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 Claims
This action is in reply to the RCE filed on 6/13/2025.
Claims 1, 3-4, 7, 9, 11-13, 15, 17, and 19-20 have been amended and are hereby entered.
Claims 21-22 have been added.
Claims 6 and 14 have been canceled.
Claims 1-5, 7-13, and 15-22 are currently pending and have been examined.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/13/2025 has been entered.
Response to Applicant’s Arguments
Claim Rejections – 35 USC § 112
The present amendments to the claim obviate the previous 112(a) and 112(b) rejections thereto; therefore, these rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant begins their 101 arguments with the same incorrect assertion regarding the standards of Step 2A, Prong One which has now been refuted multiple times in the prosecution of this application. This assertion, that Prong One requires consideration of the claim as a whole, is no more correct now than the previous two instances this assertion was advanced. As previously explained, Prong One is performed on a limitation-by-limitation basis, while Steps 2A, Prong Two and 2B are performed while considering the claim as a whole. Given that this identical language has now been presented in multiple Remarks despite being previously refuted, this language appears to be boilerplate. Examiner strongly recommends revising this boilerplate given its incorrect assertion of Prong One standards, resulting in past and present flawed Prong One analyses. Even if Applicant’s further Prong One assertions regarding the nature of several newly drafted limitations (ie: as reciting abstract ideas or additional elements) were correct (which, to be clear, they are not – see below), this failure to understand the limitation-by-limitation based analysis of Step 2A, Prong One creates a foundational failure in Applicant’s overall conclusion that “Applicant contends that the inquiry into the abstraction of Applicant’s claims should end at this first prong as Applicant’s claims do not fall into any of the enumerated groupings and therefore cannot be abstract,” rendering it unpersuasive before even considering the newly drafted limitations (ie: by way of presently repeated limitations of the independent claims which were previously found to recite abstract ideas).
Regarding the substance of Applicant’s Prong One arguments regarding individual limitations, these arguments are no more correct or persuasive than Applicant’s overall Prong One conclusion discussed above. Critically to each of these specific arguments is that it is well-established that merely claiming an abstract step as being carried out by/via computer elements neither prevents said steps from reciting abstract ideas under Prong One nor somehow renders a claim subject matter eligible. This was established in the seminal Alice case, has been upheld in countless subsequent case law, and is illustrated in myriad examples in both the MPEP (see in particular MPEP 2106.04(a)(2)) and the Examples of the most recent PEG. See these sources for more information. For example, choosing an order of prompts and feeding them into a model in said order is an abstract step. Other than the specifying of this model as a “multi-modal AI model,” there is nothing technological about this step, which may be effectuated the same way manually in relation to the building of a pen-and-paper model.
As another example, Applicant’s argued limitations regarding receipt of model result feedback from a user and the use of that feedback to modify the presented model results are both abstract steps that may be effectuated manually to achieve the same results. Specifying that the results are presented in the form of a “digital document” and that said feedback and presentation of this digital document be respectively received from and performed by a GUI is the same type of high-level instruction to perform these abstract steps on computer elements discussed above as insufficient to prevent recitation of abstract ideas or evidence eligible subject matter (in addition to the sources referenced above, see also MPEP 2106.05(f) regarding Step 2A, Prong Two and 2B analysis of such features). Again, confining these abstract steps to being effectuated by/via particular technological elements (such as the asserted GUI) in no way prevents the underlying function from reciting an abstract idea. Arguing that these steps “require a computer” is only true in that the limitations are claimed as being performed by computer elements; as noted above, the steps themselves may nonetheless be effectuated manually to the same results. As noted above, both the MPEP and the Examples of the PEG contain myriad examples of such computer-effectuated steps nonetheless reciting abstract ideas. Lastly regarding Applicant’s Prong One arguments, the repeated assertions regarding the time necessary for image generation has no bearing on the recitation of abstract ideas under Prong One.
Regarding Step 2A, Prong Two, while Applicant’s presented argument does not explicitly label it as such, it appears clear to Examiner from the content of said argument that Applicant is attempting to invoke the consideration of an improvement to a technology (see MPEP 2106.04(d), 2106.04(d)(1), 2106.05(a)). Examiner’s analysis here is based on this assumption. This argument, specifically that “[b]y ordering the prompts, and possibly delaying some of them, the AI model has enough time to think and come up with an accurate document that includes both text and images,” fails for multiple reasons.
Firstly and most importantly, this is not actually embodied in the claims as presently drafted (as phrased in MPEP, the claim doesn’t “reflect[]” the asserted improvement). While the claims do recite the determining of an order for the prompts and the input of said prompts in said determined order, the purported improvement of giving the AI model “enough time to think” (or as phrased in the specification, “time to understand and process the input data”) does not flow from this ordering of prompts. Rather, if anything, this might stem from the delaying of some prompts, which Applicant admits in this argument is not actually embodied in the claims as presently drafted (ie: “possibly delaying some of them”). Further, the claims do not place any temporal limit on the processing time of the AI model to generate the text and images making up the digital document (nor, Examiner notes, does the original disclosure appear to provide any support for doing so), thus even if this argued but unclaimed delaying of prompt inputs were claimed, this result would still not be captured in such hypothetical claim language. As such, this argument is moot in view of the present claim drafting.
Secondly, Applicant fails to properly support this argument, as Applicant has not explained what additional elements Applicant believes effectuate this result, nor explain how any such hypothetical additional elements do so, either individually or in combination. Examiner notes that, even as presently amended, the majority of the claims recite abstract ideas rather than additional elements (see discussion of Prong One arguments above and updated 101 rejections below for more information). This leaves little with which Applicant can make such an argument.
Thirdly, even if this argued function of giving the AI model “enough time” (and what “enough time” might constitute or how it might be determined is unclear from the specification) to understand and process the input prompts were properly embodied in the claims, Applicant fails to meet the evidentiary requirements for such a purported improvement to a technology. As explained in MPEP 2106.04(d)(1): “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” This is what is present in the original disclosure: a bare assertion of an improvement without sufficient detail to evidence such an improvement. Specifically, the only mention of this purported improvement Examiner was able to find in the original disclosure are two throwaway assertions in Paragraphs 0059 and 0062 as filed, which lack meaningful detail as to how and why this purported benefit would be achieved.
Regarding Applicant’s assertion that the claims do not monopolize the judicial exception, Examiner notes that this is not the proper standard for consideration of subject matter eligibility. As stated in MPEP 2106.04(I): “While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B).”
Regarding Step 2B, Applicant repeats the same conclusory and entirely unexplained language presented and refuted in the previous Remarks. Summarily, Applicant states absent any explanation or analysis that the ordered combination of elements (not even properly articulated as additional elements, as only those may evidence an inventive concept under Step 2B as previously explained) is sufficient to ensure that the claim amounts to significantly more than the judicial exception. This fails for the same reasons as explained previously; namely, that this implies an inventive concept under Step 2B yet does not even attempt to articulate what that inventive concept might be, ignores the distinction between abstract ideas and additional elements, and performs no analysis regarding what additional elements are present in the claim to show evidence of some purported inventive concept. Such entirely unsupported and unexplained assertions will not advance prosecution. Examiner maintains that, even as presently amended, there is no inventive concept which makes itself known when considering the few recited additional elements either individually or in combination.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Applicant reiterates previously advanced and refuted arguments, containing the same errors previously explained to Applicant. These repeated arguments are no more persuasive now than when advanced previously. For example, the notion that the AI of Avital is not generative AI is untrue, as explained in the Final Rejection of 3/14/2025, and the present bare reiteration of this assertion does nothing to answer said explanation nor provide a persuasive argument. In light of this foundational flaw in Applicant’s argument, Applicant’s follow-up arguments that, based on this erroneous premise, the AI application of Avital would be unable to modify content (or “regenerate,” as presently argued) of the digital document is likewise untrue and unpersuasive. Additionally, Applicant again largely repeats the same previously pointed out error of arguing Avital in isolation fails to disclose particular previously claimed features (e.g., AI-based image generation) wherein Avital was not cited against such functionality alone but rather in combination with Chambers (and in relation in particular to AI-based image generation, Avital is not cited as disclosing this feature).
In paragraphs following this argument that Avital in isolation fails to disclose such features, Applicant provides a paragraph summarizing some of the disclosure of Chambers, followed by a one-sentence conclusory statement that “Chambers fails to cure any of the deficiencies above with respect to Avital” (and similar statements regarding the Kocis and DiMaria references cited against various dependent claims). This bare conclusory statement fails to engage with any particular claimed feature or any citation provided against such above-argued features. This is particularly egregious regarding features which were previously claimed (such as the aforementioned AI-based image generation), wherein citations to these references are already on record as disclosing such features. This bare conclusory statement fails to provide a proper argument for such previously claimed and rejected features, giving Examiner no understanding of how Applicant reaches this conclusion, what (if anything) Applicant finds deficient in said previously provided citations to these features, and nothing particular to which Examiner may reply. This conclusory statement amounts to no more than stating “I disagree,” absent any evidence or reasoning as to why or how Applicant disagrees. Generally, regarding previously claimed and rejected features and functions, Examiner maintains that the previously provided references (including explicit combinations and citations thereof) continue to disclose such features in the same way as previously explained.
Regarding newly claimed features of the present amendments, arguments related thereto are rendered moot by way of the updated 103 rejections below.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-13, and 15-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 17 each contain variations on the following limitations: “display queries on a graphical user interface of a software application” and “receive feedback about the digital document from a graphical user interface (GUI) of the software application.” It is unclear as drafted whether the term “a graphical user interface (GUI) of the software application” of the latter limitation is intended to relate back to the term “a graphical user interface of a software application” of the former limitation, or to disclose a second GUI. For the purposes of this examination, the term of the former limitation will be interpreted as “a graphical user interface (GUI) of a software application” and the term of the latter limitation will be interpreted as “the graphical user interface (GUI) of the software application” (or simply the equivalent “the GUI”). Claims 2-5, 7-8, 10-13, 15-16, and 18-22 are rejected due to their dependence upon Claims 1, 9, and 17 respectively.
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 1-5, 7-13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claims 1, 9, and 16, the limitations of display queries on a graphical user interface of a software application; receive responses to the queries and combine the responses with the queries, respectively, to generate prompts; generate a document comprising a description of a plurality of events and images that represent the plurality of events based on execution of a model; determining an order for the prompts and input the prompts in the order to the model to cause the model to generate the document; receive feedback about the document; and modify content within the document based on the model and the feedback and render the modified document, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of display queries on a graphical user interface of a software application; receive responses to the queries and combine the responses with the queries, respectively, to generate prompts; generate a document comprising a description of a plurality of events and images that represent the plurality of events based on execution of a model; determining an order for the prompts and input the prompts in the order to the model to cause the model to generate the document; receive feedback about the document; and modify content within the document based on the model and the feedback and render the modified document, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of generate a document comprising a description of a plurality of events and images that represent the plurality of events based on execution of a model, and modify content within the document based on the model and the feedback and render the modified document, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a memory; a processor communicably coupled to the memory; a computer-readable storage medium comprising instructions stored therein executable by a processor; ingest web pages from websites via application programming interfaces (APIs); a graphical user interface (GUI) of a software application; a digital document; and a multi-modal artificial intelligence (AI) model. A memory; a processor communicably coupled to the memory; a computer-readable storage medium comprising instructions stored therein executable by a processor; a graphical user interface (GUI) of a software application; and a multi-modal artificial intelligence (AI) model amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)) in the context of the claims as a whole. Ingest web pages from websites via application programming interfaces (APIs) amounts to no more than insignificant extra-solution activity (see MPEP 2106.05(g)) in the context of the claims as a whole. A digital document amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)) in the context of the claims as a whole. Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, insignificant extra-solution activity, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. The limitation found to recite insignificant extra-solution activity, upon reevaluation, is further determined to be well-understood, routine, and conventional as per MPEP 2106.05(d) because it represents receiving or transmitting data over a network, and storing and retrieving information in memory, which are judicially recognized as well-understood, routine, and conventional activity. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.
Claims 2-5, 7-8, 10-13, 15-16, and 18-22, describing various additional limitations to the system of Claim 1, method of Claim 9, or product of Claim 17, amount to substantially the same unintegrated abstract idea as Claims 1, 9, and 17 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claims 2, 10, and 18 disclose generate a date, a destination, and a mode of transportation (an abstract idea in the form of a certain method of organizing human activity and a mental process); and include the date, the destination, and the mode of transportation in the digital document (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claims into a practical application.
Claims 3, 11, and 19 disclose generate the queries based on execution of the multi-modal Al model on the web pages and a profile stored within a data store (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claims into a practical application.
Claims 4, 12, and 20 disclose ingest additional web pages from the websites via the APIs (insignificant extra-solution activity); and modify the content within the digital document based on the additional web pages (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claims into a practical application. Additionally, the limitation found to recite insignificant extra-solution activity, upon reevaluation, is further determined to be well-understood, routine, and conventional as per MPEP 2106.05(d) because it represents receiving or transmitting data over a network.
Claims 5 and 13 disclose wherein the processor is further configured to ingest preference data from a profile (insignificant extra-solution activity); and generate the digital document based on execution of the multi-modal Al model on the preference data from the profile (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claims into a practical application. Additionally, the limitation found to recite insignificant extra-solution activity, upon reevaluation, is further determined to be well-understood, routine, and conventional as per MPEP 2106.05(d) because it represents receiving or transmitting data over a network.
Claims 7 and 15 disclose execute the multi-modal Al model on the feedback to retrain the multi-modal Al model (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claims into a practical application.
Claims 8 and 16 disclose wherein the processor is further configured to determine a destination and generate an image of the destination based on the execution of the multi-modal Al model (an abstract idea in the form of a certain method of organizing human activity and a mental process); and store the destination and the image of the destination within the digital document (insignificant extra-solution activity), which do not integrate the claims into a practical application. Additionally, the limitation found to recite insignificant extra-solution activity, upon reevaluation, is further determined to be well-understood, routine, and conventional as per MPEP 2106.05(d) because it represents storing and retrieving information in memory.
Claim 21 discloses wherein the processor is further configured to receive an additional input which includes a description of changes to the digital document (an abstract idea in the form of a certain method of organizing human activity and a mental process), and execute the multi-modal Al model on the description of the changes to generate the modified content of the digital document (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 22 discloses wherein the processor is further configured to send a message to a network-connected device (mere instructions to apply a judicial exception) to confirm an event from the plurality of events (an abstract idea in the form of a certain method of organizing human activity and a mental process), and update the digital document to indicate that the event is confirmed (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5, 8-11, 13, 16-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Avital (US 11755963) (hereafter, “Avital”) in view of Chambers et al (PGPub 20240428316, claiming the benefit of Provisional Application 63522345) (hereafter, “Chambers”) and Skarphedinsson et al (US 12126643) (hereafter, “Skarphedinsson”).
Regarding Claims 1, 9, and 17, Avital discloses:
a memory (Column 4, lines 29-52; Column 16, line 59 through Column 17, 8; Figs. 1, 3; one or more memories; storage device);
a processor communicably coupled to the memory (Column 4, lines 29-52; Column 16, line 51 through Column 17, 8; Figs. 1, 3; one or more hardware processors; a server computer coupled to a storage device; hardware processor coupled to a memory via a bus);
a computer-readable storage medium comprising instructions stored therein which are executable by a processor (Column 16, line 59 through Column 17, line 2; Column 17, lines 40-52; Claim 11; a non-transitory computer-readable media storing instructions executable by one or more processors);
ingest web pages from websites via application programming interfaces (APIs) (Column 8, lines 14-23; data aggregator is programmed or configured to retrieve data from various sources, such as one or more travel product providers; data aggregator may use an API of a third-party travel provider to retrieve travel product information from the third-party travel provider);
display queries on a graphical user interface of a software application (Column 4, line 61 through Column 5, line 10; Column 12, line 63 through Column 13, line 64; Figs. 2A-2D, 2G; search form comprises a plurality of buttons and search widgets for selecting trip options, such as destinations and dates; graphical search panel which displays a plurality of search parameters; the search parameters may include the values that were selected via input from the client computer from the search form; client computing device includes a client application, which is software that displays, uses, supports, or otherwise provides travel planning and booking functionality as part of the application or software);
receive responses to the queries via the graphical user interface and combine the responses with the queries, respectively, to generate prompts (Column 7, lines 13-29; Column 12, line 63 through Column 13, line 64; Figs. 2A-2D, 2G; search form comprises a plurality of buttons and search widgets for selecting trip options, such as destinations and dates; graphical search panel which displays a plurality of search parameters; the search parameters may include the values that were selected via input from the client computer from the search form; user data is digital data storing user information, which may include search history such as filters used and request data entered; anyone of ordinary skill in the art would recognize that such data must be stored in relation to the field into which it was entered, as raw data stored absent context would not be usable in the manners described in this reference);
receive feedback about the digital document from a graphical user interface (GUI) of the software application (Column 3, lines 30-41; Column 3, line 40 through Column 4, line 3; Column 7, lines 13-29; Column 10, line 61 through Column 11, line 7; Column 12, lines 2-5; Column 13, lines 7-18; the intelligent assistant may be programmed or configured suggest modifying existing components in an attempt to achieve the best possible trip configuration for the user's intent; the additional or modified trip elements may be based on the availability of these components and/or the user's preferences; the user may be searching for travel items near Orlando, Florida during March (typically spring break season) and the user's information indicates that the user travels with kids; based on the information, intelligent assistant may suggest alternate dates that have better prices for a trip to Disneyworld or alternate but similar destination; after hotel and/or flight options are selected by the server, the results are displayed at the client computer; selecting graphical search panel may cause display of search widgets which allow modification of search parameters, such as destination or dates; a travel booking application or website includes a trip dashboard, which is a graphical user interface (GUI) for creating, viewing, and modifying trips; the trip dashboard may provide GUI for receiving user input, such as search or filter parameters and trip element selections, displaying suggested trip elements to a user, and facilitating booking of a trip); and
modify content within the digital document based on the multi-modal Al model and the feedback and render the modified digital document in the GUI (Column 3, lines 30-41; Column 3, line 40 through Column 4, line 3; Column 7, lines 13-29; Column 10, line 61 through Column 11, line 7; Column 12, lines 2-5; Column 13, lines 7-18; the intelligent assistant may be programmed or configured suggest modifying existing components in an attempt to achieve the best possible trip configuration for the user's intent; the additional or modified trip elements may be based on the availability of these components and/or the user's preferences; the additional or modified trip elements may be selected based on optimizing the trip according to one or more user criteria; a travel booking application or website includes a trip dashboard, which is a graphical user interface (GUI) for creating, viewing, and modifying trips; the trip dashboard may provide GUI for receiving user input, such as search or filter parameters and trip element selections, displaying suggested trip elements to a user, and facilitating booking of a trip).
Avital additionally discloses generate a digital document comprising a description of a plurality of events based on execution of the AI model; presented digital document may include both a plurality of events and images that represent the plurality of events (Column 3, lines 1-6; Column 7, lines 13-29; Column 11, lines 38-44; Column 13, line 19 through Column 15, line 5; Figs. 2C-2H; a travel booking system includes an intelligent assistant, which is an artificial intelligence program or module that suggests or optimizes trip elements based on a user's selections; determining suggested trip elements may be performed, in whole or in part, by one or more machine learning models, which may be trained to receive inputs such as selected trip elements, origin, destination, trip category, search parameters, and/or user data, and output one or more suggested trip elements; user data is digital data storing user information, which may include search history such as search results; trip packages may be selected based on customer information and the search parameters by intelligent assistant; presented travel selection options and/or packages thereof may comprise images of the travel selection options as presented in the Figures). Avital does not explicitly disclose but Chambers does disclose wherein the AI model is a multi-modal AI model; wherein the images of the content is generated based on execution of the multi-modal AI model (¶ 0007, 0019, 0033, 0040, 0067; Figs. 6F-6I; generating and providing at least one intelligent travel recommendation based on a media input and/or a multi-modal input using the machine-learning model(s); in some implementations, one or more of the plurality of images 630 may be outputted from the machine-learning model (e.g., one or more images associated with the identified output, associated with the metadata tag, etc.)).
Avital does not explicitly disclose but Skarphedinsson does disclose wherein the software application determines an order for the prompts and input the prompts in the order to the AI model to cause the AI model to generate an output (Column 95, line 36 through Column 97, line 4; Column 99, line 66 through Column 100, line 10; Column 101, lines 16-56; Figs. 13-14; the method includes generating a prompt describing one or more natural language inputs; the one or more natural language inputs described by the prompt may each correspond to a distinct query in that the one or more natural language inputs, if received via the natural language interface, cause a query for information to be generated; each of the natural language inputs ultimately result in a response that is based on that queried information provided via the natural language interface; an initial selection of one or more natural language inputs may be performed for description in the prompt; based on the particular natural language input received via the natural language interface, responses to queries for the received natural language input, and the like, a next selection of natural language inputs may be selected for inclusion in a subsequent prompt; as the user continues to interact with the natural language interface, the user may be presented with other prompts for natural language inputs, effectively guiding or teaching a user how to perform an investigation for some event using the natural language interface; in embodiments where the one or more natural language inputs described in the initially generated prompt correspond to a predefined security workflow a dynamically generated security workflow provided as output by one or more models, the one or more natural language inputs in the other prompt may correspond to next natural language inputs in a sequence or other ordering of natural language inputs for the security workflow in which the received natural language input is included; as natural language inputs are received, new prompts are generated and provided to the natural language interface; such a process may continue as the user performs their desired investigation). Avital additionally discloses wherein the AI model output is the digital document (Column 3, lines 1-6; Column 7, lines 13-29; Column 11, lines 38-44; Column 13, line 19 through Column 15, line 5; Figs. 2C-2H; a travel booking system includes an intelligent assistant, which is an artificial intelligence program or module that suggests or optimizes trip elements based on a user's selections; determining suggested trip elements may be performed, in whole or in part, by one or more machine learning models). Avital does not explicitly disclose but Chambers does disclose wherein the AI model is a multi-modal AI model (¶ 0007, 0019, 0033, 0040, 0067; Figs. 6F-6I; generating and providing at least one intelligent travel recommendation based on a media input and/or a multi-modal input using the machine-learning model(s); in some implementations, one or more of the plurality of images 630 may be outputted from the machine-learning model (e.g., one or more images associated with the identified output, associated with the metadata tag, etc.)).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the travel-based multi-modal AI model techniques of Chambers with the travel product recommendation system of Avital because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Chambers are applicable to the base device (Avital), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined. It would have been further obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the ordered and sequential feeding of prompts into an AI model of Skarphedinsson with the AI-based travel product recommendation system of Avital and Chambers because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Skarphedinsson are applicable to the base device (Avital and Chambers), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claims 2, 10, and 18, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claims 1, 9, and 17. Avital additionally discloses:
wherein the processor is configured to generate a date, a destination, and a mode of transportation (Column 11, lines 8-27; Column 12, line 63 through Column 13, line 18; intelligent assistant may determine a plurality of trip elements for a particular travel date; form for searching for trip products, the form comprising a plurality of buttons and search widgets for selecting trip options, such as destination and dates; search form comprises a plurality of tabs corresponding to a plurality of search options, such as different modes of transportation (cars, flights, cruises) as illustrated in Fig. 2A); and
include the date, the destination, and the mode of transportation in the digital document (Column 11, lines 8-27; Column 12, line 63 through Column 13, line 18; graphical search panel 222, present in many figures such as Fig. 2G, displays the date and destination of suggested travel options/packages, and the options/packages themselves display the mode of transportation).
Regarding Claims 3, 11, and 19, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claims 1, 9, and 17. Avital additionally discloses wherein the processor is configured to generate the queries based on execution of the Al model on the web pages and a profile stored within a data store (Column 3, lines 30-41; Column 3, line 40 through Column 4, line 3; Column 7, lines 13-29; Column 10, line 61 through Column 11, line 7; the intelligent assistant may be programmed or configured suggest modifying existing components in an attempt to achieve the best possible trip configuration for the user's intent; the additional or modified trip elements may be based on the availability of these components and/or the user's preferences; a user’s preferences may be pre-defined in the user’s profile; the user may be searching for travel items near Orlando, Florida during March (typically spring break season) and the user's information indicates that the user travels with kids; based on the information, intelligent assistant may suggest alternate dates that have better prices for a trip to Disneyworld or alternate but similar destinations; user data may include information such as family information and user preferences). Avital does not explicitly disclose but Chambers does disclose wherein the AI model is a multi-modal AI model (¶ 0007, 0019, 0033, 0040, 0067; Figs. 6F-6I; generating and providing at least one intelligent travel recommendation based on a media input and/or a multi-modal input using the machine-learning model(s)).
The rationale to combine remains the same as for Claim 1.
Regarding Claims 5 and 13, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claims 1 and 9. Avital additionally discloses wherein the processor is further configured to ingest preference data from a profile corresponding to the scheduling content (Column 3, line 40 through Column 4, line 3; Column 7, lines 13-29; a user’s preferences may be pre-defined in the user’s profile; user data may include information such as family information and user preferences).
Avital additionally discloses generate the digital document based on execution of the Al model on the preference data from the profile (Column 3, line 40 through Column 4, line 3; Column 11, lines 8-27; Column 14, lines 55-62; the intelligent assistant is programmed or configured to suggest trip elements or narrow a set of suggested trip elements based on a user’s preferences, which may be pre-defined in the user profile). Avital does not explicitly disclose but Chambers does disclose wherein the AI model is a multi-modal AI model (¶ 0007, 0019, 0033, 0040, 0067; Figs. 6F-6I; generating and providing at least one intelligent travel recommendation based on a media input and/or a multi-modal input using the machine-learning model(s)).
The rationale to combine remains the same as for Claim 1.
Regarding Claims 8 and 16, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claims 1 and 9. Avital additionally discloses wherein the processor is further configured to determine a destination based on the execution of the Al model; presented digital document may include both a schedule and an image of the destination (Column 3, lines 15-22; Column 9, line 54 through Column 10, line 6; Column 11, lines 38-44; Figs. 2C-2J; an intelligent assistant is programmed or configured to determine a user's intent based on one or more user selections; the user selections may be a trip category, such as a beach vacations; the intelligent assistant is programmed or configured to receive a trip category and determine one or more suggested trip elements based on the trip category; a trip category may be a destination type such as ‘beach’ or ‘ski,’ a vacation duration such as ‘week,’ ‘weekend,’ ‘long weekend,’ a vacation type such as ‘family’ or ‘romantic,’ and etc.; determining one or more suggested trip elements based on the trip category may be based on pre-defined trip categories associated with the trip category; determining suggested trip elements may be performed, in whole or in part, by one or more machine learning models; the machine learning models may be trained to receive inputs such as trip category, and output one or more suggested trip elements). Avital does not explicitly disclose but Chambers does disclose wherein the AI model is a multi-modal AI model; wherein the images of the digital document is generated based on the execution of the multi-modal AI model (¶ 0007, 0019, 0033, 0040, 0067; Figs. 6F-6I; generating and providing at least one intelligent travel recommendation based on a media input and/or a multi-modal input using the machine-learning model(s); in some implementations, one or more of the plurality of images 630 may be outputted from the machine-learning model (e.g., one or more images associated with the identified output, associated with the metadata tag, etc.)).
Avital additionally discloses store the destination and the image of the destination within the digital document (Column 4, line 61 through Column 5, line 10; Column 13, line 19 through Column 15, line 5; Figs. 2C-2H; search results screen illustrating various travel selection options, such as hotel and travel arrangements; the various Figures illustrate these options being displayed along with images of these travel selection options; in some embodiments, rather than listing travel element options separately, sets of one or more different travel elements may be presented as suggested packages; trip packages may be selected based on customer information and the search parameters by intelligent assistant; one of ordinary skill in the art would recognize that in order to display these pieces of information on a graphical user interface, these pieces of information must be stored together, e.g., via RAM).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 21, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claim 1. Avital additionally discloses:
wherein the processor is further configured to receive an additional input which includes a description of changes to the digital document (Column 13, lines 19-33 and 42-57; Figs. 2C-2D; the search result screen illustrates selectable recommendation options; the search result screen also comprises sort/filter widgets selectable by the user, which correspond to a plurality of respective sort or filter options for sorting and/or filtering the results; the plurality of sort or filter options, when selected, cause applying selected sort and/or filter options to the set of recommended options); and
execute the multi-modal Al model on the description of the changes to generate the modified content of the digital document (Column 13, lines 19-33 and 42-57; Figs. 2C-2D; the search result screen illustrates selectable recommendation options; the search result screen also comprises sort/filter widgets selectable by the user, which correspond to a plurality of respective sort or filter options for sorting and/or filtering the results; the plurality of sort or filter options, when selected, cause applying selected sort and/or filter options to the set of recommended options).
Regarding Claim 22, Avital in view of Chambers and Skarphedinsson discloses the limitations of Claim 1. Avital additionally discloses wherein the processor is further configured to send a message to a network-connected device to confirm an event from the plurality of events, and update the digital document to indicate that the event is confirmed (Column 13, line 64 through Column 14, line 37; Figs. 2E-2F; trip overview screen may be presented to the user in response to the use