DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/27/2026 has been entered.
Status of Claims
The following is a non-final Office action in response to the communication filed 4/27/2026.
Claims 1, 4-6, 11, and 14-16 have been amended. Claims 1-20 are currently pending and have been examined.
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 .
Response to Arguments
Applicant’s amendments and associated arguments, filed 4/27/26, with respect to the rejection of the claims under 35 U.S.C. §112(a) have been considered and are persuasive. The associated rejection has been withdrawn.
Applicant’s amendments and associated arguments, filed 4/27/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §101 have been considered but they are not persuasive. The newly amended limitations merely further characterize a computer-centric environment in which to apply the abstract idea.
Applicant’s amendments and associated arguments, filed 4/27/26, with respect to the rejection of claims 1-20 under 35 U.S.C. §103(a) have been considered and are persuasive. The associated rejection has been withdrawn.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-20 are directed to a method (process), a system (machine or manufacture), and a non-transitory medium (manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those indicated in bold below:
1. A method, comprising: receiving, from a client device, image data representing a screenshot of an itinerary wherein the screenshot of the itinerary includes a user interface element; generating a machine learning model configured to identify query attributes based at least in part on training data, wherein the training data includes first weights associated with the query attributes; determining, using the machine learning model and based at least in part on the image data representing the screenshot of the itinerary including the user interface element, an origin channel associated with the user interface element of the screenshot; generating, from the image data and using the machine learning model, one or more query attributes to be used for one or more web resource requests for travel data, wherein the one or more query attributes at least partially include the origin channel and are associated with the first weights; transmitting, by a server, one or more requests for the travel data that satisfy the one or more query attributes; receiving, at a first time and in response to the one or more requests, a first portion of travel data that satisfies the one or more query attributes; generating, based at least in part on receiving the first portion of travel data, a primary itinerary recommendation, the primary itinerary recommendation comprising a first set of one or more itineraries that satisfy the one or more query attributes; transmitting the primary itinerary recommendation to the client device for presentation via a user interface; receiving a response associated with the primary itinerary recommendation; generating an updated machine learning model configured to identify query attributes based at least in part on updated training data, wherein the updated training data includes second weights associated with the query attributes and based at least in part on the response; receiving, at a second time after the first time and in response to the one or more requests, a second portion of travel data satisfying the one or more query attributes; generating, based at least in part on receiving the second portion of travel data and the updated machine learning model, a secondary itinerary recommendation, the secondary itinerary recommendation comprising a second set of one or more itineraries that satisfy the one or more query attributes, wherein the second set of one or more itineraries differs at least in part from the first set of one or more itineraries; and transmitting the secondary itinerary recommendation to the client device for presentation via the user interface.
Claims 11 and 15 recite analogous limitations to those identified as abstract above with respect to claim 1.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than reciting processing devices, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing devices does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One , the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites additional elements, which are underlined below.
1. A method, comprising: receiving, from a client device, image data representing a screenshot of an itinerary wherein the screenshot of the itinerary includes a user interface element; generating a machine learning model configured to identify query attributes based at least in part on training data, wherein the training data includes first weights associated with the query attributes; determining, using the machine learning model and based at least in part on the image data representing the screenshot of the itinerary including the user interface element, an origin channel associated with the user interface element of the screenshot; generating, from the image data and using the machine learning model, one or more query attributes to be used for one or more web resource requests for travel data, wherein the one or more query attributes at least partially include the origin channel and are associated with the first weights; transmitting, by a server, one or more requests for the travel data that satisfy the one or more query attributes; receiving, at a first time and in response to the one or more requests, a first portion of travel data that satisfies the one or more query attributes; generating, based at least in part on receiving the first portion of travel data, a primary itinerary recommendation, the primary itinerary recommendation comprising a first set of one or more itineraries that satisfy the one or more query attributes; transmitting the primary itinerary recommendation to the client device for presentation via a user interface; receiving a response associated with the primary itinerary recommendation; generating an updated machine learning model configured to identify query attributes based at least in part on updated training data, wherein the updated training data includes second weights associated with the query attributes and based at least in part on the response; receiving, at a second time after the first time and in response to the one or more requests, a second portion of travel data satisfying the one or more query attributes; generating, based at least in part on receiving the second portion of travel data and the updated machine learning model, a secondary itinerary recommendation, the secondary itinerary recommendation comprising a second set of one or more itineraries that satisfy the one or more query attributes, wherein the second set of one or more itineraries differs at least in part from the first set of one or more itineraries; and transmitting the secondary itinerary recommendation to the client device for presentation via the user interface.
Claim 11 further recites a system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions that, when executed, cause the system to perform the method of claim 1.
Claim 15 further recites one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the method of claim 1.
The functions (i.e., transmitting/receiving/storing/processing/displaying data) of the claimed computer components (client device, server, system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions; one or more non-transitory computer-readable media storing instructions .. executed…[by] one or more processors; the computer-centric environment) are recited at a high level of generality and are merely invoked as tool to perform the abstract idea.
The receipt of screenshot information (including the user interface element) from a client device is recited at a high level of generality (i.e. as a general means of gathering information for evaluation), and amounts to mere data gathering, which is a form of pre-solution activity
The presentation via the user interface is recited at a high level of generality (i.e. as a general means of displaying the outcome of the analysis), which is a form of insignificant post-solution activity.
The functions of the machine learning model (using training data to generate/update weights) is recited at a high level of generality and is invoked as a tool to perform the abstract idea and the training of said machine learning model amounts to extra-solution activity.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the functions of the additional elements (client device, server, system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions; one or more non-transitory computer-readable media storing instructions .. executed…[by] one or more processors; the computer-centric environment, machine learning model) amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As indicated above, the receipt of screenshot information (including a user interface element) from a client device is recited at a high level of generality (i.e. as a general means of gathering information for evaluation), and amounts to mere data gathering, which is a form of pre-solution activity and the presentation via the user interface is recited at a high level of generality (i.e. as a general means of displaying the outcome of the analysis), which is a form of insignificant post-solution activity. The specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). Additionally, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Furthermore, the Content Extraction and Transmission court decision cited in MPEP 2106.05(d)(II) indicate that electronically scanning or extracting data from a document is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). MPEP 2106.05(d)(II), and the cases cited therein, including in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
As indicated above, the training of the machine learning model amounts to an additional element (extra-solution activity). With respect to the training process, Danielsson et al. (US 20180096232) identifies that “as known to the skilled person, stochastic gradient descent is the most well-known method for calculating how to update the weights in the neural network so as to get a model that is as close as possible to producing the desired output (see [0041]) and Ghosh (US 20200356809) identifies that “the model and its associated weights and variables are updated using a technique known as training. Known input/output sets are used to adjust the model variables and weights, so the model can be applied to inputs with unknown outputs (see [0037]).”
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
Claims 2-4 and 12-14 recite limitations that further characterize the generation of the query attributes, which further characterizes the previously recited abstract concepts. Claims 2 and 12 recite the additional elements of transmitting data between devices over a network and performing optical character recognition of image data, which have been identified in the analysis above as functions that are extra-solution and well-understood, routine and conventional computing functions. Claims 4 and 14 recite the additional element of a machine learning model, which when recited at this level of generality amounts to instructions to implement the abstract idea on a generic computer.
Claim 5 recites limitations that further characterize the generation of the itinerary recommendations, which further characterizes the previously recited abstract concepts. Claim 5 also recites the additional element of a machine learning model, which when recited at this level of generality amounts to instructions to implement the abstract idea on a generic computer.
Claims 6 and 16 recite limitations that further characterize the generation of the itinerary recommendations, which further characterizes the previously recited abstract concepts. For the reasons described above with respect to claims 1, 11 and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claims 7 and 17 recite limitations that further characterize the generation of the recommendation and associated option to purchase, which further characterizes the previously recited abstract concepts. Claims 7 and 17 also recite the additional elements of user interface control and subsequent display of information, which amount to extra-solution activity. The specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). Additionally, the Symantec, Internet Patent Corp. court decision cited in MPEP 2106.05(d)(II) indicate that a web browser’s back and forward functionality is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Additionally, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Claims 8 and 18 recite limitations directed to threshold periods of times associated with the request for travel data and subsequent instruction to cease data collection after a threshold period of time, which is a process that can be performed in the human mind and is therefore a mental process. Claims 8 and 18 also recite the additional element of transmitting the instructions, which as indicated above amounts to extra-solution activity that is well-understood, routine and conventional.
Claims 9 and 19 recite limitations that represent additional iterations of the limitations presented and analyzed above with respect to claims 1, 11 and 15. See the analysis of claims 1, 11 and 15 above.
Claims 10 and 20 recite limitations that further characterize the generation of the itinerary recommendations, which further characterizes the previously recited abstract concepts. Claims 10 and 20 also recite the additional element of transmitting information between devices, which as indicated above amounts to extra-solution activity that is well-understood, routine and conventional.
Potentially Allowable Subject Matter
Claims 1-20 currently stand rejected under 35 USC 101, but would be allowable should this rejection be overcome.
The following is a statement of reasons for the indication of allowable subject matter:
Previously identified prior art, Grebenikof (US 20200410566 A1), is directed to a system and method for dynamic search, price comparison and optimization. Grebenikof, generally discloses the receipt of image data representing a screenshot, but does not necessarily disclose the capture of a user interface element (though it is strongly suggested) and the use of machine learning to identify query attributes from the image, which are used to request and receive itinerary recommendations. Grebenikof, in Fig. 10A (reference to frequent changes) and [0048] (reference to changes and associated updates), strongly suggests receipt of second portions of travel data (i.e. iterative data updates).
Previously identified prior art, Kelly (US 20150199621 A1), is directed to a method and system for dynamic information connection search engine. Kelly discloses the receipt of multiple requests for information and utilization or responses to provide further recommendations.
Previously applied prior art, Annakov (US 20210264326), is directed to methods and systems, including an automated flight-recommendation-and-booking system that provide accurate, short lists of flights that best match a user's preferences and flight parameters specified by the user. Annakov generally discloses the use of a machine learning model that considers user preferences and makes updates to said model (and weighting) based on user interactions.
Previously applied prior art, Iyer (US 20210034684 A1), is directed to a system and method of generating personalized search results. Iyer generally discloses the weighting of query attributes based on user profile preferences.
Newly identified prior art, Cicoretti (US 20140279537), generally discloses the recognition of a unique merchant code displayed on a website to identify associated merchant and product information.
Newly identified prior art, McClave et al. (US 20180053224), generally discloses the use of an image recognition engine to determine a website, product, etc. for use in a query.
While the aforementioned references disclose each of the elements of the invention, the combination of references does not fully capture the structure and interplay of the elements as recited in the claims. Therefore, upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious all the features of applicant’s invention as the features amount to more than a predictable use of elements in the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Relevant prior art, Thukkharam (12,424,009 B1), disclosing (18) Instead of performing OCR on a full image capture screenshot of the entire page, the OCR can be performed on a significantly reduced area (e.g., less than 75% of the maximum size of page). In some examples, OCR is performed on less than 50% of a page. In some examples, OCR is performed on no more than 25% of a page. Reducing the total percentage of the screen for which OCR is to be performed is beneficial for CPU resource constrained platforms.
Relevant prior art, Chintakindi (WO2020069154 A1), discloses the extraction, formatting and storge of observation data from a screenshot, but does not disclose the use of said data for any form of query.
Relevant prior art, Ghahramani (US 20160371798), discloses the user may provide non-textual input, such as an image or a video of a city as the travel criteria.
Relevant prior art, Wen (US 20090157664), discloses the submission of a plain-text document containing an itinerary, wherein the document is parsed, categorized and re-structured for the population of an itinerary database which is subsequently searched by a user rather than used in the formation of the search query itself.
Relevant prior art, Renaudie (US 20150317569) discloses the receipt of an image or scene, extraction of visual elements and retrieval of search results relevant to the associated query.
Relevant prior art, Yang (US20170132535), discloses detection of data cards with travel information that is used to form a purchasable itinerary.
Relevant prior art, Garman (US7979457), discloses entry of itinerary information via a website and the subsequent identification of the most relevant travel suppliers of purchasable itineraries..
Relevant prior art, English (US 20090150343), discloses a travel reservation search engine that constructs a first query from one or more constraints and, if determined necessary by the search engine, a second query is constructed from one or more constraints.
Relevant prior art, Garman (US7979457), discloses entry of itinerary information via a website and the subsequent identification of the most relevant travel suppliers of purchasable itineraries.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABBY J FLYNN whose telephone number is (571)272-9855. The examiner can normally be reached Monday - Friday 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at (571) 272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABBY J FLYNN/ Primary Examiner, Art Unit 3663
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