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 .
Status of the Claims
Claims 1-21 were previously pending. Claims 1 and 21 were amended in the reply filed January 12, 2026. Claims 1-21 are currently pending.
Response to Arguments
Applicant's arguments filed with respect to the rejection made under § 101 have been fully considered but they are not persuasive. Applicant argues that the claims do not recite an abstract idea. "The pending claims do not recite any contract formation, performance guarantees, advertising exchanges, pricing, marketing, sales, or business relationships. Rather, they recite specific computer-implemented data processing operations-fine-tuned machine learning modules that dynamically generate and apply weights to nodes and edges of a graph network based on processing natural language prompt inputs and historical traveler data." Remarks, 9-10. This does not address the finding in the rejection that the steps of interacting with a traveler and determining an alternative itinerary to address a travel interruption are commercial/business interactions. The machine learning model has been treated as an additional element. Its presence in the claim does not mean that an abstract idea is not also recited at this step in the analysis. Although Applicant also argues that "at least the step of 'generat[ing] weights for nodes and edges of a graph network based on prompt inputs,' as recited by claim 1, cannot practically be performed in the human mind," (Remarks, 10), no reasons are provided. Moreover, this limitation also fits into the categories of certain methods of organizing human activities and mathematical concepts.
Applicant also argues that the claims integrate the abstract idea into a practical application. "To solve this technical problem related to personalization of rebooking options, the present application sets forth an improvement related to fine-tuning a graph network based on traveler-specific or traveler-category data." Remarks, 11. This describes a problem arising out of certain methods of organizing human activities—not one particular to computers or any other technology. "In sum, claim 1 improves graph network technology, and thus, improves the functioning of a computer or other technology by dynamically determining and applying weights to both nodes and edges of a graph network based on fine-tuned machine learning processing of natural language prompt inputs and historical traveler data." Remarks, 12. Rather than describing improvements to machine learning itself, this describes using machine learning as a tool to more efficiently mathematically evaluate travel planning data for people. Accordingly, the rejection is maintained.
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.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea without significantly more). Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208 (2014). Claims 1-21, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The claims recite an abstract idea reflected in the representative functions of the independent claims—including:
in response to receiving an indication of an interruption of a travel itinerary of a traveler, obtaining travel identification data for the traveler and itinerary data corresponding to the traveler;
in response to an access request from the traveler, provide interaction prompt responses to the traveler regarding the interruption of the travel itinerary and to receive prompt inputs from the traveler responsive to the interaction interface;
in response to the prompt inputs from the traveler, (i) processing the prompt inputs to generate structured data representing traveler preferences, (ii) converting the structured data into ranking data, (iii) generating a graph network of available route alternatives including nodes representing alternative travel destinations and edges representing relationships between destinations, (iv) applying weights to the nodes and edges based on the ranking data and historical traveler data to incorporate the traveler preferences when generating and ranking alternative travel itineraries, and (v) accessing routing data corresponding to one or more alternative travel itineraries based on the weighted graph network; and
causing information on the one or more alternative travel itineraries to be displayed to the traveler as prompt responses, including generating and displaying a visual indication of the information on the one or more alternative travel itineraries.
These limitations taken together qualify as a certain method of organizing human activities because they recite collecting, analyzing, and outputting information for the travel behaviors of persons and restructuring the related transactional/commercial relationships with travel service providers (i.e., in the terminology of the 2019 Revised Guidance, commercial interactions (including business relations)). Additionally, the claims recite certain mental processes (e.g., a travel agent observing a travel interruption, evaluating responses from the traveler, and arriving at a judgment on an alternative travel itinerary). Finally, the claims also recite evaluating relationships among travel data mathematically by way of a graph with nodes, edges, and weights. See Ben-Yitschak, et al., U.S. Pat. Pub. No. 2010/0305984 (Reference A of the attached PTO-892) (¶ 0309—"the term 'graph' refers to mathematical structures used to model pair-wise relationships between objects from a certain collection. A graph is a collection of vertices or nodes and a collection of edges that connect the pair of vertices. (See Biggs, N.; Lloyd, E. And Wilson, R. Graph Theory, 1736-1936 (1986))"). See also Avidat, et al., U.S. Pat. Pub. No. 2017/0310595 (Reference B of the attached PTO-892) (¶ 0002—"Many optimization problems can be modeled as weighted directed graphs. Graphs are visual and mathematical representations of vertices or nodes which are connected together by edges.").
The invention shares similarities with other abstract ideas held to be non-statutory by the courts (see Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025)—using machine learning to generate network maps for live events, similar because at another level of abstraction the claims could be characterized as using machine learning to generate alternative travel itineraries; Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016)—process of gathering and analyzing information of a specified content, then displaying the results, similar because at another level of abstraction the claims could be characterized as process of gathering and analyzing information of alternative travel itineraries, then displaying the results; Smart Sys. Innovations v. Chicago Transit Authority, 873 F.3d 1364 (Fed. Cir. 2017)—formation of financial transactions in a particular field (i.e., mass transit) and data collection related to such transactions, similar because at another level of abstraction the claims could be characterized as modification of transactions in a particular field (i.e., travel) and data collection related to such transactions).
These cases describe significantly similar aspects of the claimed invention, albeit at another level of abstraction. See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) ("An abstract idea can generally be described at different levels of abstraction. As the Board has done, the claimed abstract idea could be described as generating menus on a computer, or generating a second menu from a first menu and sending the second menu to another location. It could be described in other ways, including, as indicated in the specification, taking orders from restaurant customers on a computer.").
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The elements merely serve to provide a general link to a technological environment (e.g., computers and the Internet) in which to carry out the judicial exception (interactive generative artificial intelligence system; fine-tuned machine learning module; electronic indication; interaction interface; computing system comprising: one or more processors, and one or more memories having stored thereon computer-executable instructions—all recited at a high level of generality).
The claims also recite configuring an interactive generative artificial intelligence system for providing respective interaction interface electronically to the traveler, the interactive generative artificial intelligence system comprising a plurality of trained system objects, each system object including a respective trained machine learning module fine-tuned on historical traveler-specific or traveler-category data to generate weights for nodes and edges of a graph network based on prompt inputs. This limitation describes using generic machine learning on new preferred data without setting forth any technological improvements. At the high level of generality set forth, the technical aspects of machine learning here are generic because they could be used across a multitude of data environments. Moreover, the claims do not set forth any improvements to machine learning itself, and instead use it as a tool to perform an abstract function (i.e., remedying travel disruptions for people). "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). "The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement" because "[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12 (emphasis added).
Although the claims have and execute instructions to perform the abstract idea itself (e.g., modules, program code, etc. to automate the abstract idea), this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." Aside from such instructions to implement the abstract idea, they are solely used for generic computer operations (e.g., receiving, storing, retrieving, transmitting data), employing the computer as a tool. See FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) ("[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter.") (citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245,1256 (Fed. Cir. 2014)) (emphasis added).
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools to improve the functioning of the abstract idea identified above. Looking at the additional limitations and abstract idea as an ordered combination and as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Rather than any meaningful limits, their collective functions merely provide generic computer implementation of the abstract idea identified in Prong One. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2 (i.e., they amount to nothing more than a general link to a particular technological environment and instructions to apply it there). Moreover, the additional elements recited are known and conventional computing elements (interactive generative artificial intelligence system; fine-tuned machine learning module; electronic indication; interaction interface; computing system comprising: one or more processors, and one or more memories having stored thereon computer-executable instructions—see Specification ¶¶ 0049, 61-64, 78-80 describing these at a high level of generality and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the technical particulars of such additional elements to satisfy the statutory disclosure requirements).
With respect to configuring an interactive generative artificial intelligence system for providing respective interaction interface electronically to the traveler, the interactive generative artificial intelligence system comprising a plurality of trained system objects, each system object including a respective trained machine learning module fine-tuned on historical traveler-specific or traveler-category data to generate weights for nodes and edges of a graph network based on prompt inputs, this also fails to provide "significantly more" for the same reasons it does not integrate the abstract idea into a practical application. See also Specification ¶¶ 0049-50, 78-80, describing training and deploying machine learning models without any significant technical detail.
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, storing, retrieving, transmitting data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these basic computer functions).
"The use and arrangement of conventional and generic computer components recited in the claims—such as a database, user terminal, and server— do not transform the claim, as a whole, into 'significantly more' than a claim to the abstract idea itself. We have repeatedly held that such invocations of computers and networks that are not even arguably inventive are insufficient to pass the test of an inventive concept in the application of an abstract idea." Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1056 (Fed. Cir. 2017) (citations and quotation marks omitted). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented (i.e., they merely narrow the abstract idea without adding any new additional elements beyond it). Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general link to a particular technological environment and instructions to implement the abstract idea as the independent claims (e.g., the interactive generative artificial intelligence system).
Claims 2-4, 14, and 17 recite a generic computing system/external system performing abstract booking activities. Claim 5 recites an "online" travel agency which merely limits the commercial practice to a generic application on the Internet. Claims 9-10 recites a trained graph-network machine learning model trained to generate a graph network. As with the AI tools used in claim 1, this is merely linking the abstract idea to a particular technological environment (i.e., one that also uses a generic machine learning model that creates a network graph). Claims 19-20 recite a generic database merely used for storage and retrieval of commercial data.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. Although they add the elements identified in 2A above, these do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Moreover, the Specification also indicates this is the routine use of known components for the same reasons presented with respect to the elements in the independent claims above (i.e., they are set forth at a high level of generality without any technical improvements and merely used as tools to perform the abstract idea; see Specification ¶¶ 0043, 56—generic booking computing system/external booking system; ¶ 0056—online travel agency; ¶¶ 0043-44—trained graph-network machine learning model trained to generate a graph network; ¶¶ 0041-42—database). Accordingly, they are not directed to significantly more than the exception itself, and are not eligible subject matter under § 101.
Conclusion
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 DANIEL VETTER whose telephone number is (571)270-1366. The examiner can normally be reached M-F 9:00-6:00.
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/DANIEL VETTER/Primary Examiner, Art Unit 3628