DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-21 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 .
Response to Arguments
Applicant's arguments filed 01/16/2026 (related to the 103 Rejection) 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.
Applicant's arguments filed on 01/16/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on page 12, that withdrawal of the rejection is respectfully requested.
Examiner respectfully disagrees with Applicant. Claims 1-21 are still directed to a judicial exception (e.g., abstract ideal) without reciting significantly more.
Step 2A, Prong One - 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, dynamically generating a ranking based on previous member selections is merely considering historical information to provide a filtered content to a user (see MPEP 2106.04(a)(2), filtering content). 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 - Claim 1 includes additional elements: a machine learning algorithm; a chat interface; a customized chat interface; and a customized interface element. The machine learning algorithm is merely used to process the chat flow to automatically assign at least one of the one or more task recommendations to messages of the set of messages (Paragraphs 0004 & 0128). The chat interface is merely used to: receive in real-time a set of messages between a member and a representative (Paragraph 0182); provide one or more task recommendations (Paragraph 0182); receive additional real-time messages between the member and the representative; and provide one or more task recommendations based on the received real-time messages (Paragraphs 0186 & 0209). The customized chat interface is merely used to present, to the member, a limited number of task and/or project recommendations from the ranked list of the projects and/or tasks, the task selection sub-system may process the ranked list and the member's profile from the user datastore to determine which project and/or task recommendations should be presented to the member (Paragraph 0126). The customized interface element is merely used to provide specific message details associated with messages for a given task, or can be modified uniquely based on task characteristics or task types (Paragraph 0183). These elements of “machine learning algorithm,” “customized chat interface,” and “customized interface element” 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 (MPEP 2106.05f). In this case, the machine learning algorithm includes inputs (e.g., message history and real-time feedback) and outputs (e.g., plurality of task recommendations). Although the machine learning algorithm receives updated inputs over time (e.g., member-corrected ranking), the claim and specification do not include any specific details about how the machine learning algorithm operates (see 2024 AI Guidance, Example 47, claim 2). For example, the plain meaning of the “training” step is merely describing how the machine learning is receiving continuous data to iteratively adjust the values/parameters to minimize a loss function (e.g., improve an accuracy score). Thus, the training step is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)). Also, the chat interface and the customized chat interface are considered “field of use” (MPEP 2106.05h) as they’re just used to receive member feedback (e.g., member-corrected ranking) and present filtered information (e.g., a limited number of task based on the ranking and/or provide specific message details for a given task), but the interface is not improved. 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 machine learning algorithm does not provide any specific details of how the task recommendations and/or the specific message details are generated, which results in “apply it.” The learning step of “determining a ranking according to the member-corrected ranking of the set of tasks” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05d). The chat interface and customized chat interface are considered a conventional computer function of receiving or transmitting data over a network (MPEP 2106.05d). Also, 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). In this case, the customized chat interface is merely used to arrange information (e.g., present filtered data based on user feedback such as a member-corrected ranking) in a manner that assists users in processing information more quickly, which is not sufficient to show an improvement in computer functionality (MPEP 2106.05a). Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same functions in combination as each element performs separately. The claim is not patent eligible.
Independent claims 8 and 15 recite similar features and therefore are rejected for the same reasons as independent claim 1. Claims 2-7, 9-14, and 16-21 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 8, and 11.
Examiner notes that independent claims are similar to Example 47 of the 2024 Guidance Update on Patent Subject Matter Eligibility (see claim 2 of Example 47).
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 1-21 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: receiving a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks; processing a set of ongoing messages exchanged between a member and a representative, wherein the set of ongoing messages is processed to determine a ranking according to the member-corrected ranking of the set of tasks, timing requirements associated with the set of tasks, and a member profile; dynamically generating a customized according to the ranking, wherein dynamically generating includes prioritizing future ongoing tasks, thereby improving accuracy in prioritizing the future ongoing tasks; sorting the future ongoing tasks according to priority, thereby reducing device 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, dynamically generating a ranking based on previous member selections is merely considering historical information to provide a filtered content to a user (see MPEP 2106.04(a)(2), filtering content). 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 machine learning algorithm; a chat interface; a customized chat interface; and a customized interface element.
The computer is merely used to execute code of the task facilitation service (Paragraph 0049). The machine learning algorithm is merely used to process the chat flow to automatically assign at least one of the one or more task recommendations to messages of the set of messages (Paragraphs 0004 & 0128). The chat interface is merely used to: receive in real-time a set of messages between a member and a representative (Paragraph 0182); provide one or more task recommendations (Paragraph 0182); receive additional real-time messages between the member and the representative; and provide one or more task recommendations based on the received real-time messages (Paragraphs 0186 & 0209). The customized chat interface is merely used to present, to the member, a limited number of task and/or project recommendations from the ranked list of the projects and/or tasks, the task selection sub-system may process the ranked list and the member's profile from the user datastore to determine which project and/or task recommendations should be presented to the member (Paragraph 0126). The customized interface element is merely used to provide specific message details associated with messages for a given task, or can be modified uniquely based on task characteristics or task types (Paragraph 0183). 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,” “machine learning algorithm,” “customized chat interface,” and “customized interface element” 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 chat interfaces (e.g., customized chat interface & customized chat element) are considered “field of use” (MPEP 2106.05h) as they’re just used to receive member feedback (e.g., member-corrected ranking) and present filtered information (e.g., a limited number of task based on the ranking and/or provide specific message details for a given task), but the interface is not improved. The interface element is considered “field of use” since it’s just used to present information, but the interface 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 determining an updated set of tasks based on user feedback (e.g., corrected ranking of the set of tasks). The specification shows that the computer is merely used to execute code of the task facilitation service (Paragraph 0049). The machine learning algorithm is merely used to process the chat flow to automatically assign at least one of the one or more task recommendations to messages of the set of messages (Paragraphs 0004 & 0128). The chat interface is merely used to: receive in real-time a set of messages between a member and a representative (Paragraph 0182); provide one or more task recommendations (Paragraph 0182); receive additional real-time messages between the member and the representative; and provide one or more task recommendations based on the received real-time messages (Paragraphs 0186 & 0209). The customized chat interface is merely used to present, to the member, a limited number of task and/or project recommendations from the ranked list of the projects and/or tasks, the task selection sub-system may process the ranked list and the member's profile from the user datastore to determine which project and/or task recommendations should be presented to the member (Paragraph 0126). The customized interface element is merely used to provide specific message details associated with messages for a given task, or can be modified uniquely based on task characteristics or task types (Paragraph 0183). Also, the chat interfaces (e.g., customized chat interface & customized chat element) are considered a conventional computer function of “receiving and transmitting over a network” and “performing repetitive calculations” (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.
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: a memory; and a processor. The memory is merely used to store code (Paragraph 0251). The processor is merely used to execute the code (Paragraph 0251). These elements of “memory” and “processor” 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, they are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, nothing in the claim adds significantly more to the abstract idea. 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: a non-transitory computer readable storage medium; and a processor. The non-transitory computer readable storage medium is merely used to store code (Paragraph 0249). The processor is merely used to execute the code (Paragraph 0251). These elements of “memory” and “processor” 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, they are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 2, 5, 9, 12, 16, and 19 are directed to an additional element such as: a task recommendation algorithm. The task recommendation algorithm is to: process the message history; generate a task recommendation; receive a response to the task recommendation message; and update the message history with the response (Paragraphs 0005 & 0008). Using the algorithm is considered a “particular technological environment” MPEP 2106.05h at Step 2A. Also, the algorithm is merely used as a tool to perform an abstract idea at Step 2B. Further, the step of “update” is a considered a conventional function of “performing repetitive calculations” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 3, 10, and 17 are directed to an additional element such as: a template. The template is merely used to store data elements needed to provide the one or more task recommendations (Paragraph 0006). However, using a template is considered “field of use” MPEP 2106.05h at Step 2A, Prong 2; since the template is not improved, and that data is just placed there. At Step 2B, this is conventional still, “storing information in a memory” (see MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 4, 11, 18 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 by: receiving additional real-time messages between the member and the representative as the additional real-time messages are being exchanged; processing additional chat flow associated with the additional real-time messages to assign at least a task recommendation of a plurality of task recommendations to one or more messages of the additional real-time messages, wherein the plurality of task recommendations corresponds to the set of tasks; and dynamically annotating the message history based on the processing of the one or more messages of the additional real-time messages. These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to a method of organizing human activity which include managing interactions between people. In addition, no additional elements are integrated into the abstract idea. The additional function of “receiving additional messages” is still considered “field of use” at step 2A, prong 2; since it’s just used to collect and analyze communication data between a member and a representative, but the interface of the chat flow is not improved (MPEP 2106.05h). At step 2B, it’s still considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 6, 13, and 20 are directed to an additional element such as: a chat history. The chat history is merely used to isolate messages associated with the first task (Paragraph 0188). However, using a chat history is considered “field of use” MPEP 2106.05h at Step 2A, Prong 2; since the chat history is not improved, and that data is just placed there. At Step 2B, this is conventional still, “storing information in a memory” (see MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 7 and 14 are directed to an additional element such as: a second machine learning algorithm. The machine learning algorithm is merely used to update selection of position or timing of future reminders within the chat flow of the chat interface (Paragraph 0188). Merely stating that the step is performed by a computer component (e.g., machine learning) 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 (see 2024 AI Guidance, Example 47, claim 2). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claim 21 is directed to an additional element such as: a second chat interface. The second chat interface is merely used to associate with each task with separate and distinct chat flow interfaces (Paragraph 0183, identify a vacation task and a home repair task and create a chat flow interface for each task). This is still considered “field of use” at step 2A, prong 2; since it’s just used to collect and analyze communication data between a member and a representative, but the interface of the chat flow is not improved (MPEP 2106.05h). At step 2B, it’s still considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” (MPEP 2106.05(d)). Also, 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). In this case, the customized chat interface is merely used to arrange information (e.g., present multiple chat flow interfaces) in a manner that assists users in processing information more quickly, which is not 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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 2018/0053121 A1), in view of Sim et al. (US 2021/0373943 A1), in further view of Gruber et al. (US 2013/0275164 A1).
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):
receiving, …, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0024, The scoring process 139 weighs relative significance of respective sources of the personal preferences information based on predetermined configuration; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Examiner notes that the messages are organized based on the updated ranking/scoring of the set of tasks and/or updated configuration);
processing a set of ongoing messages exchanged between a member and a representative within the chat interface, wherein the set of ongoing messages is processed through a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks, …, and a member profile (Paragraph 0018, 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, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; 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; As stated in Paragraph 0045 of Applicant’s specification, the representative can be an automated process, such as a bot. Examiner notes that Gonzalez discloses wherein the representative is a bot, which is referred as the intelligent travel planning system. Also, Examiner interprets “recommended tasks to plan a trip” as the “set of tasks.” Lastly, Examiner interprets “adjusting the rank/score of the recommended tasks based on previously learned preferences of the member or similar members” as the “member-corrected ranking of the set of tasks”);
dynamically generating a customized chat interface according to the ranking, wherein dynamically generating includes identifying a customized interface element for prioritizing future ongoing tasks through the customized chat interface (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; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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), thereby improving accuracy of the customized chat interface in prioritizing the future ongoing tasks (Paragraph 0020, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions);
sorting the future ongoing tasks in the customized chat interface according to priority, wherein the future ongoing tasks are sorted using the customized interface element, thereby reducing device interaction (Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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; Examiner notes that the interface of Gonzalez et al. provides a customized interface element since it only recommends activities/tasks that are best suited for the personal preference of the user based on an updated rank/score).
Although Gonzalez et al. discloses a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and member profile (e.g., a machine learning to recommend activities/tasks that are best suited for the personal preference of the user based on an updated rank/score), Gonzalez et al. does not specifically disclose wherein the ranking is further determined based on timing requirements associated with the set of tasks.
However, Sim et al. discloses … a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks, timing requirements associated with the set of tasks, and a member profile (Figure 3, Communication Flow; Paragraph 0022, The task hub 102 may provide recommendations to the user depending on the content of the task, user status, user feedback and personalized needs for information. The task hub 102 may take into account a user's preferences for modality of the assistance/recommendations and an account of user's preference; Paragraph 0026, In aspects, the task hub model is a machine learning model; Paragraph 0029, The order of subtasks 222 provides the task agent in the task hub with the order that each subtask should be performed according to the input 204 that was fed into the task hub model 202. To determine the order of subtasks 222, the model first identifies any explicit dependencies between subtasks 216A and 216B. Then, the model may choose to rank tasks for execution based on a) availability of resources to complete them; b) any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance); and c) by grouping by proximity/relevance—some subtasks might be performed together at the same location or in a single online order, etc.; Paragraph 0042, Initially, subtasks that can be automated may be completed by a user humans to produce a training set for a machine to learn to imitate perform user actions or preferences; Paragraph 0047, Furthermore, the task hub archives the details of completed subtasks, which can be a useful reference for completing future tasks (e.g. by recalling which contractor completed some related subtask), or for other historical purposes, such as accounting or auditing. When deployed across many users, the intelligent task hub can learn from user behavior to better manage, prioritize, and execute common subtasks, e.g., by updating its model periodically based on user actions; Examiner notes that the machine learning can rank/prioritize the order of the subtasks based on task parameters such as user behavior, user preference, and/or task deadline (e.g., pick up flowers 48 hours in advanced). Examiner interprets the task deadline as the timing requirements associated with the set of tasks).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate wherein the set of tasks (e.g., planning a trip or planning a wedding) are ranked according to timing requirements associated with the tasks of the invention of Sim et al. because doing so would allow the method to rank tasks for execution based on any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance) (see Sim et al., Paragraph 0029). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although Gonzalez et al. discloses receiving a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (e.g., based on predetermined configuration such as a higher weight for weather and a lower weight for budget), Gonzalez et al. does not specifically disclose how the user is selecting the specific criteria using a chat interface.
However, Gruber et al. (US 20130275164 A1) discloses receiving, via a chat interface, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0719, Selection criteria may have an inherent preference order. That is, the values of any particular criterion may be used to line up items in a best first order. For example, the proximity criterion has an inherent preference that closer is better. Location, on the other hand, has no inherent preference value. This restriction allows the system to make default assumptions and guide the selection if the user only mentions the criterion. For example, the user interface might offer to "sort by rating" and assume that higher rated is better; Paragraph 0750, In one embodiment, there is a precedence ordering among selection criteria. That is, some criteria may matter more than others in the filter and sort. In one embodiment, those criteria selected by the user are given higher precedence than others, and there is a default ordering over one or more criteria; Paragraph 1007, Search criteria for identifying reservable items include a search class or selection class (e.g., restaurants, entertainment events, etc.), and various constraints (e.g., location, time, price, review, genre, cuisine, etc.) for filtering and ranking the search results; Examiner interprets “selecting a class and various constraints” as the “specific criteria including a member-corrected ranking of a set of tasks”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate how the user is selecting the specific criteria to organize messages via a chat interface (e.g., a filter in the chat interface) of the invention of Gruber et al. because doing so would allow the method to filter and rank search results based on a selection criteria provided in the user interface (see Gruber et al., Paragraphs 0719 & 1007). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8 (Currently Amended), Gonzalez et al. discloses a device comprising: a memory; and one or more processors coupled to the memory and configured to perform operations 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; 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):
receiving, …, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0024, The scoring process 139 weighs relative significance of respective sources of the personal preferences information based on predetermined configuration; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Examiner notes that the messages are organized based on the ranking/scoring of the set of tasks);
processing a set of ongoing messages exchanged between a member and a representative within the chat interface, wherein the set of ongoing messages is processed through a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks, …, and a member profile (Paragraph 0018, 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; Paragraph 0020, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; 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; As stated in Paragraph 0045 of Applicant’s specification, the representative can be an automated process, such as a bot. Examiner notes that Gonzalez discloses wherein the representative is a bot, which is referred as the intelligent travel planning system. Also, Examiner interprets “recommended tasks to plan a trip” as the “set of tasks.” Lastly, Examiner interprets “adjusting the rank/score of the recommended tasks based on previously learned preferences of the member or similar members” as the “member-corrected ranking of the set of tasks”);
dynamically generating a customized chat interface according to the ranking, wherein dynamically generating includes identifying a customized interface element for prioritizing future ongoing tasks through the customized chat interface (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; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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), thereby improving accuracy of the customized chat interface in prioritizing the future ongoing tasks (Paragraph 0020, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions);
sorting the future ongoing tasks in the customized chat interface according to priority, wherein the future ongoing tasks are sorted using the customized interface element, thereby reducing device interaction (Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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; Examiner notes that the interface of Gonzalez et al. provides a customized interface element since it only recommends activities/tasks that are best suited for the personal preference of the user based on a rank/score).
Although Gonzalez et al. discloses a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and member profile (e.g., a machine learning to recommend activities/tasks that are best suited for the personal preference of the user based on a rank/score), Gonzalez et al. does not specifically disclose wherein the ranking is further determined based on timing requirements associated with the set of tasks.
However, Sim et al. discloses … a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks, timing requirements associated with the set of tasks, and a member profile (Figure 3, Communication Flow; Paragraph 0022, The task hub 102 may provide recommendations to the user depending on the content of the task, user status, user feedback and personalized needs for information. The task hub 102 may take into account a user's preferences for modality of the assistance/recommendations and an account of user's preference; Paragraph 0026, In aspects, the task hub model is a machine learning model; Paragraph 0029, The order of subtasks 222 provides the task agent in the task hub with the order that each subtask should be performed according to the input 204 that was fed into the task hub model 202. To determine the order of subtasks 222, the model first identifies any explicit dependencies between subtasks 216A and 216B. Then, the model may choose to rank tasks for execution based on a) availability of resources to complete them; b) any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance); and c) by grouping by proximity/relevance—some subtasks might be performed together at the same location or in a single online order, etc.; Paragraph 0042, Initially, subtasks that can be automated may be completed by a user humans to produce a training set for a machine to learn to imitate perform user actions or preferences; Paragraph 0047, Furthermore, the task hub archives the details of completed subtasks, which can be a useful reference for completing future tasks (e.g. by recalling which contractor completed some related subtask), or for other historical purposes, such as accounting or auditing. When deployed across many users, the intelligent task hub can learn from user behavior to better manage, prioritize, and execute common subtasks, e.g., by updating its model periodically based on user actions; Examiner notes that the machine learning can rank/prioritize the order of the subtasks based on task parameters such as user behavior, user preference, and/or task deadline (e.g., pick up flowers 48 hours in advanced). Examiner interprets the task deadline as the timing requirements associated with the set of tasks).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate wherein the set of tasks (e.g., planning a trip or planning a wedding) are ranked according to timing requirements associated with the tasks of the invention of Sim et al. because doing so would allow the method to rank tasks for execution based on any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance) (see Sim et al., Paragraph 0029). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although Gonzalez et al. discloses receiving a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (e.g., based on predetermined configuration such as a higher weight for weather and a lower weight for budget), Gonzalez et al. does not specifically disclose how the user is selecting the specific criteria using a chat interface.
However, Gruber et al. discloses receiving, via a chat interface, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0719, Selection criteria may have an inherent preference order. That is, the values of any particular criterion may be used to line up items in a best first order. For example, the proximity criterion has an inherent preference that closer is better. Location, on the other hand, has no inherent preference value. This restriction allows the system to make default assumptions and guide the selection if the user only mentions the criterion. For example, the user interface might offer to "sort by rating" and assume that higher rated is better; Paragraph 0750, In one embodiment, there is a precedence ordering among selection criteria. That is, some criteria may matter more than others in the filter and sort. In one embodiment, those criteria selected by the user are given higher precedence than others, and there is a default ordering over one or more criteria; Paragraph 1007, Search criteria for identifying reservable items include a search class or selection class (e.g., restaurants, entertainment events, etc.), and various constraints (e.g., location, time, price, review, genre, cuisine, etc.) for filtering and ranking the search results; Examiner interprets “selecting a class and various constraints” as the “specific criteria including a member-corrected ranking of a set of tasks”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate how the user is selecting the specific criteria to organize messages via a chat interface (e.g., a filter in the chat interface) of the invention of Gruber et al. because doing so would allow the method to filter and rank search results based on a selection criteria provided in the user interface (see Gruber et al., Paragraphs 0719 & 1007). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15 (Currently Amended), Gonzalez et al. discloses a non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform operations 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; Paragraph 0098, The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention; Paragraph 0099, A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a wave guide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire):
receiving, …, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0024, The scoring process 139 weighs relative significance of respective sources of the personal preferences information based on predetermined configuration; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Examiner notes that the messages are organized based on the updated ranking/scoring of the set of tasks and/or updated configuration);
determining a ranking of the set of tasks using a processor that associates a plurality of task recommendations with one or more ongoing messages according to a profile of a member …, wherein the plurality of task recommendations corresponds to the set of tasks, and wherein the one or more ongoing messages are exchanged between the member and a representative within a chat interface (Paragraph 0018, 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; Paragraph 0020, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; 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; As stated in Paragraph 0045 of Applicant’s specification, the representative can be an automated process, such as a bot. Examiner notes that Gonzalez discloses wherein the representative is a bot, which is referred as the intelligent travel planning system. Also, Examiner interprets “scoring tasks to plan a trip” as the “ranking of the set of tasks”);
determining the member-corrected ranking of the set of tasks using real-time feedback received through the chat interface (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; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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);
dynamically sorting messages to form an updated chat interface, wherein sorting messages is based on the member-corrected ranking of the set of tasks (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; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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; Examiner notes that the interface of Gonzalez et al. provides a customized interface element since it only recommends activities/tasks that are best suited for the personal preference of the user based on an updated rank/score);
processing the member-corrected ranking of the set of tasks through a chat interface machine learning system to identify a customized interface element useful for prioritizing future tasks through a future customized chat interface, and generating the future customized chat interface including the customized interface element (Paragraph 0018, 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, 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.; Paragraph 0026, By use of the real time interactions, the scoring process 139 may weigh latest interests of the user and specific purposes of a current trip more than general priorities and personal preferences of the user. By use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information; 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), thereby improving accuracy of the future customized chat interface in prioritizing the future tasks (Paragraph 0020, 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.; Paragraph 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110. The scoring process 139 provides criteria for the itinerary builder 137 to meet in searching individual booking results and in selecting the individual booking results for the itinerary, and consequently, alleviates some computing workloads of the cognitive process 131 in maintaining the real time interaction with users by optimizing the itinerary within less number of interactions).
Although Gonzalez et al. discloses a machine learning algorithm to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and member profile (e.g., a machine learning to recommend activities/tasks that are best suited for the personal preference of the user based on an updated rank/score), Gonzalez et al. does not specifically disclose wherein the ranking is further determined based on timing requirements associated with the set of tasks.
However, Sim et al. discloses determining a ranking of the set of tasks using a processor that associates a plurality of task recommendations with one or more ongoing messages according to a profile of a member and timing requirements associated with the tasks, … (Figure 3, Communication Flow; Paragraph 0022, The task hub 102 may provide recommendations to the user depending on the content of the task, user status, user feedback and personalized needs for information. The task hub 102 may take into account a user's preferences for modality of the assistance/recommendations and an account of user's preference; Paragraph 0026, In aspects, the task hub model is a machine learning model; Paragraph 0029, The order of subtasks 222 provides the task agent in the task hub with the order that each subtask should be performed according to the input 204 that was fed into the task hub model 202. To determine the order of subtasks 222, the model first identifies any explicit dependencies between subtasks 216A and 216B. Then, the model may choose to rank tasks for execution based on a) availability of resources to complete them; b) any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance); and c) by grouping by proximity/relevance—some subtasks might be performed together at the same location or in a single online order, etc.; Paragraph 0042, Initially, subtasks that can be automated may be completed by a user humans to produce a training set for a machine to learn to imitate perform user actions or preferences; Paragraph 0047, Furthermore, the task hub archives the details of completed subtasks, which can be a useful reference for completing future tasks (e.g. by recalling which contractor completed some related subtask), or for other historical purposes, such as accounting or auditing. When deployed across many users, the intelligent task hub can learn from user behavior to better manage, prioritize, and execute common subtasks, e.g., by updating its model periodically based on user actions; Examiner notes that the machine learning can rank/prioritize the order of the subtasks based on task parameters such as user behavior, user preference, and/or task deadline (e.g., pick up flowers 48 hours in advanced). Examiner interprets the task deadline as the timing requirements associated with the set of tasks).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate wherein the set of tasks (e.g., planning a trip or planning a wedding) are ranked according to timing requirements associated with the tasks of the invention of Sim et al. because doing so would allow the method to rank tasks for execution based on any needed lead time (e.g., need to book a caterer several months in advance, need to pick up flowers no more than 48 hours in advance) (see Sim et al., Paragraph 0029). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although Gonzalez et al. discloses receiving a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (e.g., based on predetermined configuration such as a higher weight for weather and a lower weight for budget), Gonzalez et al. does not specifically disclose how the user is selecting the specific criteria using a chat interface.
However, Gruber et al. discloses receiving, via a chat interface, a member selection to organize messages based on specific criteria including a member-corrected ranking of a set of tasks (Paragraph 0719, Selection criteria may have an inherent preference order. That is, the values of any particular criterion may be used to line up items in a best first order. For example, the proximity criterion has an inherent preference that closer is better. Location, on the other hand, has no inherent preference value. This restriction allows the system to make default assumptions and guide the selection if the user only mentions the criterion. For example, the user interface might offer to "sort by rating" and assume that higher rated is better; Paragraph 0750, In one embodiment, there is a precedence ordering among selection criteria. That is, some criteria may matter more than others in the filter and sort. In one embodiment, those criteria selected by the user are given higher precedence than others, and there is a default ordering over one or more criteria; Paragraph 1007, Search criteria for identifying reservable items include a search class or selection class (e.g., restaurants, entertainment events, etc.), and various constraints (e.g., location, time, price, review, genre, cuisine, etc.) for filtering and ranking the search results; Examiner interprets “selecting a class and various constraints” as the “specific criteria including a member-corrected ranking of a set of tasks”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the machine learning algorithm used to determine a ranking and to generate a customized chat interface according to the member-corrected ranking of the set of tasks and a member profile (e.g., learning member preferences based on prior tasks selected by the member) of the invention of Gonzalez et al. to further incorporate how the user is selecting the specific criteria to organize messages via a chat interface (e.g., a filter in the chat interface) of the invention of Gruber et al. because doing so would allow the method to filter and rank search results based on a selection criteria provided in the user interface (see Gruber et al., Paragraphs 0719 & 1007). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 9, and 16 (Previously Presented), which are dependent of claims 1, 8, and 15, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1, 8, and 15. Gonzalez et al. further comprising processing a message history using a task recommendation algorithm and a set of user preferences; and automatically generating an approved task based on an output of the task recommendation algorithm and the set of user preferences (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. The cognitive process 131 may ask for clarification of the inputs from the user 110, or may make inferences regarding the nature of the inputs based on a user profile associated with the user 110, by use of the user profiler 135, or otherwise relevant data as adapted from past responses and other data sources by a large number of users, such that the intelligent travel planning system 130 may have more accurate information as to the intent of the user 110 represented by the inputs, and consequently, the intelligent travel planning system 130 may generate the result that suits the intent of the user 110 better than automatically generated search results based on inputs of simple queries as provided by conventional travel booking systems; 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 interprets “approved task” as the “recommendations that are based on learned user preferences”).
Regarding claims 3, 10, and 17 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1, 8, and 15. Gonzalez et al. further comprising: identifying a template associated with a plurality of task recommendations, wherein the plurality of task recommendations corresponds to the set of tasks (Paragraph 0023, The itinerary builder 137 incorporates personal preference information of the user 110 with reservation results from the travel booking systems 150 by use of the scoring process 139 in order to generate a customized itinerary based on the personal preference information of the user 110. In one embodiment of the present invention, the itinerary builder 137 may utilize a catalog database, which includes numerous travel options, including hotel packages and tour programs with descriptions, schedules, availability, as well as itinerary templates. The catalog database may be external to the intelligent travel planning system 130 and may be accessed by use of the external source coordinator 141, or directly by the itinerary builder 137);
identifying missing data elements from the template following processing of the set of ongoing messages (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);
processing additional messages to identify data associated with the missing data elements from the template (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; Examiner notes that the first task is to choose a location where the user can see the Cherry Blossoms);
and automatically generating a task recommendation message based on generation of a complete set of data for the template (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.; Examiner notes that after the intelligent travel collects all the necessary information from the user, the intelligent travel makes recommendations based on the received inputs from the user).
Regarding claims 4, 11, and 18 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1, 8, and 15. Gonzalez et al. further comprising: receiving additional real-time messages between the member and the representative as the additional real-time messages are being exchanged (Paragraph 0058, My sister also wants to go to Mt Fuji.” The intelligent travel planning system 130 responds by “The famous “Snow Walls” are formed by expelling the heavy snow in Murodo-daira of Tateyama . . . it reaches about seven meters (23 ft) on average . . . open April 16.sup.th”, and continues “But you already have another trip planned for April 13.sup.th-April 15.sup.th”, as the intelligent travel planning system 130 checks out purchase history of the user 110 and finds an overlap. The intelligent travel planning system 130 further informs “Mt. Fuji opens year round but Cherry Blossom season here begins in mid-April”, or provides information on alternative locations in southern region having characteristics similar to Mt. Fuji such that the user 110 may further explore the options to make an informed decision);
processing additional chat flow associated with the additional real-time messages to assign at least a task recommendation of a plurality of task recommendations to one or messages of the additional real-time messages, wherein the plurality of task recommendations corresponds to the set of tasks (Paragraph 0058, My sister also wants to go to Mt Fuji.” The intelligent travel planning system 130 responds by “The famous “Snow Walls” are formed by expelling the heavy snow in Murodo-daira of Tateyama . . . it reaches about seven meters (23 ft) on average . . . open April 16.sup.th”, and continues “But you already have another trip planned for April 13.sup.th-April 15.sup.th”, as the intelligent travel planning system 130 checks out purchase history of the user 110 and finds an overlap. The intelligent travel planning system 130 further informs “Mt. Fuji opens year round but Cherry Blossom season here begins in mid-April”, or provides information on alternative locations in southern region having characteristics similar to Mt. Fuji such that the user 110 may further explore the options to make an informed decision);
and dynamically annotating a message history based on processing of the one or more messages of the additional real-time messages (Paragraph 0059, At the conclusion of the travel planning session, the user 110 agrees not to add Snow Walls to the agenda, but nevertheless adds Mt. Fuji during her trip, and wishes to extend the trip up to ten (10) days. The intelligent travel planning system 130 keeps a record that the user 110 is interested in sightseeing Snow Walls such that the user 110 may be reminded of the option in future planning sessions; Examiner interprets “annotating” as “updating the record with new interest/preference of the user”).
Regarding claims 5, 12, and 19 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1, 8, and 15. Gonzalez et al. further comprising: receiving non-automatic association tags associating messages of the set of ongoing messages with tasks of the set of tasks; updating a message history for a plurality of task recommendations based on the non-automatic association tags, wherein the plurality of task recommendations corresponds to the set of tasks; processing the message history using a task recommendation algorithm; generating a task recommendation message based on an output of the task recommendation algorithm; receiving a response to the task recommendation message; and updating the machine learning algorithm using the message history and the response to the task recommendation message (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. The cognitive process 131 may ask for clarification of the inputs from the user 110, or may make inferences regarding the nature of the inputs based on a user profile associated with the user 110, by use of the user profiler 135, or otherwise relevant data as adapted from past responses and other data sources by a large number of users, such that the intelligent travel planning system 130 may have more accurate information as to the intent of the user 110 represented by the inputs, and consequently, the intelligent travel planning system 130 may generate the result that suits the intent of the user 110 better than automatically generated search results based on inputs of simple queries as provided by conventional travel booking systems; Examiner interprets “non-automatic association tags” as “learning how to associate the intent of the user with the recommendations based on clarifications provided from the user.” In this case, Examiner notes that by receiving feedback responses and clarifications from user, the machine learning or natural language learns the intent of the user).
Regarding claims 6, 13, and 20 (Previously Presented), which are dependent of claims 1, 8, and 15, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1, 8, and 15. Gonzalez et al. further comprising: receiving a task input associated with a first task of the set of tasks (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);
dynamically filtering a chat history to isolate messages associated with the first task (Paragraph 0053, 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; 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);
and displaying the messages associated with the first task from a message history as isolated from remaining messages of the chat history (Paragraph 0053, 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; 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; Examiner notes that Gonzalez et al. is only displaying the candidates for Cherry Blossom in March and April. In this case, Examiner interprets “isolated” as “filtering the recommendations provided to the user based on inputs and preferences obtained from the user”).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 2018/0053121 A1), in view of Sim et al. (US 2021/0373943 A1), in further view of Mattox et al. (US 10791212 B1) and Walters et al. (US 2022/0368665 A1).
Regarding claims 7 and 14 (Currently Amended), which are dependent of claims 1 and 8, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claims 1 and 8. Gonzalez et al. further comprising: receiving a reminder input for a first task (Paragraph 0017, a travel manager for following up a trip and for sending reminders to the user 110; Paragraph 0057, The intelligent travel planning system 130 keeps a record that the user 110 is interested in visiting Nagasaki such that the user 110 may be reminded of the option in future planning sessions; Paragraph 0060, A memo pad icon 440, representing notes of the user 110, may also be added to any day during the trip such that the user 110 may create a personalized travel log, add files, notes, links, activities, reminders, etc.);
selecting a position or timing for one or more reminders associated with the first task … (Paragraph 0017, a travel manager for following up a trip and for sending reminders to the user 110; Paragraph 0057, The intelligent travel planning system 130 keeps a record that the user 110 is interested in visiting Nagasaki such that the user 110 may be reminded of the option in future planning sessions; Paragraph 0060, A memo pad icon 440, representing notes of the user 110, may also be added to any day during the trip such that the user 110 may create a personalized travel log, add files, notes, links, activities, reminders, etc.); … within the chat interface (Paragraph 0052, In this embodiment of the present invention, the user device 120 is a smartphone and the web user interface 121 is a smartphone app. 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, 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).
Although the combination of Gonzalez et al. and Sim et al. discloses sending reminders to users to a chat interface (see Gonzalez et al., Paragraph 0060 reminders; see Sim et al., Paragraph 0020, notifications), Gonzalez et al. does not specifically disclose using a machine learning to select position or timing of future reminders.
However, Walters et al. discloses receiving a reminder input for a first task (Paragraph 0022, The notification subsystem 116 may determine a time to provide a notification for the message to the user device 104. The notification subsystem 116 may determine the time based on a comparison of the predicted response time one or more thresholds);
selecting a position or timing for one or more reminders associated with the first task based on a message history and the reminder input (Paragraph 0022, The notification subsystem 116 may determine a time to provide a notification for the message to the user device 104. The notification subsystem 116 may determine the time based on a comparison of the predicted response time one or more thresholds);
automatically inserting the one or more reminders within the chat interface using the position or timing for the one or more reminders (Paragraph 0001, In the last few years carrying a mobile computing device (e.g., smart phones, tablets, and others) has become a common practice among many people. These devices enable users to have quick and easy access to a variety of applications, including email, instant messaging, social media, and others; Paragraph 0024, In some embodiments, the notification subsystem 116 may determine to provide the notification at a time when the user is predicted to send a response with a target sentiment. For example, the notification subsystem 116 may determine to avoid providing a notification before 9:00 am because the user has previously sent responses associated with an angry sentiment identifier when notifications for similar messages were sent before 9:00 am. As an additional example, the notification subsystem 116 may determine to provide a notification during a lunch break because the notification system 102 has determined that notifications for similar messages have distracted (e.g., stopped the user from working for longer than a threshold period of time) the user in the past);
receiving a feedback indication associated with the one or more reminders and first task (Paragraph 0024, In some embodiments, the notification subsystem 116 may determine to provide the notification at a time when the user is predicted to send a response with a target sentiment. For example, the notification subsystem 116 may determine to avoid providing a notification before 9:00 am because the user has previously sent responses associated with an angry sentiment identifier when notifications for similar messages were sent before 9:00 am. As an additional example, the notification subsystem 116 may determine to provide a notification during a lunch break because the notification system 102 has determined that notifications for similar messages have distracted (e.g., stopped the user from working for longer than a threshold period of time) the user in the past. The notification subsystem 116 may use a machine learning model (e.g., as discussed in connection with FIGS. 4A-4B) to determine when to provide a notification so that a target sentiment of a response may be achieved. The notification subsystem 116 may determine, for a first time period (e.g., between 1 pm and 3 pm), a first sentiment identifier predicted to correspond to a response from the user device, if the notification is sent during the first time period. For example. the notification subsystem 116 may use a machine learning model to determine that there is an increased likelihood that a user will send a happy response. if a notification is received at a particular time. Based on a determination that the sentiment identifier matches a target sentiment identifier (e.g., the predicted sentiment identifier of the response is happy, positive, kind. etc.), the notification subsystem 116 may determine the time to provide the message notification based on the first time period (e.g., between 1 pm and 3 pm));
and updating the machine learning algorithm using the feedback indication, wherein the machine learning algorithm is trained using the feedback indication and the position or timing of the one or more reminders to update selection of position or timing of future reminders within the chat interface (Paragraph 0001, In the last few years carrying a mobile computing device (e.g., smart phones, tablets, and others) has become a common practice among many people. These devices enable users to have quick and easy access to a variety of applications, including email, instant messaging, social media, and others; Paragraph 0024, In some embodiments, the notification subsystem 116 may determine to provide the notification at a time when the user is predicted to send a response with a target sentiment. For example, the notification subsystem 116 may determine to avoid providing a notification before 9:00 am because the user has previously sent responses associated with an angry sentiment identifier when notifications for similar messages were sent before 9:00 am. As an additional example, the notification subsystem 116 may determine to provide a notification during a lunch break because the notification system 102 has determined that notifications for similar messages have distracted (e.g., stopped the user from working for longer than a threshold period of time) the user in the past. The notification subsystem 116 may use a machine learning model (e.g., as discussed in connection with FIGS. 4A-4B) to determine when to provide a notification so that a target sentiment of a response may be achieved. The notification subsystem 116 may determine, for a first time period (e.g., between 1 pm and 3 pm), a first sentiment identifier predicted to correspond to a response from the user device, if the notification is sent during the first time period. For example. the notification subsystem 116 may use a machine learning model to determine that there is an increased likelihood that a user will send a happy response. if a notification is received at a particular time. Based on a determination that the sentiment identifier matches a target sentiment identifier (e.g., the predicted sentiment identifier of the response is happy, positive, kind. etc.), the notification subsystem 116 may determine the time to provide the message notification based on the first time period (e.g., between 1 pm and 3 pm); Paragraph 0026, The communication subsystem 112 may receive, from the user device and/or the user, feedback information that may be used to improve response time predictions. The feedback may be used, for example, to adjust (e.g., train) the sentiment detection model, the urgency detection model, the embedding model, and/or the response prediction model. The feedback information may indicate a preferred time for receiving the message notification. For example, the user may indicate, via the user device, that the notification should have been sent earlier, later, or at a time specified by the user (e.g., during a lunch break, after 5 pm, on the weekend, etc.)).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for identifying and providing one or more task recommendations in a chat interface of the invention of Gonzalez et al. to further incorporate using a machine learning for selecting a position or timing for the one or more reminders associated with the first task based on the message history and the reminder input of the invention of Walters et al. because doing so would allow the method determine when to provide a notification so that a target sentiment of a response may be achieved (see Walters et al., Paragraph 0024). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 2018/0053121 A1), in view of Sim et al. (US 2021/0373943 A1), in further view of Gruber et al. (US 2013/0275164 A1) and Thyagarajan et al. (US 11,122,165 B1).
Regarding claim 21 (Currently Amended), which is dependent of claim 1, the combination of Gonzalez et al., Sim et al., and Gruber et al. discloses all the limitations in claim 1. Gonzalez et al. further comprising receiving a … task input associated with a … task of the set of tasks; dynamically filtering a message history to isolate .. task messages associated with the … task; and displaying the … task messages in a … chat interface, …, and wherein the … task messages in the … chat interface are isolated from remaining messages of the message history (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 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; Paragraph 0021, The user profiler 135 also records characteristics and preferences of registered users in respective user profiles, as identified by the cognitive process 131 during interactions with the registered users. The user profile may further include membership numbers for various travel programs and travel preferences as input by the user 110. By use of the user profiles, the intelligent travel planning system 130 may provide personalized travel planning services for the registered users, in contrast to conventional travel booking systems making the same recommendations to all based on general assumptions on traveler preferences; 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 0025, The scoring process 139 of the intelligent travel planning system 130 weighs sources of the personal preference information of the user 110 to reduce the number of available travel selections by investigating only the high-scored options that are best suited for the personal preference of the user 110; 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.; Examiner notes that the messages provided in the chat interface are filtered based on learned user preferences. Also, the intelligent travel planning system provides further recommendations to the member for other tasks based on requests from the member).
Although Gonzalez et al. discloses a chat interface used to receive task inputs and provide task recommendations based on the inputs (e.g., provide recommendations for a travel trip based on user preferences, date of travel, and budget), Gonzalez et al. does not specifically disclose a second chat interface, wherein the second chat interface is distinct from and directly navigable from the chat interface.
However, Thyagarajan et al. discloses receiving a second task input associated with a second task of the set of tasks; dynamically filtering a message history to isolate second task messages associated with the second task; and displaying the second task messages in a second chat interface, wherein the second chat interface is distinct from and directly navigable from the chat interface, and wherein the second task messages in the second chat interface are isolated from remaining messages of the message history (Column 10, lines 62-67 & Column 11, lines 1-13, In the example shown in FIG. 7, a snapshot of the chat session shown in chat window field 705 corresponds to exchanged messages corresponding to two chat flows associated with the determined intents, “device upgrade” and “international travel.” A yet-to-be responded to chat message 715 from the customer includes “Flying to Paris this Friday.” Predictive feed app field 710 includes a menu including the unexpanded “device upgrade” chat flow 720 and the expanded “international travel” chat flow 725. The agent assist tool includes a suggested response message 730 from the CSC agent, “Awesome! Do you know what date you'll be returning to the US?” For example, bot intel unit 430 may have determined that one or more additional elements of information, such as a return date, is needed to develop a Travel Plan. Suggested response message 730 may also include GUI objects 735 (e.g., buttons) that are configured to allow the user to select a date from a calendar or to indicate the customer's uncertainty regarding a return date, etc.; Column 12, lines 4-11, Additional implementations described herein provide for a conversational AI platform that enables one service agent to drive multiple customer transactions concurrently; obviates the need for the service agent to consult multiple internal/external websites; provides for prompts to customers for providing entity information that may be extracted and used to inform a chat flow; and auto-populate suggested response messages that are improvised by the service agent).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the first chat interface used to provide a set of tasks associated with an intent of the user (e.g., based on the intent of the user to plan a trip to Paris or Japan) of the invention of Gonzalez et al. to further incorporate a second chat interface distinct from the first chat interface of the invention of Thyagarajan et al. because doing so would allow the method to exchanged messages corresponding to two chat flows associated with the determined intents, “device upgrade” and “international travel” (see Thyagarajan et al., Column 10, lines 62-67). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Suhaili (Suhaili, S.M., Salim, N. and Jambli, M.N., 2021. Service chatbots: A systematic review. Expert Systems with Applications, 184, p.115461) – discloses a Slot filling (Entity extractor). Determining a user’s intent does not stop at the stage of identifying the intention. Indeed, to better represent the information collected from the request, it is necessary to extract entities (named or specific of the domain) that would be the arguments or constraints of the identified intent, also known as slot filling. Slot filling is primarily concerned with extracting the relevant information from the user query; this task, also called entity extraction, is required by the system to process the query further. For example, in a restaurant reservation scenario, given the sentence, “Are there any French restaurants in downtown Toronto?” as an input, the task is to correctly output, or fill, the following slots: (cuisine: French) and (location: downtown Toronto). Slot filling is generally considered a sequence tagging problem; herein, each relevant word token is tagged with the respective slot name (see at least Page 4, 2.2.2.1. Intent detection & 2.2.2.2. Slot filling).
Mattox et al. (US 10, 791, 212 B1) – discloses when a plurality of resources with recommendations for the event type are identified, the electronic concierge application and/or service may utilize the recommendations in those resources to generate one or more itinerary templates for the significant life event. Those one or more itinerary templates may then be surfaced to assist users with event planning. In some examples, the itinerary templates, and elements included therein, may be interacted with to further assist with event planning. For example, individual elements of an itinerary template may be added to an electronic calendar application and/or a task completion application. In other examples, the recommendation elements included in an itinerary template may include additional details that can be drilled down into and/or linked to the source to allow users to research a recommendation in more detail (see Column 3, liens 31-45).
JUNGSUNGJUN et al. (KR 2020/0114939 A) - discloses an interactive chatbot module 110 may configure a recommendation algorithm by learning information such as host and guest information, requests, selection results, and satisfaction with the selection results through machine learning. The interactive chatbot module 110 may provide optimized recommendation information to a host or a guest by using a recommendation algorithm configured based on machine learning in conjunction with a service brokering module 130 to be described in detail later. Here, the recommendation algorithm may use collaborative filtering that utilizes collective intelligence for recommendation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia H Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624
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