 424 and applies selection criteria 427 to an aircraft trajectory received.” Id. ¶ 71. Thus, Bailey sends filtered data to selection module 426 where it is “processed” using selection criteria and then applied to an aircraft trajectory. Bailey ¶ 71. Appellant’s argument that Bailey’s selection process is not equivalent to Appellant’s comparison process as found by the Examiner is not persuasive. Appeal Br. 10. Appellant fails to explain how Bailey receives weather forecast information from a plurality of sources and then processes such information using selection criteria without engaging in a comparison process. Bailey ¶ 71, Fig. 4. The Examiner does not err in finding that Bailey compares analysis data points to test prediction data points. Final Act. 4. Appeal 2020-005835 Application 15/690,909 12 The Examiner’s findings of fact are supported by a preponderance of the evidence, and the Examiner’s legal conclusion of unpatentability is well- founded. In view thereof, we sustain the Examiner’s unpatentability rejection of claim 1. Claim 8 Claim 8 is an independent claim that is substantially similar to claim 1, except that it recites a method instead of an apparatus. Claims App. Appellant argues that Bailey fails to disclose selecting a forecasting model based on comparing an analysis data point and a data matrix of test predictions. Appeal Br. 11–12. As discussed above, we fully considered this argument with respect to the rejection of claim 1 and found it to be unpersuasive. It is equally unpersuasive here. The same is true with respect to the remainder of the arguments Appellant advances against the rejection of claim 8. Appeal Br. 12–16. We sustain the Examiner’s unpatentability rejection of claim 8. Claim 15 Claim 15 is an independent claim that is substantially similar to claim 1, except that it recites a method instead of an apparatus. Claims App. In traversing the rejection, Appellant presents only arguments that we previously considered with respect to claim 1. Appeal Br. 16–19. Such arguments are no more persuasive with respect to claim 15 than they were with respect to the rejection of claim 1. We sustain the Examiner’s rejection of claim 15. Claims 5 and 14 Claim 5 depends from claim 1 and adds the limitation: “wherein each of the plurality of forecasting models is one or more of trained and tested Appeal 2020-005835 Application 15/690,909 13 using training data common to all of the plurality of forecasting models.” Claims App. Claim 14 depends directly from claim 13 and indirectly from claim 8 and adds the limitation: “wherein the test prediction data point from the data matrix of test predictions that is representative of the at least one analysis data point is determined by creating a distance matrix to identify which of the plurality of test prediction data points has a smallest distance to the at least one analysis data point.” Id. On page 19 of the Appeal Brief, in a paragraph numbered as “1D,” Appellant states: “Claims 3-7, 9-14, and 16-21 depend from claims 1, 8, and 15 and are patentable at least by reason of their respective dependencies.” Appeal Br. 19. Later, on page 21, in a paragraph numbered only as “1D-2,” Appellant argues with respect to claim 5 that Bailey merely filters data and that data filtering differs from training and testing of a forecasting model. Appeal Br. 21. Similarly, in paragraph 1D-3 on page 21, Appellant argues with respect to claim 14 that Bailey fails to disclose trajectory predictions that read on the smallest distance. Id. The Examiner’s Answer does not address Appellant’s arguments concerning claims 5 and 14 on page 21 of the Appeal Brief. Ans. 11. Based on the following remarks in the Answer, it appears to us that the Examiner understood Appellant’s remarks on page 19 of the Appeal Brief as Appellant’s entire position on claims 5 and 14. Id. Appellant argues that claims 3-7, 9-14, and 16-21 depend from claims 1, 8, and 15 and are patentable at least by reason of their respective dependences (see argument 1D). The examiner disagrees, and because there was no issue raised with the dependent claims, these claims 1, 8, and 15 rise and fall with the independent claims. Appeal 2020-005835 Application 15/690,909 14 Id. Thus, it appears that the Examiner mistakenly overlooked Appellant’s arguments under paragraphs 1D-2 and 1D-3 on page 21 of the Appeal Brief. The instant situation presents us with a procedural dilemma. Our rules require that any claims that an Appellant intends to argue separately or as a subgroup “shall” be argued under a separate subheading. See 37 C.F.R. § 41.37(c)(1)(iv). The word “shall” denotes a mandatory requirement. Moreover, such rules further require that the separate subheading identify the claim(s) “by number.” Id. Appellant’s numbering of paragraphs in the Appeal Brief, clearly fails to comply with the subheading requirements of Rule 41.37. On the one hand, we normally would prefer to dispose of arguments on their substantive merits. However, on the other hand, it appears that Appellant’s failure to abide by our rules and provide an Appeal Brief with appropriate subheadings may have misled the Examiner which, in turn, may have deprived us of the opportunity to hear and consider the Examiner’s response to Appellant’s arguments. Procedural rules, such as Rule 41.37, serve an important purpose and are not to be ignored or neglected at the whim or carelessness of the Appellant. In this particular situation, had Appellant filed an Appeal Brief with the proper subheadings, we likely would have the benefit of the competing positions of Appellant and the Examiner on this matter. We determine that, if any hardship should befall either Appellant or the Examiner due to Appellant’s failure to abide by our rules in this particular instance, it should befall Appellant. Under the circumstances, due to Appellant’s failure to provide proper subheadings under our rules, we will treat claims 5 and 14 as having been grouped with claims 1 and 8 respectively and, thus, they fall therewith. See Subheading 1, Appeal Br. 5. Appeal 2020-005835 Application 15/690,909 15 Claims 3, 6, 7, 9–13, and 16–21 These claims depend from one of claims 1, 8, and 15 and are not separately argued. They fall with their respective independent claims. See 37 C.F.R. § 41.37(c)(1)(iv) (failure to separately argue claims constitutes a waiver of arguments for separate patentability). Unpatentability of Claim 4 Claim 4 depends from claim 1 and adds the limitation: “wherein each of the plurality of forecasting models are machine learning models.” Claims App. We deem it expedient to consider the Examiner’s alternative grounds of rejection of claim 4 together. The Examiner finds that, in Bailey, a data set and weather band/forecast is selected and Bailey’s system and then predicts how the aircraft trajectory may change from its flight plan based on weather information. Final Act. 5 (citing Bailey ¶ 71). The Examiner finds that such integration of weather data to predict how the trajectory of the aircraft may change based on a weather model amounts to a teaching that is equivalent to “machine learning.” Id. at 6, 15. The data filtering process [of Bailey] is capable of being a complex algorithm that is a combination of data mining and computational statistics to filter the data sets and identify the accuracy and relevance of the information in the database for that particular aircraft request which is the description of machine learning. The prior art uses all relevant information to predict how the trajectory may change, which is a description of a machine learning data-driven predication. Id. at 15 (citing Machine Learning, p. 1). The Examiner concludes that it would have been obvious to a person of ordinary skill in the art at the time of the invention to have applied Appeal 2020-005835 Application 15/690,909 16 machine learning techniques to the data filtering and selection features of Bailey. Final Act. 14–15. According to the Examiner, a person of ordinary skill in the art would have done this to better predict how the aircraft’s trajectory may change due to weather along the route. Id. at 15. Appellant argues that Bailey cannot be modified as proposed by the Examiner because, to do so, would change the manner in which Bailey operates. Appeal Br. 22 (“the databases and the weather band selection as they are explicitly described in Bailey would be wholly eliminated”). In response, the Examiner states that a data driven decisions process, such as Bailey, compares analysis data points measured by the aircraft to various weather band test prediction data points. Ans. 12. The Examiner finds that such comparison results in rank ordering for selection of an appropriate weather band. Id. The Examiner finds that such is a machine learning process without labeling the system as such in so many words. Id. In reply, Appellant argues that Bailey’s disclosure of a data decision approach “says nothing” about machine learning models. Reply Br. 5. Appellant defines “machine learning” as “the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.” Appeal Br. 20. According to Appellant, “[m]achine learning models (or algorithms) build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.” Id. at 20–21. Appellant’s Specification concedes that weather forecasting computers that use weather forecasting models are a form of machine learning. Spec. ¶ 2. The Specification teaches that decision trees, random Appeal 2020-005835 Application 15/690,909 17 forecast algorithms, polynomial fit, and k-nearest neighbors are “suitable machine learning models.” Id. ¶ 30. To the extent that Bailey does not explicitly teach the use of a “machine learning” model in its data filtering and selection modules, the question is raised as to whether it would have been obvious to incorporate machine learning techniques into Bailey’s system. Bailey already employs telecommunications and computer processing technology to gather, filter, analyze, select, and output flight planning data. Bailey, Figs. 1–5. Bailey applies data processing techniques to aircraft performance data, in combination with actual and predicted weather data, to predict an aircraft’s trajectory. At the end of the day, “machine learning” is little more than just another, known, data processing technique. The prospect that Bailey may not explicitly disclose the use of machine learning techniques is not fatal to the Examiner’s case. It is well settled that a claimed invention may be obvious even when the prior art does not teach each claim limitation, so long as the record contains some reason that would cause one of skill in the art to modify the prior art to obtain the claimed invention. Beckson Marine, Inc. v. NFM, Inc., 292 F.3d 718, 727 (Fed. Cir. 2002). In an obviousness analysis, “we do not ignore the modifications that one skilled in the art would make to a device borrowed from the prior art.” In re ICON Health & Fitness, Inc., 496 F.3d 1374, 1382 (Fed. Cir. 2007). This follows from the prospect that “[a] person of ordinary skill is also a person of ordinary creativity, not an automaton.” KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 421 (2007). It is well settled that, if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would Appeal 2020-005835 Application 15/690,909 18 improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill. KSR, 550 U.S. at 417. In the instant case, Appellant presents neither evidence nor persuasive technical reasoning that tends to show that modifying Bailey’s data processing features to incorporate machine learning techniques requires more than ordinary skill or somehow produces unexpected results. The crux of Appellant’s traverse of the Examiner’s rejection is contained in the following statement: Bailey cannot be modified to replace the databases and selection of the weather bands as explicitly described in Bailey with machine learning models as disclosed in Machine Learning because doing so would clearly change the manner in which Bailey operates (i.e., the databases and the weather band selection as they are explicitly described in Bailey would be wholly eliminated). Appeal Br. 22. This statement is vague and conclusory at best. It lacks any evidentiary support or persuasive technical reasoning as to how Bailey’s operation would be altered by machine learning or how or why Bailey’s databases would be “wholly eliminated.” It amounts to unsubstantiated attorney argument that is entitled to little weight. See In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997) (explaining that attorney arguments and conclusory statements that are unsupported by factual evidence are entitled to little probative value). Here, the Examiner has supplied us with sufficient findings of fact and reasons to modify the prior art to make out a prima facie case of obviousness. See In re Berg, 320 F.3d 1310, 1315 (Fed. Cir. 2003) (discussing an Examiner’s competence to make findings that can establish a prima facie case of obviousness). When a prima facie case of obviousness is Appeal 2020-005835 Application 15/690,909 19 made, the burden shifts to Appellant to come forward with evidence and/or argument supporting patentability. In re Giannelli, 739 F.3d 1375, 1379 (Fed. Cir. 2014). Appellant’s unsupported and conclusory arguments are not sufficient to overcome the Examiner’s prima facie case. See In re De Blauwe, 736 F.2d 699, 705 (Fed. Cir. 1984) (explaining that arguments relying on factually unsupported assumptions to rebut a prima facie case of obviousness “can hardly be considered factual evidence”). We sustain each of the Examiner’s alternative grounds of rejection of claim 4. CONCLUSION Claims Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 1, 3-21 103 Bailey 1, 3-21 4 103 Bailey, Machine Learning 4 Overall Outcome 1, 3-21 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