DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice of Pre-AIA or AIA Status
The following is a Final Office Action for Application Serial Number: 18/436,811, filed on February 08, 2024. In response to Examiner’s Non-Final Rejection dated March 27, 2026, Applicant on June 29, 2026, amended claims 1, 2, 5, 7, 9, 10, 12-14 and 17-19, cancelled claims 3, 4, 6, 8, 15 and 16 and added new claims 21 and 22. Claims 1, 2, 5, 7, 9-14 and 17-22 are pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 112 rejections of claims 18 and 19 are hereby withdrawn pursuant to Applicant’s amendments to claims 18 and 19.
Response to Arguments
Applicants’ Arguments/Remarks filed June 29, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicants’ Remarks regarding the pending rejections will be addressed herein below in the order in which they appear in the response filed June 29, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states claim 1 is amended herein to require: 1) initial scheduling of a driver quantity during a driver shift window based on analysis of two flights prior to the driver shift window; 2) re-simulation of predictive staffing models during the assignment window (where the assignment window occurs within the driver shift window); and 3) assigning of drivers during the assignment window based on the re-simulated predictive staffing models and the actual quantity of available drivers during the assignment window. Applicant notes that the foregoing is merely a summary of the limitations of claim 1, and claim 1 in fact includes significant specific limitations (spanning over three pages) detailing the particular manner in which the foregoing summarized steps are performed, including the use of predicted flight parameters to generate predictive staffing models prior to the driver shift window and the use of current flight parameters for generation of predictive staffing models during the assignment window.
As amended, claim 1 cannot practically be performed in the human mind. Applicant notes that a method that cannot practically be performed in the human mind is distinct under MPEP § 2106.04(a)(2)(III)(A) from those that can be merely conceptualized or understood by the human mind. To this extent, MPEP § 2106.04(a)(2)(III)(A) distinguishes claims related to calculation of GPS position (i.e., those of SiRF Tech.), detection of suspicious network activity (i.e., those of SRI Int'l), data encryption according to a several-step manipulation of data (i.e., those of Synopsys), and claims to rendering half-tone images via pixel-by-pixel comparison (i.e., those of Research Corp. Techs.) from claims merely to, e.g., observations, evaluations, judgments, and opinions.
Claim 1 requires significant and multi-step transformations of particular kinds of data in order to 1) schedule drivers for shifts ahead of a driver shift window; 2) simulate predictive staffing models based on current flight parameters during an assignment window (required to be within the driver shift window of (1)); and 3) assign drivers to each of two flights based on two different predictive staffing models, a threshold missed bag quantity, and a quantity of drivers available to work during the assignment window. In particular, the claims require (see p. 19-20, Applicant Remarks)
To this extent, claim 1 recites the type of multi-step transformation of data that is similar to the claim at issue in Synopsis and that is specifically contemplated by the MPEP to be allowable under Step 2A, Prong One because it cannot be practically performed in the human mind (see MPEP § 2106.04(a)(2)(III)(A).
In response, Examiner respectfully disagrees. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner does not find Applicant’s arguments persuasive regarding the comparison between the encryption claim of Synopsys and the data analysis of staffing baggage drivers in Applicant’s pending claim. Instead, Examiner finds the pending claim is similar to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity, limitations directed to data analysis (i.e., a multi-step transformations of particular kinds of data in order to 1) schedule drivers for shifts ahead of a driver shift window; 2) simulate predictive staffing models based on current flight parameters during an assignment window (required to be within the driver shift window of (1)); and 3) assign drivers to each of two flights based on two different predictive staffing models, a threshold missed bag quantity, and a quantity of drivers available to work during the assignment window) without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Even in a computer environment, the pending limitations are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind.
Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner finds the present claims are directed to the data analysis simulating and identifying the assignment of baggage drivers to flights to reduce missed bag quantities. The recitation of a computer-implemented machine-learning model and electronic driver scheduling system does not take the claim out of the mental processes grouping. Examiner maintains the claim recites an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states claim 1, as amended, requires the repeated simulation of flight parameters for two flights and assignment of drivers based on the resultant predictive staffing models (including performance of all limitations encompassed by the foregoing summary) during the assignment window in order to assign drivers to work flights during that same assignment window. To this extent, claim 1 requires real-time or substantially real-time generation of assignments during the assignment window. The human mind cannot practically perform the number of complex of data transformations required by claim 1 to be performed during the assignment window in real-time or substantially in real-time. In this manner, claim 1 is similar to claim 3 of Example 47 of the July 2024 SME Examples.
In view of the foregoing, claim 1 satisfies Step 2A, Prong One with respect to the recitation of a mental process because claim 1 cannot, as amended, practically be performed in the human mind.
However, even if claim 1 did not satisfy Step 2A, Prong One, claim 1 also satisfies Step 2A, Prong Two of the Subject Matter Eligibility (SME) Test. Specifically, even if claim 1
recited a mental process, claim 1, when evaluated as a whole, provides enough detail to integrate any such mental process into a practical application. Claim 1 recites a significant number of specific details outlining how drivers are initially scheduled, how predictive staffing models are generated during the assignment window, and how those predictive staffing models are then used during the assignment window (i.e., in combination with a threshold missed bag quantity and a quantity of drivers available to work during the assignment window, where that quantity is also generated during the assignment window) to generate driver assignments.
The significant detail recited by the claim provides an ordered combination of steps that reflects a technical improvement discussed in the disclosure (e.g., at 11 [0013], [0016] and [0023]; see MPEP 2106.05(a)). Applicant notes that the MPEP specifically advises that analysis under this step "should be 'careful to avoid oversimplifying the claims' by looking at them generally and failing to account for the specific requirements of the claims" (MPEP § 2106.05(a). When the actual recitations of the limitations of claim 1 are considered as a whole, it is clear that those limitations reflect a technical improvement discussed in the disclosure. As such, the claims integrate any abstract idea described therein into a practical application and satisfy Step 2A, Prong Two.
In response, Examiner respectfully disagrees. As stated above, regardless of the complexity and/or granularity, limitations directed to data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Even in a computer environment, the pending limitations are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind.
Furthermore, Examiner finds the pending claims are similar to the ineligible subject matter disclosed in claim 2 of Example 47 and not similar to the improvements disclosed in claim 3. Specifically, the Step 2A- Prong Two and Step 2B analysis of claim 2 of Example 47 states, in part, all uses of the recited judicial exceptions require data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting; See MPEP 2106.05. The computer is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The use of a trained ANN in the claim merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims, thus the use of the trained ANN simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Unlike claim 3 of Example 47 that provides for improved network security using the information from the detection to enhance security by taking proactive measures to remediate danger by detecting, dropping and blocking the source address associated with potentially malicious packets. Examiner finds there are no similar technological improvements here. Examiner finds Applicant has failed to adequately point out what technological process in the claimed invention has improved. Using machine learning models to simulate and predict expected missed bag quantities, is considered an improvement to an existing business process and not an improvement to the functioning of a computer, other technological field, or computer-related technology. Merely confining the abstract idea to a particular technological environment does not establish a practical application. See Guidance, 84 Fed. Reg. at 54. “A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit.” In re Mohapatra, 842 F. App’x 635, 638 (Fed. Cir. 2021). Thus, Examiner maintains the machine learning recited in the claim is solely used a tool to perform the instructions of the abstract idea.
Examiner finds Applicant has not identified any limitations in the claimed invention that show or submit that the computer technology used is being improved or there was a problem in or with the computer technology that the claimed invention solves. Examiner further notes the advancements disclosed in Diamond v. Diehr, and SiRF Technology v. ITC recite improvements to a technology or technical field. Specifically, Diamond v. Diehr utilized the Arrhenius equation to improve the process of controlling the operations of a mold in curing rubber parts, and SiRF Technology v. ITC disclosed a GPS receiver utilizing software that applies a mathematical formula to improve the ability to determine its position in weak environments. In contrast, Examiner finds there are no similar improvements here. Examiner finds Applicant’s arguments are directed to improvements to an existing business process (e.g. staff management). Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed inventions system, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Examiner finds, Applicant is attempting to say the Step 2A-Prong One elements, the abstract idea, is what makes the claim eligible. Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Office Action also rejected claim 1 as allegedly reciting a certain method of organizing human activity. Specifically, the Office Action stated that claim 1 recites a method based on commercial interactions.
As amended, claim 1 does not recite a method of or related to commercial interactions. MPEP § 2106.04(a)(2)(II)(B) provides a number of examples of commercial interactions, all of which generally relate to contracts, legal obligations, advertising, marketing and sales activities or behaviors, and business relations.
In particular, provides the following as specific examples of "commercial or legal interactions" under § 101 (see p. 21-22, Applicant Remarks).
None of the foregoing 13 examples provided by MPEP § 2106.04(a)(2)(II)(B) is remotely analogous to the method of amended claim 1 or even to a high-level summary of amended claim 1 (e.g., as a system for automated baggage driver scheduling and, during an assignment window of a driver shift window, automated and real-time/substantially real-time assignment of drivers to work flights). Applicant also notes that claim 1, as amended, does not recite any other certain method of organizing human activity (i.e., either 1) a fundamental economic principle or practice, or 2) managing personal behavior or relationships or interactions between people), as those other certain methods of organizing human activity are used and defined by MPEP §§
2106.04(a)(2)(II)(A), 2106.04(a)(2)(II)(C). To this extent, claim 1 satisfies Step 2A, Prong One with respect to the recitation of a certain method of organizing human activity.
Further, even if claim 1 did recite a certain method of organizing human activity, the ordered combination of elements recited by claim 1, when evaluated as a whole, is sufficient to integrate any certain method of organizing human activity into a practical application for the same reasons as discussed previously with respect to the analysis of amended claim 1 as an alleged mental process. Claim 1, as amended, recites a specific improvement over prior art systems as outlined at, e.g., 11 [0013], [0016] and [0023]. As such, claim 1 is similar to claim 1 of example 42 of the 2019 SME Examples. Therefore, even if claim 1 does not satisfy Step 2A, Prong One, claim 1 independently satisfies Step 2A, Prong Two.
In response, Examiner respectfully disagrees. Examiner notes the important issue is whether the concept (e.g., the idea of assigning baggage drivers to flights at an airport to reduce missed bag quantities) is abstract (e.g., commercial interactions) - not whether the exact fact-pattern matches the particulars of previous court decisions. For example, in Planet Bingo, which dealt with the abstract idea of managing a game of bingo, the Federal circuit used Bilski and Alice to support the asserted abstract idea. Specifically the aspect of bingo game management which includes “solv[ing] a tampering problem and also minimiz[ing] other security risks” during bingo ticket purchases was determined to be similar to the abstract ideas of “risk hedging” during “consumer transactions,” (Bilski) and “mitigating settlement risk” in “financial transactions,” (Alice) that the Supreme Court found ineligible. Clearly, the fact patterns in Planet Bingo compared to Bilski and Alice were different, but the abstract concepts were similar. Additionally, to facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types; see MPEP 2106.04(a). Thus, Examiner finds Applicants aforementioned remarks are not persuasive and maintains the amended claims recite an abstract idea under Step 2A – Prong One for the reasons set forth in the office action.
As stated above, Examiner finds Applicant has failed to adequately point out what technological process in the claimed invention has improved. Applicants’ description of the improvement is directed to the abstract idea without reciting any improvement to how the machine learning functions, reflecting and/or submitting that the technology used is being improved or there was a technical problem with the technology that the claimed invention solves. Examiner maintains the additional elements recited in the claim function as intended with no improvement to the technology. Additionally, Applicant is respectfully reminded novelty and non-obviousness over the prior art, have no bearing on whether a claim recites an abstract idea. Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) ("The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101; see MPEP 2106.05(I).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1, 2, 5, 7, 9-12 and 21 are directed towards a method and claims 13, 14, 17-20 and 22 are directed towards a system, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1, 2, 5, 7, 9-14 and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite simulating and identifying the assignment of baggage drivers to flights to reduce missed bag quantities.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, prior to a driver shift window: receiving a first set of expected flight parameters descriptive of a first flight expected to occur within the driver shift window; simulating, by a simulator and the first set of expected flight parameters, expected missed bag quantities for a plurality of baggage driver quantities to create a first predictive staffing model for the first flight by, for each baggage quantity of baggage drivers of the plurality of baggage driver quantities, predicting an expected missed bag quantity using the first set of expected flight parameters, the first predictive staffing model correlates the plurality of baggage driver quantities to a first plurality of expected missed bag quantities based on the first set of expected flight parameters; and each baggage quantity of baggage drivers of the plurality of baggage driver quantities corresponds to one expected missed bag quantity of the first plurality of expected missed bag quantities; identifying a first recommended quantity of baggage drivers based on the first predictive staffing model and a threshold missed bag quantity, wherein the first recommended quantity of baggage drivers corresponds, according to the first predictive staffing model, to an expected missed bag quantity of the first plurality of expected missed bag quantities that is less than the threshold missed bag quantity; obtaining a second set of expected flight parameters descriptive of a second flight expected to occur within the driver shift window; simulating, by the simulator and the second set of expected flight parameters, expected missed bag quantities for the plurality of baggage driver quantities to create a second predictive staffing model for the second flight by, for each baggage quantity of baggage drivers the plurality of baggage driver quantities, predicting an expected missed bag quantity using the second set of expected flight parameters, wherein: the second predictive staffing model correlates the plurality of baggage driver quantities to a second plurality of expected missed bag quantities based on the second set of expected flight parameters; and each baggage quantity of baggage drivers of the plurality of baggage driver quantities corresponds to one expected missed bag quantity of the second plurality of expected missed bag quantities; identifying a second recommended quantity of baggage drivers based on the second predictive staffing model and the threshold missed bag quantity, wherein the second recommended quantity of baggage drivers corresponds, according to the second predictive staffing model, to an expected missed bag quantity of the second plurality of missed bag quantities that is less than the threshold missed bag quantity; and modifying electronic data representative of driver schedules to assign a quantity of scheduled drivers to the driver shift window that is at least a combined quantity of the first recommended quantity of baggage drivers and the second recommended quantity of baggage drivers; during a driver assignment window within the driver shift window: receiving a first set of current flight parameters descriptive of the first flight, the first set of current flight parameters describing actual flight parameters of the first flight during the driver assignment window; receiving a second set of current flight parameters descriptive of the second flight, the second set of current flight parameters describing actual flight parameters of the second flight during the driver shift window; determining a quantity of available drivers, wherein the quantity of available drivers represents a quantity of drivers, of the quantity of scheduled drivers, available to work during the driver assignment window; simulating, by the simulator and the first set of current flight parameters, expected missed bag quantities for the plurality of baggage driver quantities to create a third predictive staffing model for the first flight by, for each baggage quantity of baggage drivers the plurality of baggage driver quantities, predicting an expected missed bag quantity using the first set of current flight parameters, wherein: the third predictive staffing model correlates the plurality of baggage driver quantities to a third plurality of expected missed bag quantities based on the first set of current flight parameters; and each baggage quantity of baggage drivers of the plurality of baggage driver quantities corresponds to one expected missed bag quantity of the third plurality of expected missed bag quantities; simulating, by the simulator and the second set of current flight parameters, expected missed bag quantities for the plurality of baggage driver quantities to create a fourth predictive staffing model for the second flight by, for each baggage quantity of baggage drivers the plurality of baggage driver quantities, predicting an expected missed bag quantity using the second set of current flight parameters, wherein: the fourth predictive staffing model correlates the plurality of baggage driver quantities to a fourth plurality of expected missed bag quantities based on the second set of current expected flight parameters; and each baggage quantity of baggage drivers of the plurality of baggage driver quantities corresponds to one expected missed bag quantity of the fourth plurality of expected missed bag quantities; and modifying electronic data representative of driver assignments to assign drivers to the first flight and the second flight based on the third predictive staffing model, the fourth predictive staffing model, the threshold missed bag quantity, and the quantity of available drivers constitutes methods based on commercial interactions, as well as, constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a computer-implemented machine-learning model does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claim 13 recites certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites simulating and predicting expected missed bag quantities using a computer-implemented machine-learning model and wherein: the computer-implemented machine-learning model is configured to relate driver quantities and flight parameters to expected missed bag quantities. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the computer-implemented machine-learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination with the claim limitations, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Claim 13 recites receiving, from the flight database, sets of expected and current flight parameters and modifying electronic data stored by the electronic driver scheduling system, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, Claim 13 recites the system comprising: a flight database; an electronic driver scheduling system; a server electronically connected to the flight database and the electronic driver scheduling system, the server comprising: a processor; and a memory encoded with instructions executable by the processor at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 13 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including the electronic driver scheduling system, the system comprising: a flight database; an electronic driver scheduling system; a server electronically connected to the flight database and the electronic driver scheduling system, the server comprising: a processor; and a memory encoded with instructions executable by the processor amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0062]; [0064]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 7, 11, 19 and 20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 17 and 22 recite modifying electronic data stored by the electronic driver scheduling system limitations, which are considered insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 2, 9, 10, 14, 16, 18 and 19 recites additional computer-implemented machine-learning model general use and technique limitations, respectively. The general use of machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the computer-implemented machine-learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 2, 5, 10, 12, 14, 14, 17, 21 and 22 recite steps that further narrow the abstract idea. Therefore claims 2-12 and 14-22 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hale et al. (US 20050065834 A1) – Processing passengers for departure from an airport comprises a first line for passengers to be processed through a control point on the basis of first-in first-out. There is a second line for passengers to be processed on a non first-in first-out basis. The availability for the second line is determined by at least one of the following characteristics, namely the status of the passenger relative to an airline class of service; delays in flight times of one or more flights using the airport; cancellations of other flights using the airport; security factors at the airport; staffing at the airport; calendar date of flight, time of day of flight; number of passengers for the flight; numbers of passengers for other flights; and baggage handling. The system also processes passengers for arrival at an airport. The system also processes passengers for arrival at an airport so as facilitate baggage handling.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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