Marshall & Swift/Boeckh, LLCDownload PDFPatent Trials and Appeals BoardOct 21, 20202020003689 (P.T.A.B. Oct. 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. 14/688,254 04/16/2015 Janakiraman Jagannathan CLOGI.054A 3991 101850 7590 10/21/2020 Knobbe, Martens, Olson & Bear, LLP CORELOGIC (CLOGI) 2040 Main Street, Fourteen Floor Irvine, CA 92614 EXAMINER BARTLEY, KENNETH ART UNIT PAPER NUMBER 3693 NOTIFICATION DATE DELIVERY MODE 10/21/2020 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): efiling@knobbe.com eofficeaction@appcoll.com jayna.cartee@knobbe.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte JANAKIRAMAN JAGANNATHAN Appeal 2020-003689 Application 14/688,254 Technology Center 3600 ____________ Before RICHARD M. LEBOVITZ, FRANCISCO C. PRATS, and JAMIE T. WISZ, Administrative Patent Judges. LEBOVITZ, Administrative Patent Judge. DECISION ON APPEAL The Examiner rejected claims 1–8, 10, 12–22, and 25–27 under 35 U.S.C. § 103 as obvious and under 35 U.S.C. § 101 as directed to patent ineligible subject matter. Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the Examiner’s decision to reject the claims. We have jurisdiction under 35 U.S.C. § 6(b). We AFFIRM. 1 We use the word “Appellant” to refer to “applicant” as defined in 37 C.F.R. § 1.42. Appellant identifies the real party in interest as is Marshall & Swift/Boeckh, LLC. Appeal Br. 1. Appeal 2020-003689 Application 14/688,254 2 STATEMENT OF THE CASE The Examiner rejected the claims in the Final Office Action as follows: 1. Claims 1–8, 10, 12, 15–22, 25, and 27 under 35 U.S.C. § 103 as obvious in view of Swanson et al. (US 2014/0019166 A1, published Jan. 16, 2014) (“Swanson”), Emison (US 2015/0235322A1, published Aug. 20, 2015) (“Emison”), Collins et al. (US 2009/0265193 A1, published Oct. 22, 2009) (“Collins”), and Jeong (US 2015/0043803 A1, published Feb. 12, 2015) (“Jeong”). Final Act. 28. 2. Claims 13, 14, and 26 under 35 U.S.C. § 103 as obvious in view of Swanson, Emison, Collins, Jeong, and Vande Pol (US 2003/0014342 A1, published Jan. 16, 2003). Final Act. 44. 3. Claims 1–8, 10, 12–22, and 25–27 under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Final Act. 24. Independent claim 1 is directed to a system programmed to carry out steps in a method. Independent claim 16 is directed to a computer- implemented method which comprises substantially the same steps recited in claim 1. We select claim 16 as representative. The claim is reproduced below, annotated with bracketed numbering for reference to the limitations in the claim: 16. A computer-implemented method comprising: [1] receiving identification information associated with a property, the identification information comprising a location of the property; [2] receiving a roofing characteristic associated with a roofing system associated with the property, the roofing system comprising a roof of a building on the property; Appeal 2020-003689 Application 14/688,254 3 [3] receiving a weather characteristic associated with the location of the property; [4] receiving an image of the roof of the building on the property; [5] analyzing the image using machine learning to determine a roof image characteristic that indicates whether the roof of the building includes any missing shingles; [6] applying a roof condition machine learning model to the roofing characteristic, the weather characteristic, and the roof image characteristic to determine a risk of roof damage; [7] determining a discrepancy between the determined roof image characteristic and the received roofing characteristic; and [8] output an indicator of the discrepancy between the determined roof image characteristic and the received roofing characteristic. Appeal Br. 28 (Claims Appendix). CLAIM 16 The first four steps of claim 16 involve receiving information about a property (step [1]), a roof characteristic of a roof on the property (step [2]), weather at the property location (step [3]), and an image of the roof (step [4]). In step [5] of the claim, machine learning is used “to determine a roof image characteristic that indicates whether the roof of the building includes any missing shingles.” Machine learning is employed to analyze the image, identify a roof image characteristic, and determine if a shingle is missing using the roof image characteristic. A “risk of roof damage” is determined in step [6] using “a roof condition machine learning model” that is applied to the roofing characteristic of step [2], the weather characteristic of step [3], and the roof image characteristic determined in step [5]. A discrepancy between the determined roof image characteristic (step [5]) and the received Appeal 2020-003689 Application 14/688,254 4 roofing characteristic (step [2]) is determined and then outputted in steps [7] and [8], respectively. The Specification lists various “roofing system data” about a roof that can be stored in a database and provided in step [2], such as roof age, roof dimensions, roof slope, roof aspect, roof pitch, roof direction, roof shape, roof type, roof covering material type, evidence of damage, etc. Spec. ¶¶ 36, 47. Whatever “roofing characteristic” is received in step [2], it also must be the same “roof image characteristic” of step [5] which “indicates whether the roof of the building includes any missing shingles” because the two are compared for a discrepancy in step [7]. OBVIOUSNESS REJECTIONS The Examiner found Swanson describes receiving the information described in steps [1]–[4] of claim 16. Final Act. 30–32. The Examiner further found that Swanson analyzed patterns in images to determine roof condition as in step [5] of the claim (Final Act. 33–34), but not using machine learning algorithms (Final Act. 36) to detect missing shingles (Final Act. 39). For machine learning, the Examiner cited Emison, which also describes determining the condition of a roof on a property (Final Act. 36– 37). For determining missing shingles as required by step [5] of the claim, the Examiner cited Collins (Final Act. 37–39). For step [6] of determining the “risk of roof damage” using the roofing characteristic, the weather characteristic, and the roof image characteristic, the Examiner cited Swanson and Emison. Final Act. 34–35, 36–37. The Examiner found that Swanson determined a discrepancy between characteristics as recited in step [7] of the claim, but not an output of the discrepancy. Final Act. 35. The Examiner found that Jeong describes outputting differences in images, and determined Appeal 2020-003689 Application 14/688,254 5 it would have been obvious to do so to show differences between a real and model image. Final Act. 39. Appellant contends that Swanson does not suggest determining the risk of roof damage. Appeal Br. 18. Appellant argues that Swanson focuses instead on assessing roof aging. Id. Appellant also argues that “[d]etermining property liability or assessing property value in the context of Swanson is not the same as determining a risk of roof damage, and Swanson does not teach or suggest such a connection.” Id. at 19. This argument does not persuade us that the Examiner erred. Swanson teaches that “[w]hen insurers are writing a [property insurance liability] policy on an individual property, the real value roof replacement cost is often not known and can be greatly misrepresented if the condition of the roof is poor, old, or otherwise degraded.” Swanson ¶ 2. In writing the policy to determine the cost of roof replacement Swanson further teaches that “knowledge of rooftop condition is important because natural hazards, such as wind, hail, and extreme heat, pose a much higher risk of causing significant damage to old or aged roof material versus younger or unweathered roof material that posses[s] more structural integrity to withstand damaging events.” Id. (emphasis added.) Thus, Swanson teaches that an insurance liability policy takes into account the roof condition and the risk of the occurrence of damage to it because of natural hazards, such as weather conditions (“wind, hail, and extreme heat”). Swanson further discloses: determining “the risk associated with selected features in the geographic area of interest is assessed by the insurance practitioner” (Swanson ¶ 26), obtaining data “for different kinds of liability, such as wind, flood, and fire phenomenology and risk Appeal 2020-003689 Application 14/688,254 6 assessment” (id. ¶ 32), and using “library of spectral images for vegetation, water quality, and other materials . . . to extract those aspects and document additional hazards or variables that impact property state and potential liability or intrinsic risk” (id. ¶ 39). Each of these passages describes determining “risk” based on various types of data collected about a particular location and the use of the data to determine “potential liability or intrinsic risk” which in the context of Swanson would be understood to mean risk of damage to a roof, for example, when there are high winds or neighboring vegetation that could pose risk of fire.2 In other words, the liability insurance determination is made based on knowledge of the risk that a roof might be damaged by weather conditions or nearby vegetation. Thus, the preponderance of the evidence supports the Examiner’s determination that Swanson teaches determining the risk of roof damage as recited in the rejected claims. Appellant addressed Swanson’s disclosure regarding roof damage and roof aging (e.g., Reply Br. 7–8), but did not consider that such determinations are a part of Swanson’s determination about the risk that the roof could be damaged in determining the liability policy. Appellant provides no alternative explanation as to why Swanson would be determining the potential liability and risk that a roof will be damaged. Appellant acknowledges in the Reply Brief that Swanson “provides a general statement that certain weather events pose a higher risk of causing 2 “While the above example has been given in terms of assessing rooftop conditions, the system is equally applicable to vegetation and other features of a landscape. For example, dry trees and brush very close to a home pose a greater fire risk than moist healthy trees located 150 ft from a home.” Swanson ¶ 34. Appeal 2020-003689 Application 14/688,254 7 significant damage to certain types of roofs,” but contends that Swanson “does not provide any disclosure as to how roof damage risk would specifically be determined.” Reply Br. 8. This argument is not persuasive because the Examiner specifically cited Emison for teaching machine learning to determine risk of damage as recited step [6] of claim 16. Final Act. 36–37. Appellant did not identify a defect in the Examiner’s findings regarding Emison’s disclosure. Appellant also argues that the cited publications do not teach or suggest step [7] of the claim of “determining a discrepancy between the determined roof image characteristic and the received roofing characteristic.” Appeal Br. 19–20. The claim requires “[5] analyzing the image using machine learning to determine a roof image characteristic that indicates whether the roof of the building includes any missing shingles.” The Examiner did not interpret the “roof image characteristic” to be the missing shingle, itself. As explained by the Examiner, “[d]etecting a non-shingle roof by spectra analysis would indicate there are no shingles on the roof and therefore they might be missing.” Final Act. 39. In other words, a spectral analysis of the roof could serve as a roof characteristic that is used to determine whether a shingle is missing. The Examiner found that Swanson’s disclosure of rectifying data “by a series of processes that correct for radiometric, atmospheric, and geospatial attributes tied to the aerial data” (Swanson ¶ 26) and performing corrections on radiometric data meet the limitation of step [7] of the claim because Appeal 2020-003689 Application 14/688,254 8 “[i]nherent with correction is determining a discrepancy between the received data and the final roof generated image.”3 Final Act. 35. The data described in paragraphs 26 and 27 of Swanson describes rectifying and correcting data (such as using “laboratory calibration data”), but, as explained below, there is not sufficient detail in these passages to convey step [7] of claim 16 of “determining a discrepancy between the determined roof image characteristic and the received roofing characteristic.” The passage in paragraph 26 states that “at step 14, the data is rectified by a series of processes that correct for radiometric, atmospheric, and geospatial attributes tied to the aerial data.” This statement does not explain that the rectification is done based on a discrepancy between the determined roof image characteristic and the received roofing characteristic. The Examiner’s conclusion that this step inherently detects a discrepancy between the two characteristics is insufficient because the Examiner did not identify where in Swanson’s disclosure the two recited characteristics are disclosed. In paragraph 27, a correction is performed “on the collected data using laboratory calibration data 44.” The Examiner did not explain how “laboratory calibration data” serves either as a determined roof image 3 “At step 42, radiometric correction is performed on the collected data using laboratory calibration data 44. This correction gives a scale to the pixel values, e.g. the monochromatic scale of 0 to 255 will be converted to actual radiance values. In step 46, the HSI data undergoes a process of geo- rectification using the tagged IMU geo coordinates and attitudinal data 48. This process aligns image data to a set of geographical coordinates so that each pixel is assigned a geographical coordinate.” Swanson ¶ 27. Appeal 2020-003689 Application 14/688,254 9 characteristic obtained from image analysis as in step [5] and the received roofing characteristic of step [4]. An examiner bears the initial burden of presenting a prima facie case of obviousness. In re Huai-Hung Kao, 639 F.3d 1057, 1066 (Fed. Cir. 2011). The Examiner did not meet the burden of establishing that step [7] of claim 16 is made obvious based on the disclosures of Swanson, Emison, Collins, and Jeong. All the claims require step [7]. The Examiner did not find that Vande Pol makes up for this deficiency. Consequently, claims 1–8, 10, 12, 15, 17–22, 25, and 27 are reversed for the same reasons. REJECTION BASED ON § 101 Principles of Law Under 35 U.S.C. § 101, an invention is patent-eligible if it claims a “new and useful process, machine, manufacture, or composition of matter.” However, not every discovery is eligible for patent protection. Diamond v. Diehr, 450 U.S. 175, 185 (1981). “Excluded from such patent protection are laws of nature, natural phenomena, and abstract ideas.” Id. The Supreme Court articulated a two-step analysis to determine whether a claim falls within an excluded category of invention. Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75–77 (2012). In the first step, it is determined “whether the claims at issue are directed to one of those patent-ineligible concepts.” Alice, 573 U.S. at 217. If it is determined that the claims are directed to an ineligible concept, then the second step of the two-part analysis is applied in which it is asked “[w]hat Appeal 2020-003689 Application 14/688,254 10 else is there in the claims before us?” Id. (alteration in original). The Court explained that this step involves a search for an “‘inventive concept’”—i.e., an element or combination of elements that is “sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 573 U.S. at 217–18 (alteration in original) (citing from Mayo, 566 U.S. at 75–77). Alice, relying on the analysis in Mayo of a claim directed to a law of nature, stated that in the second part of the analysis, “the elements of each claim both individually and ‘as an ordered combination’” must be considered “to determine whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 573 U.S. at 217. The PTO published revised guidance on the application of 35 U.S.C. § 101. USPTO’s January 7, 2019 Memorandum, 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Eligibility Guidance”). This guidance provides additional direction on how to implement the two-part analysis of Mayo and Alice. Step 2A, Prong One, of the Eligibility Guidance, looks at the specific limitations in the claim to determine whether the claim recites a judicial exception to patent eligibility. In Step 2A, Prong Two, the claims are examined to identify whether there are additional elements in the claims that integrate the exception in a practical application, namely, is there a “meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Eligibility Guidance, 84 Fed. Reg. 54 (Prong Two). Appeal 2020-003689 Application 14/688,254 11 If the claim recites a judicial exception that is not integrated into a practical application, then as in the Mayo/Alice framework, Step 2B of the Eligibility Guidance instructs us to determine whether there is a claimed “inventive concept” to ensure that the claims define an invention that is significantly more than the ineligible concept itself. Eligibility Guidance, 84 Fed. Reg. 56. With these guiding principles in mind, we proceed to determine whether the claimed subject matter in this appeal is eligible for patent protection under 35 U.S.C. § 101. Discussion Claim 16 is directed to a computer-implemented method. Methods are “processes,” which is a statutory category of patent-eligible subject matter listed in 35 U.S.C. § 101. Thus, because the claim falls into a statutory category of patent-eligible subject matter, following the first step of the Mayo analysis, we proceed to Step 2A, Prong One, of the Eligibility Guidance. Step 2A, Prong One In Step 2A, Prong One, of the Eligibility Guidance, the specific limitations in the claim are examined to determine whether the claim recites a judicial exception to patent eligibility, namely, whether the claim recites an abstract idea, law of nature, or natural phenomenon. The Eligibility Guidance directs examiners to search each limitation in the claim to determine whether it recites abstract an abstract idea. Eligibility Guidance, 84 Fed. Reg. 54. The Examiner found the steps in the claim “cover performance of the [claim] limitation[s] as mental processes” (Final Act. 25), which is one of Appeal 2020-003689 Application 14/688,254 12 the categories of patent-ineligible abstract ideas identified by the courts and set forth in the Eligibility Guidance. Eligibility Guidance, 84 Fed. Reg. 52. Steps [1]–[4] of the claim involve receiving information about a property (step [1]), a roof on the property (step [2]), weather at the property location (step [3]), and an image of the roof (step [4]). These steps are information gathering and not abstract. The last step [8] of the claim in which the discrepancy of [7] is output, is also not an abstract step, but rather is extra-solution activity. Eligibility Guidance, 84 Fed. Reg. 55. Steps [5] and [6] of the claim use machine learning to determine a roof image characteristic and to determine a risk of roof damage. The Examiner stated that machine learning is a mathematical algorithm and “computer processing an algorithm to learn (machine learning) is the same or similar to a person in their mind processing an algorithm to learn.” Final Act. 25. We do not agree that steps [5] and [6] recite abstract ideas. With respect to the Examiner’s finding that machine learning is a mathematical algorithm and therefore an abstract idea (Eligibility Guidance, 84 Fed. Reg. 52), the claim does not specifically recite the algorithm, but instead only states it generically. Thus, in accordance with the Eligibility Guidance, it is not an abstract idea. As explained in the PEG Update:4 A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. For example, a limitation that is merely based on or involves a mathematical concept described in the specification 4 Available at https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.p df (last accessed Jul. 3, 2020) (“PEG Update”). Appeal 2020-003689 Application 14/688,254 13 may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim. PEG Update 3 (footnotes omitted.) Example 39 of the Eligibility Examples discloses a claim for “training a neural network” and recites steps in which a neural network is trained. Subject Matter Eligibility Examples (Jan. 7, 2019) 8 (“SMEE”). A neural network is a type of machine learning algorithm. Id. The neural network is not identified in the example as either a mental process or a mathematical concept (another category of abstract ideas identified by the courts and the Eligibility Guidance as an abstract idea. Eligibility Guidance, 84 Fed. Reg. 52). Therefore, in accordance with the Eligibility Guidance, we conclude that steps [5] and [6] which recite “machine learning,” without reciting the specific mathematical algorithm or implementation of it, do not recite an abstract idea. The Examiner’s statement that machine learning is a mental process because it is the “same or similar” to what a person does in their mind to learn (Final Act. 25) is unreasonable because machine learning uses, for example, “a logistic regression model, a Generalized Linear Model, a Support Vector Machine, a Naive Bayes model, a Random Forest, or other statistical algorithm” (Spec. ¶ 7) and there is no evidence that a person uses these mathematical models when learning. See also Appeal Br. 8 (“Furthermore, humans do not analyze information in the same manner as a computing system running supervised or unsupervised pattern recognition because, as stated above, humans and computing systems do not intake, analyze, or learn from information in the same manner.”). Step [7] of the claim recites determining a discrepancy between the determined roof image characteristic (step [5]) and the received roofing Appeal 2020-003689 Application 14/688,254 14 characteristic (step [2]). The claim does not recite any limitation as to how the discrepancy is determined. For example, it could be done by simply observing that there is a difference between the received characteristic and the determined characteristic. An observation that could be performed in the human mind is a “mental process” and an ineligible abstract ideas (“Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).” Eligibility Guidance, 84 Fed. Reg. 52 (footnotes omitted)). Step [7] therefore recites an abstract idea. Accordingly, we proceed to Step 2A, Prong Two, of the Eligibility Guidance. Step 2A, Prong Two Prong Two of Step 2A under the 2019 Eligibility Guidance asks whether there are additional elements that integrate the exception into a practical application. Integration into a practical application is evaluated by identifying whether there are additional elements individually, and in combination, which go beyond the judicial exception. Eligibility Guidance, 84 Fed. Reg. at 54–55. As explained in the Eligibility Guidance, integration may be found when an additional element “reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” or “applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.” Id. at 55. Appellant argues that the claim is not directed to an abstract idea for the reasons described in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1307–08 (Fed. Cir. 2016), namely that the rejected claim is Appeal 2020-003689 Application 14/688,254 15 “directed to a specific set of rules for improving technology related to the processing of images for roof condition evaluation.” Appeal Br. 10. Appellant identifies step [1]–[6] of the claim as reciting a “rule-based process can be used to produce a more accurate determination of the condition of a roof than can be produced by existing systems that merely rely on aerial imagery.” Id. at 11. Steps [1]–[4] of claim 16 involve receiving various type of information. The claim does not recite a specific way in which the information is received by the system. We consider these steps to be data gathering steps. As explained in the Eligibility Guidance, “a mere data gathering such as a step of obtaining information about credit card transactions so that the information can be analyzed in order to detect whether the transactions were fraudulent” is insignificant extra-solution activity which is insufficient to integrate the judicial exception into a practical application. Eligibility Guidance, 8 Fed. Reg. 55 (fn. 31). Step [5] recites “analyzing the image using machine learning to determine a roof image characteristic that indicates whether the roof of the building includes any missing shingles.” In step [7] a discrepancy between the determined roof image characteristic of step [5] and the received roofing characteristic of step [2] is determined. Step [5] invokes “machine learning,” but it does not describe a specific way in which machine learning is applied to determine the “roof image characteristic.” The Specification identifies various machine learning algorithms by name, but with no specific information on how they are implemented to determine a roof image characteristic. Spec. ¶¶ 7, 26, 50. Thus, unlike McRO, there is no specific Appeal 2020-003689 Application 14/688,254 16 rule in the claim that integrates the judicial exception recited in step [7] into a practical application. In McRO, 837 F.3d at 1307–08, the claim recited a series of steps that “produce[d] lip synchronization and facial expression control of said animated characters.” The court found that the “claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: a sequence of synchronized, animated characters.” Id. at 1315. McRO found that the recited rules “are limiting in that they define morph weight sets as a function of the timing of phoneme sub-sequences.” Id. at 1313. The claims were found to be directed to a “technological improvement over the existing, manual 3-D animation techniques.” Id. at 1316. Appellant has not directed us to specific rules which are applied to implement the machine learning process recited in the step [5]. The claim recites that the image is analyzed by machine learning to determine a “roof image characteristic” which is compared to the received roof characteristic in step [7], but there is no disclosure in the claim, or Specification, of how the image analysis is implemented and what aspects of the image are subjected to the machine learning algorithm. Thus, steps [5] and [7] do not “embody a concrete solution to a problem” because they lack “the specificity required to transform a claim from one claiming only a result to one claiming a way of achieving it.” Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1343 (Fed. Cir. 2018) (citing SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1021–22 (Fed. Cir. 2018). Step [8], in which an indicator of the discrepancy is output, does not require a specific graphic interface or functionality to achieve the output. Appeal 2020-003689 Application 14/688,254 17 Thus, this step is distinguishable from cases in which a graphic interface was found to confer patent eligibility. For example, in Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018) the court found the claim “directed to an improved user interface for computing devices, not to the abstract idea of an index.” The representative claim in Core Wireless was directed to a computing device comprising a display screen “configured to display on the screen a menu listing one or more applications” and “to display on the screen an application summary that can be reached directly from the menu.” Core Wireless, 880 F.3d at 1359 (emphasis removed). The court found the claims patent-eligible because the claim “restrains the type of data that can be displayed in the summary window” and specified “a particular manner by which the summary window must be accessed.” Id. at 1362–1363. Thus, there was a recited functionality associated with the menu. Here, there is no detail or functionality recited in the claim associated with the output of an indicator of the discrepancy. A “risk of roof damage” is determined in step [6] using “a roof condition machine learning model” that is applied to the roofing characteristic of step [2], the weather characteristic of step [3], and the roof image characteristic determined in step [5]. This step, alone, as discussed above, does not recite a mental process. However, this step is also independent of step [7], and is not involved in the output of step [8], and therefore cannot serve to integrate the mental process recited in step [7] into a practical application. Moreover, there is no disclosure in the claim of the specific rules to implement the roof condition machine learning model. We also have not been guided to a specific description of it in the Specification. Appeal 2020-003689 Application 14/688,254 18 Citing steps [1]–[6] of the claim, Appellant argues that the claim recites “a specific manner of analyzing an image to determine a roof image characteristic that indicates whether the roof of the building includes any missing shingles and of determining a risk of roof damage.” Appeal Br. 13. Appellant also argues that the claim “as a whole is directed to a particular improvement in electronic analysis of images to detect roof characteristics, such as missing shingles.” Id. at 14. The improvement relied upon by Appellant, as explained above, is not accomplished by a specific rule or implementation. Rather, the claim, and the written descriptive support in the Specification, refer only to generic machine learning algorithms with no explanation as to how they are implemented to improve analysis of images and detection of characteristics as required by step [5] of the claim. Spec. ¶¶ 7, 26, 50. As explained in Dropbox, Inc. et al. v. Synchronism Technologies, Inc., 2020 WL 3400682 at *4 (Fed. Cir. 2020) (nonprecedential), it has been “consistently held that an ‘inventive concept’ exists when a claim ‘recite[s] a specific, discrete implementation of the abstract idea’ where the ‘particular arrangement of elements is a technical improvement over [the] prior art.’” [BASCOM Glob. Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016)]. The patent has to describe how to solve the problem in a manner that encompasses something more than the “principle in the abstract.” See [ChargePoint, Inc. v. SemaConnect, Inc., 920 F.3d 759, 769 (Fed. Circ. 2019)] (explaining that an invention may not be patent eligible if the “claims ‘were drafted in such a result-oriented way that they amounted to encompassing the ‘principle in the abstract’ no matter how implemented’” (quoting Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1343 (Fed. Cir. 2018))); see also Finjan, Inc. v. Blue Coat Sys., Appeal 2020-003689 Application 14/688,254 19 Inc., 879 F.3d 1299, 1305 (Fed. Cir. 2018) (“[A] result, even an innovative result, is not itself patentable.”). Dropbox at *4. While Appellant states that the claim is for determining missing shingles and determining risk of roof damage, the claim also recites steps [7] and [8] of determining a discrepancy between characteristics and then displaying the output. Thus, the claim can also be characterized as method of determining discrepancies between a characteristic of a received image and an image analyzed by conventional machine learning algorithms (step [5] of the claim). As discussed above, the additional element of machine learning and the lack of any rule by which the discrepancy is determined is not sufficient to integrate the mental process recited in claim 16 into a practical application. In sum, Appellant has not identified an additional element in the claim, beyond the abstract idea, that integrates the judicial exception into practical application. Step 2B Because we determined that the judicial exception is not integrated into a practical application, we proceed to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept beyond the judicial exception. In making this Step 2B determination, we must consider whether there are specific limitations or elements recited in the claim “that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Appeal 2020-003689 Application 14/688,254 20 Eligibility Guidance, 84 Fed. Reg. 56. We must also consider whether the combination of steps performs “in an unconventional way and therefore include[s] an ‘inventive concept,’ rendering the claim eligible at Step 2B.” Id. In this part of the analysis, we consider “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 573 U.S. at 217. Appellant states that the limitations recited in the claim “are directed to improving computing systems that evaluate roof conditions by using weather data and image processing based on machine learning.” Appeal Br. 15. Appellant states that the claimed features recited in the claim “can improve the accuracy of computing systems that evaluate roof conditions by applying weather data and the results of machine learning-based image analysis to a machine learning model.” Id. at 16. Appellant also argues that existing systems “do not evaluate roof conditions using weather data in combination with machine learning-based image analysis that identifies missing shingles based on roof images.” Id. at 17. The focus of our analysis is on step [7] because it recites a mental process, and therefore it must be determined whether the additional elements in the claim provide an inventive concept when considered in combination with step [7]. The additional element cited by Appellant is step [5] which applies machine learning in a conventional way to perform the image analysis. Spec. ¶¶ 7, 26, 50. Thus, this step alone does not provide an inventive concept. The step of receiving the roofing characteristic is recited in step [2]. The roofing characteristic is used in determining the discrepancy in step [7]. Step [2] does not limit how the roofing characteristic is received Appeal 2020-003689 Application 14/688,254 21 and thus could be accomplished by conventional processes. Appellant did not explain how the steps [2] and [5], using known and conventional techniques, when combined, provide an inventive concept. The claim also determines the risk of roof damage in step [6]. This step of the claim is not integrated with step [7], but it is separate step which relies on the roof image characteristic of step [5]. Step [6] does not add “significantly more” to the judicial exception of step [7] because it is independent and unrelated to how the discrepancy is determined. The rejection based on Section 101 of all the claims is affirmed. Claims not separately argued fall with claim 16. 37 C.F.R. § 41.37(c)(1)(iv). CONCLUSION In summary: Claims Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 1–8, 10, 12, 15–22, 25, 27 103 Swanson, Emison, Collins, Jeong 1–8, 10, 12, 15–22, 25, 27 13, 14, 26 103 Swanson, Emison, Collins, Jeong, Vande Pol 13, 14, 26 1–8, 10, 12– 22, 25–27 101 Subject matter eligibility 1–8, 10, 12– 22, 25–27 Overall Outcome 1–8, 10, 12– 22, 25–27 Appeal 2020-003689 Application 14/688,254 22 TIME PERIOD 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