DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-2, 5, 7-9, 12, 14-16, 19, 21-30 are currently pending and have been addressed below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/26/2025 has been entered.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 10/01/2025 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Response to Arguments
Applicant's arguments filed 09/26/2025 (related to the 101 and 103 Rejections) have been fully considered but are moot in view of new grounds of rejection. Applicant's amendments necessitated the new ground(s) of rejection presented in this Office action. Rejection based on a newly cited reference(s) follows.
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 1-2, 5, 7-9, 12, 14-16, 19, 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method, comprising: communicating a set of proposal recommendations for completion of a project, wherein the set of proposal recommendations is communicated between a member and a representative, and wherein the set of proposal recommendations is displayed; processing a set of messages exchanged to identify a response to the set of proposal recommendations; processing a set of task parameters corresponding to the response to generate one or more proposal tasks, wherein the proposal task generation algorithm is trained to identify a set of correlations from a dataset that includes historical task parameters and previously performed proposal tasks, and wherein the one or more proposal tasks are generated according to the set of correlations; processing the one or more proposal tasks to identify a set of resources for performance of the one or more proposal tasks performing the one or more proposal tasks according to the set of resources, wherein when the one or more proposal tasks are performed, the project is completed according to the response; continuously processing new messages exchanged to obtain feedback corresponding to the performance of the one or more proposal tasks; retraining to automatically generate and perform new tasks without representative interaction, wherein retrained based on the feedback; and communicating the new tasks, wherein the new tasks are communicated automatically without the representative interaction. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, the claim as a whole is directed to communications between a member and a representative, wherein the representative provides one or more propose tasks based on historical responses/feedback of the member and current responses/feedback of the member (see MPEP 2106.04(a)(2), social activity such passing a note to a person who is in the middle of a meeting or conversation). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a computer; a communications session; an application executing on a member computing device; a natural language processing; a proposal task generation algorithm; and a task coordination machine learning algorithm.
The computer is merely used to process the set of messages in real-time to automatically identify a response to a set of proposals (Paragraph 0003). The communication session is merely used to receive in real-time a set of messages between a member and a representative (Paragraph 0003). The application executing on a member computing device is merely used to display the proposals and/or the ranked proposals (Paragraph 0185). The natural language processing is merely used to evaluate received messages or other communications from the member to identify any information that may be used to supplement the project and/or the member profile (Paragraph 0141). The proposal task generation algorithm and the task coordination machine learning algorithm are merely used to generate proposals, recommend proposals, coordinate tasks, and/or perform tasks on behalf of a member 518 over time, the task recommendation system 512 may continuously and automatically update the member profile according to feedback related to the generation of proposals, recommendation of proposals, coordination of tasks, and/or performance of tasks (by, for example, the representative 506, the task recommendation system 512, and/or the third-party services 516 or other services/entities affiliated with the task facilitation service) (Paragraph 0151). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “computer,” “communication session,” “application executing on a member computing device,” “natural language processing,” “proposal task generation algorithm,” and “task coordination machine learning algorithm” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the communication session is considered “field of use” since it’s just used to transmit and receive information, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of communicating a set of proposal recommendations and obtaining feedback from the user. The specification shows that the computer is merely used to process the set of messages in real-time to automatically identify a response to a set of proposals (Paragraph 0003). The communication session is merely used to receive in real-time a set of messages between a member and a representative (Paragraph 0003). The application executing on a member computing device is merely used to display the proposals and/or the ranked proposals (Paragraph 0185). The natural language processing is merely used to evaluate received messages or other communications from the member to identify any information that may be used to supplement the project and/or the member profile (Paragraph 0141). The proposal task generation algorithm and the task coordination machine learning algorithm are merely used to generate proposals, recommend proposals, coordinate tasks, and/or perform tasks on behalf of a member 518 over time, the task recommendation system 512 may continuously and automatically update the member profile according to feedback related to the generation of proposals, recommendation of proposals, coordination of tasks, and/or performance of tasks (by, for example, the representative 506, the task recommendation system 512, and/or the third-party services 516 or other services/entities affiliated with the task facilitation service) (Paragraph 0151). In this case, the plain meaning of “providing feedback corresponding to the performance of the one or more proposal tasks” is merely describing how the algorithm is receiving continuous data to iteratively learn about the member’s preferences. However, claim 1 does not provide any details about how the NLP or the algorithm operates (see 2024 AI Guidance, Example 47, Claim 2). Also, the communication session and the retraining step are considered conventional computer functions of “receiving and transmitting over a network” and “performing repetitive calculations” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 8 is directed to a system at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 8 further recites “processor” and “memory” – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is ineligible.
Independent claim 15 is directed to an article of manufacture at step 1, which is a statutory category. Claim 15 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 15 further recites “non-transitory computer-readable storage medium” and “processor” – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, the claim is ineligible.
Dependent claims 2, 7, 9, 14, 16, and 21 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as wherein the coordination machine learning algorithm is used to: provide the set of resources for the performance of the one or more proposal tasks (Paragraph 0071); and select the vendor based on prior performance of similar tasks (Paragraph 0225). In this case, the additional limitations do not describe any specific details about how the trained machine learning algorithm operates or how the resources are selected (see 2024 AI Guidance, Example 47, Claim 2). Therefore, the trained machine learning algorithm is recited at a high level of generality, which results in “apply it” at both Step 2A, Prong 2 and Step 2B (MPEP 2106.05(f)). The claim is ineligible.
Dependent claims 5, 12, and 19 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as wherein the computer is further used to: automatically coordinate with the third-party entity for performance of at least one proposal task from the one or more proposal tasks. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, mere automation of a manual process may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05(a)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 22, 25, and 28 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as wherein when the member profile is updated, the proposal task generation algorithm processes the updated member profile to identify a set of preferences for generating the new tasks. Although the proposal task generation algorithm receives feedback over time to provide more accurate or improved recommendations (Paragraph 0092), the claim and specification do not include any specific details about how the trained machine learning model operates. Therefore, the trained proposal task generation algorithm is recited at a high level of generality, which results in “apply it” at both Step 2A, Prong 2 and Step 2B (MPEP 2106.05(f)). Also, the step of “dynamically updating a member profile” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). The claim is ineligible.
Dependent claims 23-24, 26-27, and 29-30 are directed to additional elements such as: one or more interface elements. The one or more interface elements is merely used to convey information obtained from systems of the task facilitation service, obtained from a member, obtained from a third-party service or other service/entity affiliated with the task facilitation service, and/or obtained from other sources. The application 1404 may use these user interface elements to obtain information from the representative and to provide the obtained information to systems of the task facilitation service, to the member, to a third-party service or other service/entity affiliated with the task facilitation service, and/or to other information subscribers (Paragraph 0244). This is considered “field of use” at step 2A, Prong 2; since it’s just used to receive information, but does not improve the interface (MPEP 2106.05h). Also, at Step 2B, this considered a conventional computer function of “receiving or transmitting data over a network” (MPEP 2106.05d). Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Claim Rejections - 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 7-9, 14-16, 19, 21-23, 25-26, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 2018/0053121 A1), in view of Wernick et al. (US 9,122,757 B1).
Regarding claim 1 (Currently Amended), Gonzalez et al. discloses a computer-implemented method, comprising (Paragraph 0001, The present disclosure relates to automating travel planning, and more particularly to methods, computer program products, and systems for generating a personalized and well-informed itinerary and providing follow-up services during a trip according to the generated itinerary):
communicating a set of proposal recommendations for completion of a project, wherein the set of proposal recommendations is communicated through an ongoing communications session between a member and a representative, and wherein the set of proposal recommendations is displayed through an application executing on a member computing device (Paragraph 0015, The user device 120 runs a web user interface 121, a software application program that facilitates access to the intelligent travel planning system 130 for the user 110, who may be an unregistered guest or a registered user; Paragraph 0052, The cognitive process 131 of the intelligent travel planning system 130 mainly interacts with the user 110 via the web user interface 121, which will be represented as the intelligent travel planning system 130 for brevity; Paragraph 0053, The user 110 speaks, “I want to take my 50 years old sister to Japan for a week to see Cherry blossom. Can you please help me plan?” The user 110 also may type in the text box, select one of the images, to begin a travel planning session. As the user 110 is interested in a seasonal travel option, the intelligent travel planning system 130, responds with a period during which the seasonal travel option is available, “Certainly! Cherry blossom in Japan usually blooms from February to May depending on regions.” The intelligent travel planning system 130 continues by asking, “Do you have a date range and/or places in mind?” In the same embodiment, the user 110 and the intelligent travel planning system 130 may communicate via a chat box with text display, via voice, via user input on the screen by selecting various selectable items, and combinations thereof. Also while responding to the user 110, the intelligent travel planning system 130 refreshes the screen display with images of Cherry blossom, and relevant information such as regional peak season map, etc., to further provide relevant information to the user 110; Paragraph 0054, The user 110 responds by, “How about end of March, early April?”, and, the intelligent travel planning system 130 selects cities/regions in which Cherry blossom is in season between March and April. The intelligent travel planning system 130 displays candidate locations as selected above by marking in a map, with any known user preference previously learned by the intelligent travel planning system 130 from the user 110 or from a large scale data on users who traveled to Japan for Cherry Blossom. The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April; Examiner notes that the representative is a bot, which is referred as the intelligent travel planning system. Also, Examiner interprets the “set of proposal recommendations” as the “places where the member can see the cherry blossoms in Japan”);
processing a set of messages exchanged through the ongoing communications session to identify a response to the set of proposal recommendations, wherein the set of messages is processed in through natural language processing (NLP) (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses. As the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130 and/or for components external to the intelligent travel planning system 130, processes corresponding to the specific request are invoked to complete the specific request, and to generate a result responsive to the inputs for the user 110; Paragraph 0053, The user 110 speaks, “I want to take my 50 years old sister to Japan for a week to see Cherry blossom. Can you please help me plan?” The user 110 also may type in the text box, select one of the images, to begin a travel planning session. As the user 110 is interested in a seasonal travel option, the intelligent travel planning system 130, responds with a period during which the seasonal travel option is available, “Certainly! Cherry blossom in Japan usually blooms from February to May depending on regions.” The intelligent travel planning system 130 continues by asking, “Do you have a date range and/or places in mind?” Paragraph 0054, The user 110 responds by, “How about end of March, early April?”, and, the intelligent travel planning system 130 selects cities/regions in which Cherry blossom is in season between March and April. The intelligent travel planning system 130 displays candidate locations as selected above by marking in a map, with any known user preference previously learned by the intelligent travel planning system 130 from the user 110 or from a large scale data on users who traveled to Japan for Cherry Blossom. The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April. Do you see ones you like or need more information?” The map presented during the travel planning session is similar to the high-level view map 410, having highlights in candidate locations 415; Paragraph 0055, tell me more about Tokyo?” The user 110 also may respond by clicking on or hovering over the displayed map to browse information as displayed by the intelligent travel planning system 130. The intelligent travel planning system 130 provides detailed information about Tokyo in the context of Cherry Blossom according to the request by the user 110 by: “There are many famous Cherry blossom viewing spots and activities in Tokyo including Ueno Park, Chidorigafuchi, hanami (picnic) party, boat rides, thousand cherry trees lit up in the evenings”; In this case, Examiner notes that the response is “Tokyo”);
processing a set of task parameters corresponding to the response through a proposal task generation algorithm to generate one or more proposal tasks, wherein the proposal task generation algorithm is trained to identify a set of [associations] from a dataset that includes historical task parameters and previously performed proposal tasks, and wherein the one or more proposal tasks are generated according to the set of [associations] (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses. As the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130 and/or for components external to the intelligent travel planning system 130; Paragraph 0020, By use of the history tracker 133, the intelligent travel planning system 130 may interact with respective users in a manner individually customized based on the usage data without a significant amount of preference data that are conventionally requested for the users to manually input. The usage data recorded by the history tracker 133 is made available for the user profiler 135 to build a user profile for later use as authorized by the user 110. For example, if the user 110, in the past, had repeatedly selected travel seasons which are characterized as more expensive but more likely to have pleasant weather, the intelligent travel planning system 130 associates a higher priority for weather than a priority for price, and adjust weights for respective aspects accordingly in the process of scoring a candidate itinerary by use of the scoring process 139. For another example, in a case that an unregistered guest requests information on a particular destination, the intelligent travel planning system 130 may utilize usage data from other users who made a trip to the destination such that the intelligent travel planning system 130 may predict what kind of information would be of interest to the unregistered guest more accurately and make recommendations as to activities, accommodations, local and nearby attractions, restaurants, etc. For still another example, the history tracker 133 may build large-scale purchase history data for a group of users having similar purchase histories such that the intelligent travel planning system 130 may recommend similar travel options for the group of users sharing similar purchase histories rather than for the general user population. The purchase histories of the intelligent travel planning system 130 may include travel destinations, travel reviews/evaluations, duration of travels, etc., that may or may not be what the user had preferred at the time of planning. The intelligent travel planning system 130 may add travel evaluations by users to respective user profiles; Paragraph 0054, The user 110 responds by, “How about end of March, early April?”, and, the intelligent travel planning system 130 selects cities/regions in which Cherry blossom is in season between March and April. The intelligent travel planning system 130 displays candidate locations as selected above by marking in a map, with any known user preference previously learned by the intelligent travel planning system 130 from the user 110 or from a large scale data on users who traveled to Japan for Cherry Blossom. The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April; Paragraph 0055, tell me more about Tokyo?” The user 110 also may respond by clicking on or hovering over the displayed map to browse information as displayed by the intelligent travel planning system 130. The intelligent travel planning system 130 provides detailed information about Tokyo in the context of Cherry Blossom according to the request by the user 110 by: “There are many famous Cherry blossom viewing spots and activities in Tokyo including Ueno Park, Chidorigafuchi, hanami (picnic) party, boat rides, thousand cherry trees lit up in the evenings”; Examiner notes that the machine learning is used to generate one or more propose tasks to the user based on previously learned user preferences (e.g., propose task/activities in Tokyo). Examiner interprets the machine learning as the proposal task generation algorithm);
processing the one or more proposal tasks through a task coordination machine learning algorithm to identify a set of resources for performance of the one or more proposal tasks; performing the one or more proposal tasks according to the set of resources, wherein when the one or more proposal tasks are performed, the project is completed according to the response (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses; Paragraph 0060, In the same embodiment, the intelligent travel planning system 130 further makes recommendations based on a departure airport, an arrival airport, travel dates, local transportation options, business hours of respective attractions, hotel availability in respective areas, user profile including physical conditions and preferences, memberships, reviews and ratings of candidate options, historical data of other travelers for the candidate options, special events during the travel period, and combinations thereof. The intelligent travel planning system 130 builds and displays the exemplary itinerary 400 including the high-level view map 410 and the detailed view table 420 to the user 110. The high-level view map 410 includes, regions, cities, days, etc., respectively linked to relevant item in the detailed view table 420, and vice versa. Activities, names of airports, hotels, tourist destinations, etc., in the detailed view table 420 are linked to further information such as respective home pages, review pages, etc. Once the user 110 decides on the itinerary as customized and adjusted, the intelligent travel planning system 130 makes reservation with respective travel booking systems and keeps a coordinated record based on the itinerary of the user 110; Examiner notes that the machine learning of Gonzalez et al. is further used to coordinate tasks since it can identify resources for performance of the one or more proposed tasks (e.g., coordinate with a hotel that has availability on the selected dates and/or has good reviews));
continuously processing new messages exchanged through the ongoing communications session through NLP to obtain feedback corresponding to the performance of the one or more proposal tasks (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses; Paragraph 0020, For still another example, the history tracker 133 may build large-scale purchase history data for a group of users having similar purchase histories such that the intelligent travel planning system 130 may recommend similar travel options for the group of users sharing similar purchase histories rather than for the general user population. The purchase histories of the intelligent travel planning system 130 may include travel destinations, travel reviews/evaluations, duration of travels, etc., that may or may not be what the user had preferred at the time of planning. The intelligent travel planning system 130 may add travel evaluations by users to respective user profiles; Paragraph 0024, The personal preference information of the user 110 may have been gathered by the cognitive process 131 during a live session with the user 110, made available from the history tracker 133 and/or the user profiler 135. The scoring process 139 weighs relative significance of respective sources of the personal preferences information based on predetermined configuration; Paragraph 0034, In block 210, the intelligent travel planning system 130 gathers personal preference information of the user and travel requirements. As described above, the personal preference information may be a combination of a user profile, a purchase history, inputs and feedback to a proposed itinerary during a travel planning session; Examiner interprets “feedback related to travel reviews/evaluations” as the “feedback corresponding to the performance of the one or more proposal tasks”);
retraining the proposal task generation algorithm and the task coordination machine learning algorithm to automatically generate and perform new tasks without representative interaction, wherein the proposal task generation algorithm and the task coordination machine learning algorithm are retrained based on the feedback; and communicating the new tasks through the ongoing communications session, wherein the new tasks are communicated automatically by the proposal task generation algorithm without the representative interaction (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses. As the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130 and/or for components external to the intelligent travel planning system 130, processes corresponding to the specific request are invoked to complete the specific request, and to generate a result responsive to the inputs for the user 110; Paragraph 0024, The personal preference information of the user 110 may have been gathered by the cognitive process 131 during a live session with the user 110, made available from the history tracker 133 and/or the user profiler 135. The scoring process 139 weighs relative significance of respective sources of the personal preferences information based on predetermined configuration; Paragraph 0060, In the same embodiment, the intelligent travel planning system 130 further makes recommendations based on a departure airport, an arrival airport, travel dates, local transportation options, business hours of respective attractions, hotel availability in respective areas, user profile including physical conditions and preferences, memberships, reviews and ratings of candidate options, historical data of other travelers for the candidate options, special events during the travel period, and combinations thereof; Paragraph 0061, Certain embodiments of the present invention may offer various technical computing advantages, including fully automating, customizing and following up a travel planning process for a coordinated and personalized itinerary; Examiner notes that the representative is a machine learning bot, which is referred as the intelligent travel planning system. In this case, the machine learning bot is already sufficiently trained to automatically generate one or more propose tasks to the user based on previously learned user preferences. Also, the machine learning bot coordinates activities based on dates, reviews, and/or hotel availability).
Gonzalez et al. discloses training a proposal task generation algorithm to generate a set of proposal tasks (Paragraph 0018, using a machine learning to learn personal preferences of the user based on previous selections), Although Gonzalez et al. further discloses learning personal preferences by identifying associations from a dataset that includes historical task parameters and previously performed proposal tasks (Paragraph 0020, associates a higher priority for weather than a priority for price based on previous selections), Gonzalez et al. does not specifically disclose learning personal preferences by identifying a set of correlations from a dataset that includes historical task parameters and previously performed proposal tasks (e.g., substitute an association learning algorithm for a correlation learning algorithm).
However, Wernick et al. discloses processing a set of task parameters corresponding to the response through a proposal task generation algorithm to generate one or more proposal tasks, wherein the proposal task generation algorithm is trained to identify a set of correlations from a dataset that includes historical task parameters and previously performed proposal tasks, and wherein the one or more proposal tasks are generated according to the set of correlations (Column 5, items 23-28, Feedback may be incorporated into item classifications, rules and/or templates to improve plan generation. Optionally, feedback on specific item classifications may be used to refine or modify those item classifications. Feedback may be acquired during the planning process, while the plan is being executed and/or thereafter; Column 5, lines 44-64, Feedback from large numbers of people may be collected and used to discover correlations among items. For example, feedback from users who have attended concerts at a particular venue may indicate that a significant number of attendees at concerts performed at that particular venue often go to a nearby restaurant afterwards for dinner. This information may be incorporated in plans generated for concerts at that venue by suggesting dinner afterwards at the nearby restaurant. Users who build plans around that concert venue would benefit as a result of prior feedback since they would see more plans that include the nearby restaurant. Depending on the feedback available, finer-grained correlations may be discovered among different plan items. For example, it may be learned that attendees of concerts at a particular venue prefer one restaurant, while attendees of comedy shows at the same venue prefer a different restaurant. Optionally, any correlations among items may be used independently of plans for other purposes such as providing the user a list of items that correlate with a particular item (e.g., providing a list of “classy” bars that complement a particular “classy” restaurant).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for training a proposal task generation algorithm to generate one or more proposal tasks based on previous responses/feedback (e.g., using an association learning algorithm) of the invention of Gonzalez et al. to further incorporate wherein the proposal tasks are generated by identifying a set of correlations from a dataset that includes historical task parameters and previously performed proposal tasks (e.g., using a correlation algorithm) of the invention of Wernick et al. because doing so would allow the method to discover correlations among different plan items based on the feedback, which is used to improve plan generation (see Wernick et al., Column 5, items 23-28 & Column 5, lines 44-64). Further, the claimed invention is merely a simple substitution of one known algorithm for another known algorithm to obtain predictable results (e.g., substitution of an association learning algorithm for a correlation learning algorithm).
Regarding claim 8 (Currently Amended), Gonzalez et al. discloses a system comprising (Paragraph 0001, The present disclosure relates to automating travel planning, and more particularly to methods, computer program products, and systems for generating a personalized and well-informed itinerary and providing follow-up services during a trip according to the generated itinerary):
one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to (Paragraph 0085, Computer system 12 may be described in the general context of computer system-executable instructions, such as program processes, being executed by a computer system; Paragraph 0086, The components of computer system 12 may include, but are not limited to, one or more processors 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16):
communicate a set of proposal recommendations for completion of a project, wherein the set of proposal recommendations is communicated through an ongoing communications session between a member and a representative, and wherein the set of proposal recommendations is displayed through an application executing on a member computing device (Paragraph 0015, The user device 120 runs a web user interface 121, a software application program that facilitates access to the intelligent travel planning system 130 for the user 110, who may be an unregistered guest or a registered user; Paragraph 0052, The cognitive process 131 of the intelligent travel planning system 130 mainly interacts with the user 110 via the web user interface 121, which will be represented as the intelligent travel planning system 130 for brevity; Paragraph 0053, The user 110 speaks, “I want to take my 50 years old sister to Japan for a week to see Cherry blossom. Can you please help me plan?” The user 110 also may type in the text box, select one of the images, to begin a travel planning session. As the user 110 is interested in a seasonal travel option, the intelligent travel planning system 130, responds with a period during which the seasonal travel option is available, “Certainly! Cherry blossom in Japan usually blooms from February to May depending on regions.” The intelligent travel planning system 130 continues by asking, “Do you have a date range and/or places in mind?” In the same embodiment, the user 110 and the intelligent travel planning system 130 may communicate via a chat box with text display, via voice, via user input on the screen by selecting various selectable items, and combinations thereof. Also while responding to the user 110, the intelligent travel planning system 130 refreshes the screen display with images of Cherry blossom, and relevant information such as regional peak season map, etc., to further provide relevant information to the user 110; Paragraph 0054, The user 110 responds by, “How about end of March, early April?”, and, the intelligent travel planning system 130 selects cities/regions in which Cherry blossom is in season between March and April. The intelligent travel planning system 130 displays candidate locations as selected above by marking in a map, with any known user preference previously learned by the intelligent travel planning system 130 from the user 110 or from a large scale data on users who traveled to Japan for Cherry Blossom. The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April; Examiner notes that the representative is a bot, which is referred as the intelligent travel planning system. Also, Examiner interprets the “set of proposal recommendations” as the “places where the member can see the cherry blossoms in Japan”);
process a set of messages exchanged through the ongoing communications session to identify a response to the set of proposal recommendations, wherein the set of messages is processed in real-time through natural language processing (NLP) (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses. As the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130 and/or for components external to the intelligent travel planning system 130, processes corresponding to the specific request are invoked to complete the specific request, and to generate a result responsive to the inputs for the user 110; Paragraph 0053, The user 110 speaks, “I want to take my 50 years old sister to Japan for a week to see Cherry blossom. Can you please help me plan?” The user 110 also may type in the text box, select one of the images, to begin a travel planning session. As the user 110 is interested in a seasonal travel option, the intelligent travel planning system 130, responds with a period during which the seasonal travel option is available, “Certainly! Cherry blossom in Japan usually blooms from February to May depending on regions.” The intelligent travel planning system 130 continues by asking, “Do you have a date range and/or places in mind?” Paragraph 0054, The user 110 responds by, “How about end of March, early April?”, and, the intelligent travel planning system 130 selects cities/regions in which Cherry blossom is in season between March and April. The intelligent travel planning system 130 displays candidate locations as selected above by marking in a map, with any known user preference previously learned by the intelligent travel planning system 130 from the user 110 or from a large scale data on users who traveled to Japan for Cherry Blossom. The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April. Do you see ones you like or need more information?” The map presented during the travel planning session is similar to the high-level view map 410, having highlights in candidate locations 415; Paragraph 0055, tell me more about Tokyo?” The user 110 also may respond by clicking on or hovering over the displayed map to browse information as displayed by the intelligent travel planning system 130. The intelligent travel planning system 130 provides detailed information about Tokyo in the context of Cherry Blossom according to the request by the user 110 by: “There are many famous Cherry blossom viewing spots and activities in Tokyo including Ueno Park, Chidorigafuchi, hanami (picnic) party, boat rides, thousand cherry trees lit up in the evenings”; In this case, Examiner notes that the response is “Tokyo”);
process a set of task parameters corresponding to the response through a proposal task generation algorithm to generate one or more proposal tasks, wherein the proposal task generation algorithm is trained to identify a set of [associations] from a dataset that includes historical task parameters and previously performed proposal tasks, and wherein the one or more proposal tasks are generated according to the set of [associations] (Paragraph 0018, The cognitive process 131 of the intelligent travel planning system 130 interacts with the user 110 via the web user interface 121 to take travel requirement inputs and feedback responses from the user 110. The cognitive process 131 includes natural language processing and machine learning functionalities, and is trained in travel jargon such that the intelligent travel planning system 130 may communicate with the user 110 in a natural language to plan a trip and may learn personal preferences of the user 110 as the interaction between the cognitive process 131 and the user 110 progresses. As the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130 and/or for components external to the intelligent travel planning system 130; Paragraph 0020, By use of the history tracker 133, the intelligent travel planning system 130 may interact with respective users in a manner individually customized based on the usage data without a significant amount of preference data that are conventionally requested for the users to manually input. The usage data recorded by the history tracker 133 is made available for the user profiler 135 to build a user profile for later use as authorized by the user 110. For example, if the user 110, in the past, had repeatedly selected travel seasons which are characterized as more expensive but more likely to have pleasant weather, the intelligent travel planning system 130 associates a higher priority for weather than a priority for price, and adjust weights for respective aspects accordingly in the process of scoring a candidate itinerary by use of the scoring process 139. For another example, in a case that an unregistered guest requests information on a particular destination, the intelligent travel planning system 130 may utilize usage data from other users who made a trip to the destination such that the intelligent travel planning system 130 may predict what kind of information would be of interest to the unregistered guest more accurately and make recommendations as to activities, accommodations, local and nearby attractions, restaurants, etc. For still another example, the history tracker 133 may build large-scale purchase history data for a group of users having similar purchase histories such that the intelligent travel planning system 130 may recommend similar travel options for the group of users sharing similar purchase histories rather than for the general user population. The purchase histories of the intelligent travel