Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 3/10/2026. Claims 1-33 are currently pending and have been examined. Claims 1, 6, 10, 12, 17, 21, 23, 28, 32 have been amended. The claim objections have been overcome by amendment.
Claim Rejection - 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 1-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-11 are directed to a process, claims 12-22 are directed to a machine, and claims 23-33 directed to an article of manufacture. Therefore, claims 1-33 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 12, and 23 at least the following limitations that are believed to recite an abstract idea:
receiving a first chat message from a chat means of a reservation system;
determining from the first chat message, whether the first chat message specifies an intent for a recommendation of a travel item by programmatically classifying the first chat message to output the intent, wherein the determination of the intent for a recommendation of a travel item includes a determination whether enough contextual information is provided based on a pre-determined threshold;
in response to determining that the first chat message specifies the intent, generating a first plurality of travel items and a second chat message comprising one or more descriptors of the intent, outputting the first plurality of travel items and the second chat message in the chat means, and including, in each travel item of the first plurality of travel items, a means to be selected to initiate a reservation dialog based on the travel item;
in response to determining that the first chat message does not specify the intent,
When a consent has been specified, accessing one or more stored data items specific to the user to obtain contextual information if prior user consent has been provided,
When a consent has not been specified, outputting in the chat means a third chat message comprising a prompt for contextual information,
receiving, in the chat means, a fourth chat message specifying the contextual information,
executing an inference stage of one or more procedures over the contextual information to output one or more named entities,
deriving the intent from the first chat message and the one or more named entities,
generating the intent, and the one or more named entities, a second plurality of travel items and a fifth chat message comprising one or more descriptors of the intent,
outputting in the chat means the second plurality of travel items and the fifth chat message, and including, in each of the second travel items, a second means to be selected to initiate the reservation dialog based on one or more of the second travel items.
The above limitations recite the concept of personalized conversation and recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-33 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
The method being computer implemented
A server computer
An interface
An application executing on a mobile computing device
graphical user interface (GUI) widgets that are programmed
storage being digital
one or more trained machine-learning models
A system comprising: one or more non-transitory computer-readable storage media including instructions; one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to perform steps
A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a system, cause the one or more processors to perform steps
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 4-5, 15-17, and 26-28 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 2-3, 6-11, 13-14, 18-22, 24-25, and 29-33, these claims are similar to the independent claims except that they recite the further additional elements of additional trained machine-learning/classification models, additional interfaces, a database, language models or large language models. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
The method being computer implemented
A server computer
An interface
An application executing on a mobile computing device
graphical user interface (GUI) widgets that are programmed
storage being digital
one or more trained machine-learning models
A system comprising: one or more non-transitory computer-readable storage media including instructions; one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to perform steps
A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a system, cause the one or more processors to perform steps
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Interpretation
With reference to subsection II of MPEP 2111.04, it is noted that “the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP 2143.03 further notes that “language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation,” with a contingent limitation “rais[ing] a question as to its limiting effect.”
In the pending claims, such contingent limitations include the steps of:
“in response to determining that the first chat message specifies the intent, generating … outputting …, and including, …” or “in response to determining that the first chat message does not specify the intent, when a consent has been specified, accessing…if prior user consent has been provided… when a consent has not been specified, outputting …, executing …, deriving …, generating, …, outputting …, and including, …” in Claim 1; and those limitations that depend thereon.
In the interest of a compact prosecution, art has nonetheless been applied to the contingent limitations of the method claims.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejection – 35 USC § 103
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 non-
obviousness.
Claims 1-3, 7-14, 18-25, and 29-33 are rejected under 35 U.S.C. 103 as being unpatentable over Vadodaria (US 20170068551 A1), in view of Devaux et al (US 20240370691 A1) hereinafter Devaux, and further in view of Layton et al (US 20200380020 A1), hereinafter Layton.
Regarding Claim 1, Vadodaria discloses a computer-implemented method, comprising:
receiving a first chat message at a server computer from a chat interface of a reservation application executing on a mobile computing device (Vadodaria: “The Conversation Management can start a new session as soon as it senses a command input from the user e.g. if the user says “I want you to search flights for me”.” [0099] – “The User Input is sent to the NLP Server and if the user has provided a Travel Command eg. “I want a flight to New York”, the NLP Server recognizes the TRAVEL Action.” [0597] – See chat interface Figure 12);
determining, by the server computer, from the first chat message, whether the first chat message specifies an intent for a recommendation of a travel item by programmatically classifying the first chat message to output the intent, wherein the determination of the intent for a recommendation of a travel item includes a determination whether enough contextual information is provided based on a pre-determined threshold (Vadodaria: “if the user says “I want you to search flights for me”. This input by itself is not complete. …This will trigger a user dialog session to be initiated by the Conversation Management Module … However if the user specifies a command with all the required parameters in 1 line. “search flights from LAX to JFK tomorrow at 8 pm”, then the dialog management module doesn't maintain a session and considers it a spot request with all the sufficient parameters to perform the requested operation.” [0099-0100] – See also Figure 26 as supported by [0598] and [0602], where the system can determine either (I) that the input is not abstract (NO), and that the search criteria is sufficient (YES), and can perform flight search operations without needing to “prompt for missing travel search criteria.;” or can (II) determine that the input is abstract and “prompt for specific input”.);
in response to determining that the first chat message specifies the intent,
generating a first plurality of travel items [search results] and a second chat message comprising one or more descriptors of the intent (Vadodaria: “the Flight Search Operation is performed …Once the Flight Search Results are obtained, the INTELLIGENT ASSISTANT Optimizes the results by Sorting them … Once the INTELLIGENT ASSISTANT has located the most optimal Flight Result … the INTELLIGENT ASSISTANT scrolls to the result (if needed) and then zooms in on it by displaying all the relevant details of the Flight Result and also summarizes the important details using the Natural Language Generator and then conveys the user this summarized information through text” [0604-0607] – “response that needs to be sent back to the user …I found 20 flights from LAX to JFK tomorrow at around 8 pm” [0109]),
outputting the first plurality of travel items and the second chat message in the chat interface (Vadodaria: “Once the Flight Search Results are obtained, the INTELLIGENT ASSISTANT Optimizes the results by Sorting them … … the INTELLIGENT ASSISTANT scrolls to the result (if needed) and then zooms in on it by displaying all the relevant details of the Flight Result and also summarizes the important details using the Natural Language Generator and then conveys the user this summarized information through text” [0606-0607] – “Once the Flight Search Results are obtained, they are displayed to the user as shown in the figure below: See Fig. 33.” [0636] – “response that needs to be sent back to the user …I found 20 flights from LAX to JFK tomorrow at around 8 pm” [0109] – See Figures 33-35),
in response to determining that the first chat message does not specify the intent,
outputting in the chat interface a third chat message comprising a prompt for contextual information (Vadodaria: “The User Input is sent to the NLP Server and if the user has provided a Travel Command eg. “I want a flight to New York”, the NLP Server recognizes the TRAVEL Action. The Abstraction Elimination Module then checks if the user has provided abstract input and if so, the INTELLIGENT ASSISTANT prompts the user for a more specific input. … for the Source Location the user is expected to provide at least the name of the City but if the user instead provides a Continent the INTELLIGENT ASSISTANT will render a confused Facial Expression and prompt the user to be more specific using a Natural Language text like, “You have specified a continent. I need the name of the city from which you will be traveling” [0597-0598]),
receiving, in the chat interface, a fourth chat message specifying the contextual information (Vadodaria: “a Natural Language text “North America is a continent. I need a specific city or airport from where you'll be traveling from.”” [0619] – “The user then provides the city name as Los Angeles” [0624]),
executing an inference over the contextual information to output one or more named entities (Vadodaria: “Named Entity Recognition (NER) is to identify if a Travel related proper nouns (e.g. … City name, … exists in the user input sentence and identify the Concept/Entity Type of Proper Noun (e.g. … CITY, …, assign the correct Part of Speech Type (POSType) and find out additional information regarding the “Named Entity” so it could be used for further processing. This process uses a cluster based NER algorithm that segregates and unifies the different persons, locations, organizations and other proper nouns mentioned in the user input.” [0138] – See [0266-0300], which describe a specific process for recognizing named entities, such as “Los Angeles”),
deriving the intent from the first chat message and the one or more named entities (Vadodaria: “The INTELLIGENT ASSISTANT prompts the user in the form of a dialog (in Natural Language) the missing search criteria (if any). … Once all the necessary Travel Search Criteria (Start Location, Destination Location and Departure Date/Time) are obtained from the user, the user's input is further scanned for additional parameters” [0602-0603] – See Also Figures 5 & 13, where context/intent is determined cumulatively.),
generating, using the server computer, the intent, and the one or more named entities, a second plurality of travel items and a fifth chat message comprising one or more descriptors of the intent (Vadodaria: “the Flight Search Operation is performed …Once the Flight Search Results are obtained, the INTELLIGENT ASSISTANT Optimizes the results by Sorting them … Once the INTELLIGENT ASSISTANT has located the most optimal Flight Result … the INTELLIGENT ASSISTANT scrolls to the result (if needed) and then zooms in on it by displaying all the relevant details of the Flight Result and also summarizes the important details using the Natural Language Generator and then conveys the user this summarized information through text” [0604-0607] – “response that needs to be sent back to the user …I found 20 flights from LAX to JFK tomorrow at around 8 pm” [0109]),
outputting in the chat interface the second plurality of travel items and the fifth chat message (Vadodaria: “Once the Flight Search Results are obtained, the INTELLIGENT ASSISTANT Optimizes the results by Sorting them … … the INTELLIGENT ASSISTANT scrolls to the result (if needed) and then zooms in on it by displaying all the relevant details of the Flight Result and also summarizes the important details using the Natural Language Generator and then conveys the user this summarized information through text” [0606-0607] – “Once the Flight Search Results are obtained, they are displayed to the user as shown in the figure below: See Fig. 33.” [0636] – “response that needs to be sent back to the user …I found 20 flights from LAX to JFK tomorrow at around 8 pm” [0109] – See Figures 33-35).
While Vadodaria teaches interaction with displayed travel items [0640] and executing an inference over the contextual information to output one or more named entities [0138], it does not specifically teach including, in each travel item of the first plurality of travel items, a graphical user interface (GUI) widget that is programmed when selected to initiate a reservation dialog based on the travel item; executing an inference stage of one or more trained machine-learning models over the contextual information to output one or more named entities; and including, in each of the second travel items, a second GUI widget that is programmed when selected to initiate the reservation dialog based on one or more of the second travel items; and when a consent has been specified, accessing one or more digitally stored data items specific to the user to obtain contextual information if prior user consent has been provided; and when a consent has not been specified, outputting in the chat interface a third chat message.
However, Devaux teaches a conversational search engine (Devaux: [0006)], including:
including, in each travel item of the first plurality of travel items, a graphical user interface (GUI) widget that is programmed when selected to initiate a reservation dialog based on the travel item/including, in each of the second travel items, a second GUI widget that is programmed when selected to initiate the reservation dialog based on one or more of the second travel items (Devaux: “receiving a response to the structured travel query … a sub-message 504-4-SR, which includes a flight card 904 assembled by travel management engine 122 based on airline data … Flight card 904 includes a single flight option from Nice (NCE) to Paris (CDG) departing at 930 AM on April 12 and arriving at 1105 AM on April 12. Flight card 904 is in a format that is readable to a user 124, and also includes interactive buttons like “Book this Flight” … Note that sub-message 504-4-SR is simplified, in that multiple flight options and/or flight cards may be generated ” [0109] – See Figure 9.); and
executing an inference stage of one or more trained machine-learning models [large language model (LLM)] over the contextual information to output one or more named entities (Devaux: “machine learning feedback can be used to further improve the context shifts and/or train the LLM Engine 120 in providing its dialogue with the user ” [0185] – “Message 504-1 is a salient example, which includes the sentence “Hey, I need to book a Flight to Paris”. Collaboration platform 104 a and LLM engine 120 a can thus derive the origin parameter “Paris” from message 504-1.” [0122]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Vadodaria would continue to teach outputting the first plurality of travel items and the second chat message in the chat interface; executing an inference over the contextual information to output one or more named entities; and outputting in the chat interface the second plurality of travel items and the fifth chat message, except that now it would also teach including, in each travel item of the first plurality of travel items, a graphical user interface (GUI) widget that is programmed when selected to initiate a reservation dialog based on the travel item; executing an inference stage of one or more trained machine-learning models over the contextual information to output one or more named entities; and including, in each of the second travel items, a second GUI widget that is programmed when selected to initiate the reservation dialog based on one or more of the second travel items, according to the teachings of Devaux. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved search efficiency (Devaux: [0174]).
While Vadodaria/Devaux teach in response to determining that the first chat message does not specify the intent, outputting in the chat interface a third chat message comprising a prompt for contextual information (Vadodaria: [0597-0598]), they do not specifically teach, when a consent has been specified, accessing one or more digitally stored data items specific to the user to obtain contextual information if prior user consent has been provided; and when a consent has not been specified, outputting in the chat interface a third chat message.
However, Layton teaches a context-driven chat bot [Abstract], including:
when a consent has been specified, accessing one or more digitally stored data items specific to the user to obtain contextual information if prior user consent has been provided (Layton: “in response to receiving permission from a user 10 for data collection, registering users with the context recognition system 100, which provides the context of a user query 11 to assist the chat bot 50 in answering the user query without requiring that the chat bot 50 ask the user 10 a number of qualifying questions to determine the context of the query 11 through requests to the user directly.” [0030] - “receiving a query 11 from the user 10 to the chat bot 50… in which the query 11 does not include the safety word, the process flow may extend to bock 7…. Block 7 is the application of the user profile 109 in determining the context of the query 11. The user profile 109 may be applied to the query 11 by the user profile applicator ” [0049-0051]); and
when a consent has not been specified, outputting in the chat interface a third chat message (Layton: “The user can revoke the permission to the system for creating and/or using a user profile at any time.” [0065] – “ collect, store, or employ personal information provided by, or obtained from, individuals …the collection, storage, and use of such information may be subject to consent of the individual to such activity, for example, through “opt-in” or “opt-out” processes” [0031] – “the method may include defining a “sleep” word. The sleep word may be employed by the user 10 to turn the context recognition system 100 to an OFF setting. The sleep word can be entered to a chat bot 50 by typing text” [0038] - “in which the query 11 does include a safe word, the method may bypass the steps of applying the user profile 109 to the query 11.”[0054] - “Without the present context recognition system 100, in order for the chat bot 50 to determine the context of the query 11, the chat bot 50 would ask questions back to the user 10, such as “are you interested in SAP databases, Microsoft databases, DB2 databases or something else?”.” [0034] – “At block 8, the chat bot 50 may provide an answer 12 to the query 11.” [0055] - See Figure 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Vadodaria/Devaux would continue to teach in response to determining that the first chat message does not specify the intent, outputting in the chat interface a third chat message comprising a prompt for contextual information, except that now it would also teach when a consent has been specified, accessing one or more digitally stored data items specific to the user to obtain contextual information if prior user consent has been provided; and when a consent has not been specified, outputting in the chat interface a third chat message, according to the teachings of Layton. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to interact with a chat bot by providing additional context (Layton: [0020]).
Regarding Claim 2, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, wherein programmatically classifying the first chat message to output an intent comprises:
executing a first inference stage of one or more first trained machine-learning models over the first chat message to output one or more responsive chat messages that respond to the first chat message (Devaux: “LLM engine 120 can analyze the message 504-1 from block 404 and, via an iterative conversation between LLM engine 120 and user 124-1 (per block 416 and block 420), LLM engine 120 can direct questions to user 124-1 and receive further input from user 124-1 until a fully structured travel query can be generated.” [0097]);
executing a second inference stage of one or more second trained machine-learning models over the first chat message to output the intent (Devaux: “responds to the message 504-2 with the necessary additional information in the form of message 504-3 with the natural language text “I would like to travel the 12 of April from Nice”. At this point, based on the configuration from Table 228-7, LLM Engine 120 can determine at block 420 that there is sufficient information to generate a structured travel query ” [0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vadodaria/Layton with Devaux for the reasons identified above with respect to claim 1.
Regarding Claim 3, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, further comprising: displaying a home screen interface of the reservation application; receiving, at the server computer, and the reservation application, a request to generate the travel items; displaying the chat interface in response to the request (Vadodaria: “The Conversation Management can start a new session as soon as it senses a command input from the user e.g. if the user says “I want you to search flights for me”.” [0099] – “This will trigger a user dialog session to be initiated by the Conversation Management Module which include a step by step procedure to ask questions for the unspecified parameters. ” [0100])
Regarding Claim 7, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, wherein determining whether the first chat message specifies the intent for the recommendation of a travel item further comprises: inputting the first chat message into a classification model trained to identify an appropriateness of the first chat message; causing, by the server computer, the chat interface to display a sixth chat message, wherein the sixth chat message comprises an indication of the appropriateness of the first chat message (Vadodaria: “The User Input is sent to the NLP Server and if the user has provided a Travel Command eg. “I want a flight to New York”, the NLP Server recognizes the TRAVEL Action. … prompt the user to be more specific using a Natural Language text” [0597-0598] – See Figure 26, “Action=Travel?”).
Regarding Claim 8, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, wherein generating the first plurality of travel items or the second plurality of travel items comprises retrieving the first plurality of travel items or the second plurality of travel items from a database utilizing one or more recommendation systems (Devaux: “With assistance of LLM engine 120 a, such intent can be resolved into terms that are searchable within open search domain 1712, whether or not those terms are canonical and directly searchable in master data domain 1704.” [0147] – “Different search domains can include any databases that are available over the Internet or other network, including search domains that may be searched using open search interfaces ” [0120] – “Travel management engine 122 provides a central gateway for collaboration platform 104 to interact with travel actor engines 112, receiving structured search requests from collaboration platform 104 and conducting searches across travel actor engines 112, and collecting structured search results” [0061]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vadodaria/Layton with Devaux for the reasons identified above with respect to claim 1.
Regarding Claim 9, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 8, wherein retrieving the first plurality of travel items or the second plurality of travel items further comprises: generating a retrieval request based on the intent from the first chat message and the one or more named entities; providing the retrieval request to the one or more recommendation systems; retrieving, by the one or more recommendation systems, the first plurality of travel items or the second plurality of travel items from the database based on the retrieval request (Devaux: “n unstructured natural language travel query is received from a device 116 at collaborator platform 104, the platform 104 can pass that unstructured query to LLM engine 120, which in turn can generate a structured query in reply that can then be used to formally search travel actor engines 112.” [0085] – “Different search domains can include any databases that are available over the Internet or other network, including search domains that may be searched using open search interfaces ” [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vadodaria /Layton with Devaux for the reasons identified above with respect to claim 1.
Regarding Claim 10, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 8, wherein the one or more recommendation systems comprise one or more trained machine-learning models (Devaux: “he platform 104 can pass that unstructured query to LLM engine 120, which in turn can generate a structured query in reply that can then be used to formally search travel actor engines 112” [0085]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vadodaria/Layton with Devaux for the reasons identified above with respect to claim 1.
Regarding Claim 11, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, wherein the one or more trained machine-learning models comprise one or more language models (LMs) or one or more large language models (LLMs) (Devaux: “machine learning feedback can be used to further improve the context shifts and/or train the LLM Engine 120 in providing its dialogue with the user ” [0185] – “Message 504-1 is a salient example, which includes the sentence “Hey, I need to book a Flight to Paris”. Collaboration platform 104 a and LLM engine 120 a can thus derive the origin parameter “Paris” from message 504-1.” [0122] – “a large language model (LLM) engine 120” [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vadodaria /Layton with Devaux for the reasons identified above with respect to claim 1.
Regarding Claims 12-14 and 18-22, the limitations of claims 12-14 and 18-22 are closely parallel to the limitations of claims 1-3 and 7-11, with the additional limitation of a reservation management system associated with a reservation service, the reservation management system comprising: one or more non-transitory computer-readable storage media including instructions; one or more processors coupled to the storage media, the one or more processors configured to execute the instructions (Vadodaria: [0013], [0018]), and are rejected on the same basis.
Regarding Claims 23-25 and 29-33, the limitations of claims 23-25 and 29-33 are closely parallel to the limitations of claims 1-3 and 7-11, with the additional limitation of a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a reservation management system associated with a reservation service, cause the one or more processors to perform steps (Vadodaria: [0013], [0018]), and are rejected on the same basis.
Claims 4-6, 15-17, and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Vadodaria/Devaux/Layton, and further in view of Popp (US 20240338534 A1).
Regarding Claim 4, Vadodaria/Devaux/Layton teach the computer-implemented method of Claim 1, further comprising: receiving, by the server computer, and from the reservation application, a selection of at least one of the second plurality of travel items (Devaux: “receiving a response to the structured travel query … a sub-message 504-4-SR, which includes a flight card 904 assembled by travel management engine 122 based on airline data … Flight card 904 includes a single flight option from Nice (NCE) to Paris (CDG) departing at 930 AM on April 12 and arriving at 1105 AM on April 12. Flight card 904 is in a format that is readable to a user 124, and also includes interactive buttons like “Book this Flight” … Note that sub-message 504-4-SR is simplified, in that multiple flight options and/or flight cards may be generated ” [0109] – See Figure 9.);
but do not specifically teach, in response to receiving the selection, causing the chat interface to display a third plurality of travel items associated with the second plurality of travel items.
However, Popp teaches a travel-based search system (Popp: Abstract), including, in response to receiving the selection, causing the chat interface to display a third plurality of travel items [search results] associated with the second plurality of travel items [destinations] (Popp: “The query parameter manager 122 may then request the dialog questions data store 124 or dialog manager 128 to provide a prompt for another parameter (“Would you prefer the Caribbean or Polynesia”). In this case, user data such as profile data or historical interaction data may be used to determine a typical destination for this user (Caribbean or Polynesia), or potential destinations associated with a named entity in the initial query 302 (“in a sunny place”) may be selected to be included in the prompt. The user responds with information for the parameter (“Polynesia”).” [0040] – “the travel search application may generate search results for presentation to the user. ” [0018] – “as the session continues through subsequent dialog turns and additional parameters are determined, the results may be filtered or updated accordingly” [0048] – see Figures 3-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Vadodaria/Devaux/Layton would continue to teach receiving, by the server computer, and from the reservation application, a selection of at least one of the second plurality of travel items, except that now it would also teach in response to receiving the selection, causing the chat interface to display a third plurality of travel items associated with the second plurality of travel items, according to the teachings of Popp. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to determine the user’s intent (Popp: [0012]).
Regarding Claim 5, Vadodaria/Devaux/Layton/Popp teach the computer-implemented method of Claim 4,
wherein the second plurality of travel items comprises a plurality of recommendations of one or more travel destinations (Popp: “The query parameter manager 122 may then request the dialog questions data store 124 or dialog manager 128 to provide a prompt for another parameter (“Would you prefer the Caribbean or Polynesia”). In this case, user data such as profile data or historical interaction data may be used to determine a typical destination for this user (Caribbean or Polynesia), or potential destinations associated with a named entity in the initial query 302 (“in a sunny place”) may be selected to be included in the prompt. The user responds with information for the parameter (“Polynesia”).” [0040]), and
wherein the third plurality of travel items comprises a plurality of recommendations of a lodging, a transport, or an attraction (Popp: “The queries to be handled by the dialog-based pipeline may relate to travel activities and reservations, such as hotels, airfare, rental cars, or packages of such activities.” [0016] – See Figures 3-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Popp with Vadodaria/Devaux/Layton for the reasons identified above with respect to claim 4.
Regarding Claim 6, Vadodaria/Devaux/Layton/Popp teach the computer-implemented method of Claim 4, further comprising receiving, by the server computer, and from the reservation application, a second request corresponding to a selection of at least one of the third plurality of travel items; in response to receiving the second request, causing, by the by the server computer, the reservation application to display the at least one of the third plurality of travel items, the at least one of the third plurality of travel items being displayed to prompt a reservation (Popp: “As the session continues through subsequent dialog turns and additional parameters are determined, the results may be filtered or updated accordingly.” [0048] – “The queries to be handled by the dialog-based pipeline may relate to travel activities and reservations, such as hotels, airfare, rental cars, or packages of such activities.” [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Popp with Vadodaria/Devaux/Layton for the reasons identified above with respect to claim 4.
Regarding Claims 15-17, the limitations of claims 15-17 are closely parallel to the limitations of claims 4-6 and are rejected on the same basis.
Regarding Claims 26-28, the limitations of claims 26-28 are closely parallel to the limitations of claims 4-6 and are rejected on the same basis.
Response to Arguments
Applicant’s arguments filed 3/10/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that the dependent claims have not been fully considered, and argues that they “recite additional technical elements that integrate any judicial exception…to provide a practical application of computing.”
Examiner respectfully disagrees. With reference to the rejection above, the dependent claims have been fully evaluated under 101 and determined, similar to the independent claims, to invoke computer-related additional elements at a high level of generality as mere instructions to apply the identified abstract idea to a technical field [MPEP 2106.05(f)]. With reference to the two claims specifically noted by Applicant, claims 7 & 9, these claims recite abstract steps which fall within the identified abstract idea, except for additional elements of a trained model and a database, recited at a high level of generality, which provide only a general linking of the abstract steps, e.g. “imputing a first chat message into a [means] to identify an appropriateness of the first chat message,” and “retrieving, by the one or more recommendation systems, the first plurality of travel items or the second plurality of travel items,” from a storage, “based on the retrieval request,” to computer technology, amounting to mere instructions to use these generic computer components to perform the abstract idea’s identifying & storing.
Applicant further argues with respect to Step 2A Prong 1 that the independent claims “are so particularized and solution-specific that they cannot monopolize any particular abstract idea.”
Examiner respectfully disagrees. With reference to the rejection above, the claims recite steps which amount to an abstract idea for personalized conversations and recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions.
Applicant claims that MPEP 2106 states that “an Office Action is apparently allowed not to specifically identify any particular abstract idea, …but to merely paraphrase parts of the claim and identify categories or groups” and argues “that the case law does not authorize that approach.” Applicant does not specifically identify the alleged language from the MPEP or the case law alluded to.
Examiner notes that MPEP 2106 does not recite “paraphras[ing] parts of the claim,” as argued, and makes reference to the rejection above, which, consistent with the MPEP and relevant case law, specifically identifies the abstract idea present in the pending claims.
Applicant argues with respect to Step 2A Prong 2 that the present 101 rejections “are unsustainable” based on the pending claims and “the Office’s significant changes in subject matter eligibility policy, particularly the decision in Ex Parte Desjardins.” In particular, Applicant argues that the “by combining machine learning models that can intelligently process natural language and identify a user’s intent and named entities with reservation management systems, the number of computer-intensive database calls is reduced, which, in turn, improves the overall CPU performance and memory storage capacity and decreases the data latency of the reservation management system.” Applicant argues that, similar to Desjardins, the present application presents “ample information to determine that the problem outlined in the Background is solved by the new computer processing described in the disclosure,” specifically “that limiting the number of database queries by collecting sufficient contextual information before generating travel items would reduce unnecessary use of computing resources and data latency.”
Examiner disagrees. The alleged improvements to reduce data-search activity by identifying user intent, and the alleged efficiencies therefrom, are part of the abstract idea itself rather than the computer-related additional elements, such that the alleged improvements are at best a business improvement stemming solely from the abstract idea. Whereas Desjardins recites a specific technical problem in the Specification and claims a specific technical solution to that problem, the pending application merely invokes generic computing components as instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], creating only a general linking to computer technology. In other words, the abstract idea recites the method for “collecting sufficient contextual information before generating travel items,” with the additional elements invoked as a tool to generally link this abstract problem & solution to high-generality computing components.
Applicant further argues with respect to Step 2B that “the same claim elements and limitations summarized above amount to significantly more than any judicial exception.” Applicant contends that the rejection “does not identify any particular judicial exception,” and argues that “the technical elements in the claims effectively tie computing, generative artificial intelligence, and reservation management systems into a computer-implemented process, thereby providing significant more than “whatever the Office considers abstract.” Applicant argues that “all the additional elements not paraphrased on the Office Action’s page 6 confer a technological improvement…because the Applicant is the first to discover that trained machine learning systems applied as recited in the claim…can provide significant reduction in the number of operations required by the server without compromising quality of the results.” Applicant argues that the claims provide “specific implementation of machine learning systems that the inventors have discovered to yield beneficial results when used in the rest of the claimed process,” and that “the particular features of the claimed GUI widget …are also technical and specific to the solution.”
Examiner disagrees. With reference to the rejection above, the Office Action explicitly recites the abstract idea found in the claims, which provides a concept of personalized conversation and recommendations. The limitations of this abstract idea, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. The rejection does not paraphrase this abstract idea or the computer-related additional elements, instead specifically listing the additional elements found in the claim. These elements, identified in the Rejection above, do not integrate the abstract idea into significantly more, but are invoked at a high level of generality as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. Similar to the discussion above with respect to Step 2A Prong 2, the alleged “significant reduction in the number of operations required… without compromising quality of the results” is rooted solely in the abstract idea, with the additional elements providing only a general linking to computer technology. For instance, the argued trained machine-learning models are recited at a high level of generality, and amount to mere instructions to take the abstract step of determining named entities from context data and apply it to a high-generality, black-box computer component.
Claim Rejection – 35 USC §103
Applicant argues with respect to Claim 1 that “Vadodaria and Devaux, separately or combined, fail to disclose, teach, or suggest in response to determining that the natural language chat message from the user does not specify the intent, when a consent has been specified by the user, accessing one or more digitally stored data items specific to the user to obtain contextual information and when a consent has not been specified by the user, outputting a chat message prompt for the user to provide contextual information, in the manner that claim 1 recites.”
Examiner partially disagrees. Vadodaria teaches receiving a first chat message from a user, such as “I want you to search flights for me” [0099] and analyzing it user natural language processing [0597]. The system determines if the input has “all the sufficient parameters” to answer the request, and, if not, initiates a dialog session [0099-0100]. In this session, initiated upon determination that the system requires further context to answer the request, the system provides additional messages to the user requesting the missing context, [0597-0598] and receives user response to fill in the missing context data, such as a departure city for a flight [0619, 0624]. The system uses natural language processing to detect named entities in the user response [0138], and, having the needed context, responds to the request [0604-0605]. However, the rejection above turns to newly-relied-upon reference Layton to teach that, when a user has provided permission, the chat bot system can access collected user data to provide the context of a user query without requiring additional questions to be asked of the user [0030], such as by determining needed data from a user profile in response to a user not entering a term to reject consent for data access [0049-0051]. When the user revokes permission or opts out [0065, 0031] of data collection or uses a term to decline/reject it for a particular query, the system does not access the user profile data for context [0038]. Instead, the system bypasses the applying of the context from the profile [0054], and without the context from the profile, asks questions back to the user [0034]. Thus, the combination of Vadodaria and Layton teach the argued limitation, with Vadodaria teaching that a user can be questioned to obtain necessary context, and Layton teaching that, if permitted, the system can retrieve context from a profile instead of asking questions, and that such context can be declined, resulting in the questions needing to be asked.
Applicant further argues that “Each of independent claims 12 and 23 recites similar elements as claim 1 and is therefore also in condition for allowance for the same reasons as claim 1,” and that “each of claims 2-11, 13-22, and 24-33 depends, directly or indirectly, from one of claims 1, 12, or 23, and therefore includes, by dependency, the allowable subject matter of claims 1, 12, or 23 that distinguishes the claims from the cited prior art of record.”
Examiner disagrees for the reasons addressed in the Rejection and Response above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chew et al (US 10929485 B1) teaches a travel reservation bot that engages the user in natural language conversation using a trained ML model, including a selectable option to initiate the bot.
Reference U (NPL – see attached) discusses machine-learning chatbots for travel recommendations, including multi-stage recommendations first suggesting locations and then suggesting activities at those locations.
THIS ACTION IS MADE FINAL. 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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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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