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 amendment filed on 11/10/2025.
Claims 1, 3-4, 7, 9, 12, 15, 17, and 20-22 have been amended and are hereby entered.
Claims 1-5, 7-13, and 15-22 are currently pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
All references listed in the IDS documents dated 8/26/2025 and 11/09/2025 have been considered. Examiner notes that US Application 19053942, listed as NPL in the 8/26/205 IDS, should have been listed under US Patent Application Publications, as this application was published prior to the date of this IDS (ie: published as PGPub 20250191062 on 6/12/2025).
Response to Applicant’s Arguments
Claim Rejections – 35 USC § 112
The present amendments to Claims 1, 9, and 17 obviate the previous 112(b) rejections thereto; therefore, these rejections are withdrawn.
Despite Applicant’s assertion of support in the present Remarks, Examiner finds nothing in Applicant’s asserted Paragraphs 0038, 0045, 0065, 0067, 0070, 0072-0074, or 0127 (either as filed or as published; Applicant failed to specify), nor elsewhere in the original disclosure, which fully supports the amendments to the independent claims as presently drafted. See 112(a) new matter rejections below for more information.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
As a preliminary observation, most of Applicant’s present arguments either fail to distinguish between the two prongs of Step 2A, or solely reference Step 2A, Prong Two, consequently failing to take into consideration the limitation-by-limitation based analysis of Prong One (as opposed to the “as a whole” based analysis of Prong Two) as well as the effect the results of the Prong One analysis have on the Prong Two analysis (ie: under Prong Two, integration into a practical application may not be achieved by way of judicial exceptions such as abstract ideas, but rather may only be achieved by way of any recited additional elements or the combination thereof). This causes foundational failings in several of Applicant’s presently presented arguments, such as assertions of improvements stemming from purely abstract concepts. Further, despite explanation of this distinction in the previous Office Actions, particularly regarding the limitation-by-limitation analysis of Prong One, Applicant appears to erroneously argue that Prong One should be determined based on the claim “as a whole.” Applicant is encouraged to review this explanation in the previous Office Action, as well as review the various Examples from the most recent PEG Updates (which illustrate such limitation-by-limitation analysis of Prong One, as well as the effect this analysis has on the Prong Two analysis) in order to properly understand the distinct steps and standards of the 101 subject matter eligibility analysis.
Regarding the substance of Applicant’s arguments, Applicant firstly asserts that “when Claim 1 is ‘viewed as a whole’, Claim 1 is not directed to an abstract concept, but rather a process of generating a digital document such as a travel itinerary with a plurality of scheduled future events, such as travel events, flight events, lodging events, and the like, using generative AI.” Examiner disagrees, noting that what a claim is “directed to” has a particular meaning within the 101 subject matter eligibility context (ie: the result of the Step 2A, Prong Two analysis), and other than the “digital” and “using generative AI” pieces of the above-quoted language, what Applicant asserts Claim 1 is directed to (ie: “a process of generating a [] document such as a travel itinerary with a plurality of scheduled future events, such as travel events, flight events, lodging events, and the like”) is entirely an abstract concept, thus refuting Applicant’s own conclusion. The presence of the non-abstract “digital document” and “using generative AI,” claimed as they are in an extremely high level and results-based manner, fail to integrate this abstract concept into a practical application. See previous and presently updated 101 rejections for more information.
The newly claimed features stressed by Applicant, namely the monitoring of event data (“[f]or example, prices, flight times, availability of lodging, etc.”) in order to detect a lack of accuracy and effectuate updates to the input data and the scheduled events of the document (thereby ensuring the accuracy thereof) likewise represents abstract concepts rather than non-abstract additional elements, and as such cannot show integration into a practical application.
Regarding Applicant’s assertion that “[t]hese are steps that require a computer, and the modification of a digital document stored in a device in this way is not something that is well-understood in the art,” seemingly making a combined argument against multiple steps of the 101 subject matter eligibility analysis simultaneously absent any meaningful explanation of the effect Applicant believes this has on any of these steps, Examiner disagrees. Examiner notes that the high-level claiming of abstract steps as occurring on a computer has now been addressed in multiple previous Office Actions, and Applicant’s conclusions here fail for essentially the same reasons explained in said previous Office Actions. Summarily, such high-level computer implementation of abstract ideas (a) does not prevent recitation of abstract ideas under Step 2A, Prong One, and (b) fails to show integration into a practical application under Step 2A, Prong Two, with Applicant’s conclusions otherwise flying in the face of the analysis and conclusions of the seminal Alice. See previous Office Actions for more information.
A new wrinkle of the above-quoted argument not addressed in the previous Office Actions is the invocation of the Step 2B consideration of well-understood, routine, and conventional activity. Regarding this consideration, Applicant’s unexplained and conclusory assertion is incorrect and misapprehends the well-understood, routine, and conventional consideration including how and to what it applies. This consideration only applies to those claim limitations which are categorized as additional elements, and further sub-categorized as insignificant extra-solution activity (see, e.g., the Step 2B analyses of Example 46, Claim 1 of the October 2019 PEG Update, and of Example 47, Claim 2 and Example 48, Claim 1 of the July 2024 PEG Update). The functionality identified by Applicant does not conform to this categorization, and as such the well-understood, routine, and conventional consideration does not apply. Further, the modification of a digital document, both as claimed and as described in the original disclosure, constitutes an abstract step. Whether judicial exceptions such as abstract ideas are well-understood, routine, and conventional is entirely irrelevant to the 101 subject matter eligibility analysis. See at least MPEP 2106.05(d) for more information.
Regarding Step 2A, Prong Two, Applicant argues that the “claims impose meaningful, practical, and succinct operations that include correcting the accuracy of content within a digital document generated by artificial intelligence. The system can continuously monitor real-time data associated with event content stored within the digital document and automatically update content within the digital document in real-time in response to changes in the event data. This process corrects inaccurate data within the document in a self-reliant way that
involves generative AI, and ingestion of updated data via an API.” Examiner disagrees, finding that this argument cloaks an abstract improvement in technological language in the same manner as several previously advanced and refuted arguments (see previous Office Actions). The monitoring of real-time data to detect changes (or, as claimed, “a lack of accuracy”) in scheduled events, as well as the updating of an itinerary of scheduled events based on this monitoring, is an entirely abstract solution to an entirely abstract, business-related problem. Merely specifying that the scheduled events to be updated are stored in the form of a “digital document” does not somehow transform the problem being addressed here into a technological problem, nor does it transform the solution into a technological solution. Similarly, the AI-based generation of the scheduled events (e.g., a travel itinerary), particularly in the high-level manner in which it is claimed, likewise does not transform either the problem or solution into a technological one.
Regarding the remainder of Applicant’s arguments, all of which constitute entirely unexplained conclusory statements (and thus, improper arguments), Examiner makes the following observations. Merely stating that “Applicant's numerous claim limitations would clearly integrate an alleged abstract idea into a practical application” does not make it so. As explained in the previous Office Action, monopolization of a judicial exception, while a concern underlying the 101 subject matter eligibility analysis, is not a test for eligibility, and Applicant’s continued invocation of this language is no more persuasive now than it was previously. Similarly, Examiner also explained in a previous Office Action that a “real-world benefit” is irrelevant to the 101 subject matter eligibility analysis, but rather relates to the separate 101 utility requirement. As the claims were not previously and are not presently rejected under this separate requirement, Applicant’s continued reference of this standard remains irrelevant. Regarding Step 2B, Applicant fails to articulate how the scant additional elements present in the claims evidence an inventive concept when considered in combination, and Examiner does not see how this would be the case.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Applicant presents a brief set of improper 103 arguments, which (a) consider previously cited references in isolation rather than in combination as previously cited, (b) make unexplained conclusory statements that individual references fail to disclose large swathes of amended language, failing to consider or explain why Applicant might believe these references fail to disclose individual limitations thereof, and (c) makes a conclusory statement that three other references fail to cure the purported deficiencies of the Avital and Chambers references, again failing to consider these references in combination or support this assertion in any way. Such improper arguments need not be addressed here. Further, these arguments are moot in view of the updated 101 rejections below. See said 101 rejections for more information.
Claim Objections
Claim 4 is objected to because of the following informality: “…further configured to displaying…” should read “…further configured to display…” Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “…a device” in Claims 1, 9, and 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifically, this term is interpreted in view of at least Paragraphs 0050-0051 and Fig. 3A as published.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 7-13, and 15-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 9, and 17 contain variations on the following limitations: “detect a lack of accuracy within the plurality of scheduled events based on the real-time event data” and “modify the digital document within the device to correct the lack of accuracy based on an additional execution of the multi-modal Al model on the real-time event data.” The original disclosure fails to properly support these limitations. Specifically, the original disclosure does not disclose the detection of a lack of accuracy within the plurality of scheduled events, nor the re-running of the AI model and modification of the digital document in order to correct any such lack of accuracy. For example, the user feedback found in at least Paragraphs 0072-0074 and 0127 of Applicant’s asserted passages of support do not constitute a “lack of accuracy” or the detection thereof. As another example, Paragraph 0128 discloses the continuous monitoring of real-time travel-related information for, e.g., “crucial updates on flight statuses and any unexpected weather changes,” followed by the sending of a notification to the user to ensure they are “well-informed.” These “crucial updates” and “unexpected weather delays” likewise do not constitute “a lack of accuracy within the plurality of scheduled events,” nor does the consequent notification sent to the user constitute a modifying of the digital document nor a re-running of the AI model. As these features are not properly supported in the original disclosure, they constitute new matter. Claims 2-5, 7-8, 10-13, 15-16, and 18-22 are rejected due to their respective dependence upon Claims 1, 9, and 17.
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.
Claim 22 is 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.
Claim 22 contains the limitation “wherein the processor is further configured to send a message to a network-connected device to confirm an event from the plurality of scheduled events.” It is unclear as drafted whether “a network-connected device” of this limitation is intended to relate back to the “device” of Claim 1 (upon which Claim 22 depends). For the purposes of this examination, and in light of the original disclosure, these are interpreted as distinct devices.
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 requesting data at particular points in time (ie: at a point in time, and at a different point in time); display queries on a graphical user interface of a software application; receive responses to the queries and combine the responses with the queries to generate prompts; generate a document comprising a description of a plurality of scheduled events and images that represent the plurality of scheduled events based on execution of a model; store the document; detect a lack of accuracy within the plurality of scheduled events based on the real-time event data; and modify the document to correct the lack of accuracy based on an additional execution of the model on the real-time event data, 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 requesting data at particular points in time (ie: at a point in time, and at a different point in time); display queries on a graphical user interface of a software application; receive responses to the queries and combine the responses with the queries to generate prompts; generate a document comprising a description of a plurality of scheduled events and images that represent the plurality of scheduled events based on execution of a model; store the document; detect a lack of accuracy within the plurality of scheduled events based on the real-time event data; and modify the document to correct the lack of accuracy based on an additional execution of the model on the real-time event data, 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 scheduled events and images that represent the plurality of events based on execution of a model, and modify the document to correct the lack of accuracy based on an additional execution of the model on the real-time event data, 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 of a software application; a digital document; a multi-modal artificial intelligence (AI) model; a device; and query the web pages via the APIs for real-time event data. 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; a multi-modal artificial intelligence (AI) model; and a device 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) and query the web pages via the APIs for real-time event data amount 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 limitations found to recite insignificant extra-solution activity, upon reevaluation, are further determined to be well-understood, routine, and conventional as per MPEP 2106.05(d) because they represent 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 displaying the digital document via a profile page of the software application (an abstract idea in the form of a certain method of organizing human activity and a mental process) and modify the digital document displayed within the profile page (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 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 feedback received via the graphical user interface 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 an additionally modified 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 scheduled 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 1-5, 8-13, and 16-22 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 Kocis et al (PGPub 20100287073) (hereafter, “Kocis”).
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) at a point in time (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 and combine the responses with the queries 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);
store the digital document in a device (Column 2, lines 34-41; Column 11, lines 45-62; Column 12, lines 24-29; each trip is digitally represented using a programmatic object in memory and/or a record in a database table; mobile application may be programmed to communicate with database 120 to request trip information; data or the trip object may be stored in database, such as in trip object data; the trip object may be stored in association with the user who created the trip);
query the web pages via the APIs for real-time event data at a later point in time (Column 8, lines 14-38; Column 15, lines 7-31; Column 16, lines 21-30; Claim 1; 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; the search engine may be configured to interact with third-party travel product providers as discussed above for the data aggregator; in an embodiment, the search engine stores retrieved data in cache memory for further processing by the data aggregator; additionally, the search engine may be optimized to run parallel searches and/or continuous data retrieval; when a user retrieves a trip in the trip dashboard, booking application validates the trip; in an embodiment, validating the trip comprises verifying, for each trip element, element availability, element pricing, and element information; booking application may also verify trip integrity, such as connections between trip elements, trip schedule conflicts, trip element locations, and etc.; trip validation is performed at various trip planning checkpoints, e.g., when a trip is created, when a trip is retrieved, or when a trip is modified such as when trip elements are added, modified, removed, or when the trip element order is changed; additionally or alternatively, trip validation may be performed at particular intervals, e.g., every hour, every 8 hours, or every 12 hours; in an embodiment, trip elements are validated and booked simultaneously; validating the trip integrity and each of the one or more trip suggested elements, updating the trip integrity of each of the one or more suggested trip elements at different time intervals); and
detect a lack of accuracy within the plurality of scheduled events based on the real-time event data (Column 15, lines 7-31; Column 16, lines 21-30; Claim 1; when a user retrieves a trip in the trip dashboard, booking application validates the trip; in an embodiment, validating the trip comprises verifying, for each trip element, element availability, element pricing, and element information; booking application may also verify trip integrity, such as connections between trip elements, trip schedule conflicts, trip element locations, and etc.; trip validation is performed at various trip planning checkpoints, e.g., when a trip is created, when a trip is retrieved, or when a trip is modified such as when trip elements are added, modified, removed, or when the trip element order is changed; additionally or alternatively, trip validation may be performed at particular intervals, e.g., every hour, every 8 hours, or every 12 hours; in an embodiment, trip elements are validated and booked simultaneously, and the trip is not booked unless all trip elements can be booked; validating the trip integrity and each of the one or more trip suggested elements, updating the trip integrity of each of the one or more suggested trip elements at different time intervals, and causing the one or more suggested trip elements to be displayed in a graphical user interface of the user computing device).
Avital additionally discloses generate a digital document comprising a description of a plurality of scheduled events based on execution of the AI model; presented digital document may include both a plurality of scheduled events and images that represent the plurality of scheduled 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 Kocis does disclose modify the content of the output to correct the lack of accuracy based on an additional execution of the model on the additional data (¶ 0140, 0146; Fig. 3; in step S303, a data validity check may be conducted (either by the user or by a computer application) to ensure that the input data has imported properly, that no crucial input data has been omitted, or that some error has not occurred; after the revision in step S305, the new input data is then fed into the model; the model may be re-run for any reason, e.g., in order to compare the effects of various input data on the results of the optimization, or to otherwise compare various results; Steps S302-S308 may then be repeated until the user no longer wishes to run additional models (e.g., an acceptable set of optimized transportation decisions has been determined)). Avital additionally discloses wherein the output is the digital document; wherein the additional data is the real-time event data; wherein the digital document is stored within the device (Column 2, lines 34-41; Column 3, lines 1-6; Column 7, lines 13-29; Column 8, lines 14-38; Column 11, lines 38-62; Column 12, lines 24-29; Column 13, line 19 through Column 15, line 31; Column 16, lines 21-30; Figs. 2C-2H; Claim 1; 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; each trip is digitally represented using a programmatic object in memory and/or a record in a database table; when a user retrieves a trip in the trip dashboard, booking application validates the trip; in an embodiment, validating the trip comprises verifying, for each trip element, element availability, element pricing, and element information; booking application may also verify trip integrity, such as connections between trip elements, trip schedule conflicts, trip element locations, and etc.; trip validation is performed at various trip planning checkpoints, e.g., when a trip is created, when a trip is retrieved, or when a trip is modified such as when trip elements are added, modified, removed, or when the trip element order is changed; additionally or alternatively, trip validation may be performed at particular intervals, e.g., every hour, every 8 hours, or every 12 hours). Avital does not explicitly disclose but Chambers does disclose wherein the 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 further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the model re-running functionality of Kocis with the 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 Kocis 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 Kocis 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 Kocis 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 4, 12, and 20, Avital in view of Chambers and Kocis discloses the limitations of Claims 1, 9, and 17. Avital additionally discloses:
wherein the processor is further configured to displaying the digital document via a profile page of the software application (Column 12, lines 24-29; Column 14, line 63 through Column 15, line 5; data or the trip object may be stored in database, such as in trip object data; the trip object may be stored in association with the user who created the trip; a user may view a list of saved trips, retrieve a trip, or delete trips using the trip dashboard); and
modify the digital document displayed within the profile page (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 12, lines 24-29; Column 13, lines 7-18; Column 14, line 63 through Column 15, line 5; 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; data or the trip object may be stored in database, such as in trip object data; the trip object may be stored in association with the user who created the trip; a user may view a list of saved trips, retrieve a trip, or delete trips using the trip dashboard).
Regarding Claims 5 and 13, Avital in view of Chambers and Kocis 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 Kocis 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 Kocis 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 an additionally modified 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 Kocis 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 scheduled 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 user selecting particular trip element(s) from the list of recommended trip elements).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Avital in view of Chambers, Kocis, and DiMaria et al (PGPub 20230057877) (hereafter, “DiMaria”).
Regarding Claims 7 and 15, Avital in view of Chambers and Kocis discloses the limitations of Claims 1 and 9. Avital does not explicitly disclose but DiMaria does disclose wherein the processor is further configured to execute the Al model on the feedback to retrain the Al model (¶ 0134-0138, 0174; as feedback data is received, it may be used to retrain the machine learning algorithm). 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)). Avital additionally discloses wherein the feedback is received via the graphical user interface (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).
The rationale to combine Avital, Chambers, and Kocis remains the same as for Claim 1. One of ordinary skill in the art would further have been motivated to include the feedback-based model re-training functionality of DiMaria with the travel product recommendation system of Avital, Chambers, and Kocis to provide more accurate model predictions over time (see at least Paragraphs 0134-0135 of DiMaria).
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20240037459 – “Database Operations and Analysis for Virtual Interlining of Travel Routes,” Pourmehrab et al, disclosing a system for ingesting offered travel services from a plurality of third-party sources, and generating and offering a suggested travel itinerary in response to a search request
PGPub 20170031931 – “Disambiguating Search Queries,” Linda et al, disclosing a system for determining travel-related offerings to suggest to a requesting user, in some embodiments utilizing machine learning algorithms
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.
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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