DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1, 3-8, 10-15, and 17-24 are currently pending and have been addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/02/25 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term "similar" in claims 1, 8, and 15 is a relative term which renders the claim indefinite. The term "similar" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For selecting a template, a similar template may be a template associated to a specific task type (see Applicant’s specification, Paragraph 0084). For examination purposes the term “similar” has been construed to be a template associated to a specific task type. Examiner recommends to change “similar proposals” to “proposals associated to a task type.”
Claims 3-7, 10-14, and 17-24 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 15.
Response to Arguments
Applicant's arguments filed on 12/02/2025 (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 12/02/2025 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on page 11, that withdrawal of the rejection is respectfully requested.
Examiner respectfully disagrees with Applicant. Claims 1, 3-8, 10-15, and 17-24 are still directed to a judicial exception (e.g., abstract idea) 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, generating one or more proposal tasks based on previous selections is a social activity (see MPEP 2106.04(a)(2)). 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 such as: a computer; a representative interface to display a set of representative interface elements; a representative computing device; a member interface; a member computing device; a proposal task generation machine learning algorithm; and a template generation option. The computer is merely used to execute instructions (Paragraph 0010). The set of representative interface elements is merely used to obtain information from the representative and to provide the obtained information to systems of the task facilitation service, to the member, to a third-party service or other service/entity affiliated with the task facilitation service, and/or to other information subscribers (Paragraph 0244). The member interface is merely used to present questions to the member and receive a response from the member (Paragraph 0057). The representative computing device and the member computing device are merely used to facilitate communications between the representative and the member (Paragraph 0051). The proposal task generation machine learning is merely used to generate a set of proposal tasks that may be performed to complete the project or task according to the set of accepted and/or partially accepted proposals (Paragraph 0207). The template generation option is merely used to generate a proposal template for a particular task type (Paragraphs 0076 & 0084). These elements of “computer,” “representative interface,” “representative computing device,” “member interface,” “member computing device” “proposal task generation machine learning algorithm,” and “template generation option” 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 proposal task generation machine learning algorithm includes inputs (e.g., a dataset of project parameters corresponding to previously provided proposals and proposal tasks previously performed to complete different projects) and outputs (e.g., generate a set of proposal tasks). Although the proposal task generation machine learning algorithm further receives updated messages over time (e.g., feedback corresponding to the one or more actions), the claim and specification do not include any specific details about how the proposal task generation machine learning algorithm operates (see 2024 AI Guidance, Example 47). Thus, the training step is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)). Lastly, the interface elements (e.g., member interface, representative interface, and the template generation option) are considered “field of use” (MPEP 2106.05h) as they’re just used to display messages and receive a response, but the interface is not improved.
Step 2B - The proposal task generation machine learning algorithm does not provide any specific details of how the set of proposals are generated, which results in “apply it.” The step of “retraining the proposal task generation machine learning algorithm based on the feedback and the proposal template” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05d). Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). In this case, the user interface is merely used to arrange information (e.g., present one or more proposal tasks) in a manner that assists users in processing information more quickly, which is not sufficient to show an improvement in computer functionality (see 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 3-7, 10-14, and 17-24 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 15.
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, 3-8, 10-15, and 17-24 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: updating a representative to display a set of representative elements associated with a set of proposals, wherein the set of proposals corresponds to different tasks performable on behalf of a member, wherein the set of proposals is automatically generated based on a set of project parameters and a member profile, and wherein the representative is updated; monitoring representative interaction to detect selection of a subset of proposals from the set of proposals; updating a member to display the subset of proposals and a set of member elements associated with the subset of proposals, wherein the member is updated, and wherein the member and the representative are distinct; monitoring member interactions with the set of member elements to detect acceptance of a proposal from the subset of proposals; processing the set of project parameters and the proposal through a proposal task generation to generate one or more proposal tasks performable according to the proposal, wherein the proposal task generation is trained using a dataset of different project parameters corresponding to previously provided proposals and previously performed proposal tasks; updating the representative to display one or more new representative elements corresponding to the proposal, wherein the one or more new representative elements include a template generation associated with the particular proposal, and wherein when the template generation is selected, a proposal template is created for generating similar proposals and similar proposal tasks; continuously monitoring messages exchanged between the member and a representative to obtain feedback corresponding to performance of the one or more proposal tasks; and retraining the proposal task generation based on the feedback and the proposal template. 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, generating one or more proposal tasks based on previous selections is a social activity (see MPEP 2106.04(a)(2)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “method 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 representative interface to display a set of representative interface elements; a representative computing device; a member interface; a member computing device; and a proposal task generation machine learning algorithm; and a template generation option.
The computer is merely used to execute instructions (Paragraph 0010). The representative interface including a set of representative interface elements is merely used to obtain information from the representative and to provide the obtained information to systems of the task facilitation service, to the member, to a third-party service or other service/entity affiliated with the task facilitation service, and/or to other information subscribers (Paragraph 0244). The member interface is merely used to present questions to the member and receive a response from the member (Paragraph 0057). The representative computing device and the member computing device are merely used to facilitate communications between the representative and the member (Paragraph 0051). The proposal task generation machine learning is merely used to generate a set of proposal tasks that may be performed to complete the project or task according to the set of accepted and/or partially accepted proposals (Paragraph 0207). The template generation option is merely used to generate a proposal template for a particular task type (Paragraphs 0076 & 0084). 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,” “representative interface,” “representative computing device,” “member interface,” “member computing device” “proposal task generation machine learning algorithm,” and “template generation option” 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 interface elements (e.g., member interface, representative interface, and the template generation option) are considered “field of use” (MPEP 2106.05h) as they’re just used to display messages and receive a response, 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 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 generating and updating one or more proposal tasks based on feedback corresponding to performance of the one or more proposal tasks. The specification shows that the computer is merely used to execute instructions (Paragraph 0010). The representative interface including a set of representative interface elements is merely used to obtain information from the representative and to provide the obtained information to systems of the task facilitation service, to the member, to a third-party service or other service/entity affiliated with the task facilitation service, and/or to other information subscribers (Paragraph 0244). The member interface is merely used to present questions to the member and receive a response from the member (Paragraph 0057). The representative computing device and the member computing device are merely used to facilitate communications between the representative and the member (Paragraph 0051). The proposal task generation machine learning is merely used to generate a set of proposal tasks that may be performed to complete the project or task according to the set of accepted and/or partially accepted proposals (Paragraph 0207). The template generation option is merely used to generate a proposal template for a particular task type (Paragraphs 0076 & 0084). Although the one or more proposal tasks are generated using a proposal task generation machine learning algorithm, the claim and specification do not include any specific details about how the proposal task generation machine learning algorithm operates (see 2024 AI Guidance, Example 47). Also, the step of “retraining the proposal task generation machine learning algorithm based on the feedback and the proposal template” is considered a well-understood, routing, and conventional function since it’s just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05d). Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). In this case, the user interface is merely used to arrange information (e.g., present one or more proposal tasks) in a manner that assists users in processing information more quickly, which is not sufficient to show an improvement in computer functionality (see 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 processor; and a memory. The processor is merely used to execute instructions (Paragraph 0010). The memory is merely used to store instructions (Paragraph 0010). These elements of “processor” and “memory” are treated as just an explicit “computer component” for executing the operations (see MPEP 2106.05f). Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Therefore, the claim is not patent eligible.
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 computer-readable storage medium; and a computer system. The computer system is merely used to execute instructions (Paragraph 0010). The computer-readable storage medium is merely used to store instructions (Paragraph 0010). These elements of “computer-readable storage medium” and “computer system” are treated as just an explicit “computer component” for executing the operations (see MPEP 2106.05f). Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Therefore, the claim is not patent eligible.
Dependent claims 3-7, 10-14, and 17-21 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as: additional information communicated between a member and a representative; how the vendors are selected based on prior performance of other projects similar to the project; wherein the response includes a selection of a vender; and wherein the response indicates a rejection. 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 “certain methods of organizing human activity” which include “managing personal behavior.” Also, the additional functions are considered “field of use” (MPEP 2106.05h) at step 2A, Prong 2; as they are just used to exchange information, but the technology is not improved. At Step 2B, those functions are considered a conventional computer function of “receiving or transmitting data over a network” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 21-24 are directed to additional elements such as: a button. The button is merely used to save the proposal as a template and subsequent use the template by the representative (Paragraph 0250). This is considered “field of use” (MPEP 2106.05h) at step 2A, Prong 2; as it’s just used to select information, but does not improve the interface. At Step 2B, this is considered a conventional computer function of “receiving or transmitting data over a network” and “storing information in a memory” (MPEP 2106.05d). 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, 3-8, 10-15, and 17-24 are rejected under 35 U.S.C. 103 as being unpatentable over Yusuf et al. (US 2023/0036167 A1), in view of Zhang et al. (US 2018/0054523 A1).
Regarding claim 1 (Currently Amended), Yosuf et al. discloses a computer-implemented method, comprising (Paragraph 0005, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers; See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0014):
updating a representative interface to display a set of representative interface elements, associated with a set of proposals, wherein the set of proposals corresponds to different tasks performable on behalf of a member, wherein the set of proposals is automatically generated based on a set of project parameters and a member profile (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer. The proposal may include information automatically generated by the agent responder system with or without agent's edits. In some cases, the information may include objective information such as the selected recommendation of a service meeting the customer request and the predicted customer intent, and subjective information such as insights and implicit customer preference extracted from customer data. The agent responder system 121 may assist and guide an agent to complete a proposal in a streamlined fashion via an agent responder user interface, and deliver the proposal to the customer via a customer user interface (e.g., in-app messaging, chat bot, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025), and wherein the representative interface is updated through a representative computing device (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025);
monitoring representative interaction with the set of representative interface elements to detect selection of a subset of proposals from the set of proposals (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079);
updating a member interface to display the subset of proposals and a set of member interface elements associated with the subset of proposals, wherein the member interface is updated through a member computing device (Paragraph 0047, In some embodiments, a user (e.g., agent) 103-1, 103-2 may be associated with one or more user devices 101-1, 101-2, 101-3. In some cases, a user (e.g., agent) may communicate with customers using a user device. For example, the user 103-1 may receive one or more customer requests, receive one or more auto-generated recommendations, edit and customize recommendations or proposals within an agent responder user interface rendered on the user device 101-1. The user 103-1 may also communicate with the customer via an instant communication channel running on the user device 101-1. The instant communication channel may be provided in a customer software or customer user interface provided by the platform 100; Paragraph 0051, The GUI may show customer requests, recommendation and images, interactive elements relating to a proposal or customer request (e.g., editable fields, tasks status, customer feedback, etc.); Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0029, 0033, and 0101), and wherein the member interface and the representative interface are distinct (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 and 0101);
monitoring member interactions with the set of member interface elements to detect acceptance of a proposal from the subset of proposals (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; Examiner notes that in Figure 15, the member accepted Ella Canta from the subset of proposals; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101);
processing the set of project parameters and the proposal through a proposal task generation machine learning algorithm to generate one or more proposal tasks performable according to the proposal (Paragraph 0031, Current virtual assistant or digital assistant systems may not be able to provide satisfactory user experience as a customer is usually requested to provide detailed inputs such as location, date, time, restaurant name, so that a digital assistant perform the search. The present disclosure provides a concierge network addressing the above needs by leveraging human agents knowledge, machine learning-based auto-processing and insight extraction; Paragraph 0032, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers. Moreover, the provided system allows for live communication experience with customers, and offers recommendations, booking, transaction, and fulfillment through an integrated platform in a convenient manner; Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.). A user of the provided concierge network or platform may also include individual customers who seek of personalized services such as travel, dining, concerts, trips, activities, events tailored to the customer that the personalized service may not be readily available or easily accessed by performing a search; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014 & 0016), wherein the proposal task generation machine learning algorithm is trained using a dataset of different project parameters corresponding to previously provided proposals and previously performed proposal tasks (Paragraph 0036, The provided systems may employ artificial intelligence techniques to analyze customer request to extract data points, triage the requests based on the extracted data points, generate recommended proposals with editable fields and insight data extracted from customer feedback, and guide human assistant to customize the recommended proposals in an optimized flow. In some cases, personalized feedback survey may also be generated using artificial intelligence techniques. Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0018));
updating the representative interface to display one or more new representative interface elements corresponding to the proposal, wherein the one or more new representative interface elements include a [input field] generation option associated with the particular proposal (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079), and wherein when the [input field] generation option is selected, a proposal … is created for generating similar proposals and similar proposal tasks (Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.); Paragraph 0075, In some embodiments, the machine learning module 201 may be configured to train one or more predictive models. The one or more predictive models may be trained to process customer request data, generate personalized recommendations, and various other functions described herein. In some cases, the input data to the one or more predictive models may comprise customer request about a service. The customer request may include limited information such as a request of concierge-type service. For instance, the request may not include all the information sufficient for performing search of a service by a conventional digital assistant system; Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0016, 0057, & 0059);
continuously monitoring messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, and 0094);
and retraining the proposal task generation machine learning algorithm based on the feedback and the proposal [fields] (Paragraph 0036, Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0018 & 0059).
Yosuf et al. discloses: displaying one or more proposal tasks to a member and/or a representative interface; selecting a service type using a member interface (see Yosuf et al., Paragraph 0075, The customer request may include limited information such as a request of concierge-type service); and providing one or more proposal tasks based on the member selected service type (see Yosuf et al., Paragraph 0077, providing a personalized predictive model for a service or a type of service). Although the Yosuf et al. discloses all the limitations above and providing one or more proposal tasks for a specify type of service, Yosuf et al. does not specifically disclose a proposal template for each type of service.
However, Zhang et al. discloses updating the [developer] interface to display one or more new [developer] interface elements corresponding to the proposal, wherein the one or more new [developer] interface elements include a template generation option associated with the particular proposal, and wherein when the template generation option is selected, a proposal template is created for generating similar proposals and similar proposal tasks (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction; Paragraph 0148, According to one embodiment of the present teaching, the visual input based program integrator 1012 may store the customized virtual agent as a template, and retrieve the template from the customized task database 139 when a developer is developing a different but similar virtual agent. In this case, the bot design programming interface manager 1002 may present the template to the developer via another bot design programming interface, such that the developer can directly modify the template, e.g. by modifying some parameters, instead of selecting and building all modules of the virtual agent from beginning);
continuously monitoring messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks; and retraining the proposal task generation machine learning algorithm based on the feedback and the proposal template (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. More specifically, based on machine learning and AI technique, the disclosed system can learn how to strategically ask user questions, present intermediate candidates to the users based on historical human-human or human-machine or machine-machine conversation data, together with human or machine action data that involves calling third party applications, services or databases. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction. Moreover, the disclosed system can provide more effective products/services recommendations in the conversation by using not only user transaction history and user demographic information that are normally used in traditional recommendation engines, but also additional contextual information about the user needs, such as possible user initial request (i.e. a user query) or supplemental information collected while talking with the user. The disclosed system is also capable of using those information as well as users' implicit feedback signals (such as clicks and conversions) when interacting with our recommendation results to more effectively learn users' interests, persuade them for certain conversions, collect their explicit feedback (such as rating), as well as actively solicit additional sophisticated user feedback such as their suggestions for future product/service improvement; Paragraph 0056, The virtual agent development engine 170 may also store the customized tasks into the customized task database 139, which can provide previously generated tasks as a template for future task generation or customization during virtual agent development; Examiner notes that the initial template may be modified over time based on new users’ interests and/or suggestions for future product/service improvement).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for using a proposal task generation machine learning algorithm to generate one or more proposal tasks performable based on a set of project parameters and a member profile (e.g., type of service and member preferences), wherein the one or more proposal tasks are displayed to a representative interface of the invention of Yosuf et al. to further specify a template for similar proposals and similar proposal tasks of the invention of Zhang et al. because doing so would allow the method to provide previously generated tasks for a specific type of service as a template for future task generation (see Yosuf et al., Paragraphs 0056 & 0129, a virtual agent for booking a flight). 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), Yosuf et al. discloses a system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to (Paragraph 0005, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers; Paragraph 0015, Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014 & 0043):
update a representative interface to display a set of representative interface elements, associated with a set of proposals, wherein the set of proposals corresponds to different tasks performable on behalf of a member, wherein the set of proposals is automatically generated based on a set of project parameters and a member profile (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer. The proposal may include information automatically generated by the agent responder system with or without agent's edits. In some cases, the information may include objective information such as the selected recommendation of a service meeting the customer request and the predicted customer intent, and subjective information such as insights and implicit customer preference extracted from customer data. The agent responder system 121 may assist and guide an agent to complete a proposal in a streamlined fashion via an agent responder user interface, and deliver the proposal to the customer via a customer user interface (e.g., in-app messaging, chat bot, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025), and wherein the representative interface is updated through a representative computing device (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025);
monitor representative interaction with the set of representative interface elements to detect selection of a subset of proposals from the set of proposals (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079);
update a member interface to display the subset of proposals and a set of member interface elements associated with the subset of proposals, wherein the member interface is updated through a member computing device (Paragraph 0047, In some embodiments, a user (e.g., agent) 103-1, 103-2 may be associated with one or more user devices 101-1, 101-2, 101-3. In some cases, a user (e.g., agent) may communicate with customers using a user device. For example, the user 103-1 may receive one or more customer requests, receive one or more auto-generated recommendations, edit and customize recommendations or proposals within an agent responder user interface rendered on the user device 101-1. The user 103-1 may also communicate with the customer via an instant communication channel running on the user device 101-1. The instant communication channel may be provided in a customer software or customer user interface provided by the platform 100; Paragraph 0051, The GUI may show customer requests, recommendation and images, interactive elements relating to a proposal or customer request (e.g., editable fields, tasks status, customer feedback, etc.); Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0029, 0033, and 0101), and wherein the member interface and the representative interface are distinct (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 and 0101);
monitor member interactions with the set of member interface elements to detect acceptance of a proposal from the subset of proposals (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; Examiner notes that in Figure 15, the member accepted Ella Canta from the subset of proposals; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101);
process the set of project parameters and the proposal through a proposal task generation machine learning algorithm to generate one or more proposal tasks performable according to the proposal (Paragraph 0031, Current virtual assistant or digital assistant systems may not be able to provide satisfactory user experience as a customer is usually requested to provide detailed inputs such as location, date, time, restaurant name, so that a digital assistant perform the search. The present disclosure provides a concierge network addressing the above needs by leveraging human agents knowledge, machine learning-based auto-processing and insight extraction; Paragraph 0032, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers. Moreover, the provided system allows for live communication experience with customers, and offers recommendations, booking, transaction, and fulfillment through an integrated platform in a convenient manner; Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.). A user of the provided concierge network or platform may also include individual customers who seek of personalized services such as travel, dining, concerts, trips, activities, events tailored to the customer that the personalized service may not be readily available or easily accessed by performing a search; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014 & 0016), wherein the proposal task generation machine learning algorithm is trained using a dataset of different project parameters corresponding to previously provided proposals and previously performed proposal tasks (Paragraph 0036, The provided systems may employ artificial intelligence techniques to analyze customer request to extract data points, triage the requests based on the extracted data points, generate recommended proposals with editable fields and insight data extracted from customer feedback, and guide human assistant to customize the recommended proposals in an optimized flow. In some cases, personalized feedback survey may also be generated using artificial intelligence techniques. Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0018));
update the representative interface to display one or more new representative interface elements corresponding to the proposal, wherein the one or more new representative interface elements include a [input field] generation option associated with the proposal (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079), and wherein when the [input field] generation option is selected, a proposal … is created for generating similar proposals and similar proposal tasks (Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.); Paragraph 0075, In some embodiments, the machine learning module 201 may be configured to train one or more predictive models. The one or more predictive models may be trained to process customer request data, generate personalized recommendations, and various other functions described herein. In some cases, the input data to the one or more predictive models may comprise customer request about a service. The customer request may include limited information such as a request of concierge-type service. For instance, the request may not include all the information sufficient for performing search of a service by a conventional digital assistant system; Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0016, 0057, & 0059);
continuously messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, and 0094);
and retrain the proposal task generation machine learning algorithm based on the feedback and the proposal [fields] (Paragraph 0036, Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0018 & 0059).
Yosuf et al. discloses to: display one or more proposal tasks to a member and/or a representative interface; select a service type using a member interface (see Yosuf et al., Paragraph 0075, The customer request may include limited information such as a request of concierge-type service); and provide one or more proposal tasks based on the member selected service type (see Yosuf et al., Paragraph 0077, provide a personalized predictive model for a service or a type of service). Although the Yosuf et al. discloses all the limitations above and to provide one or more proposal tasks for a specify type of service, Yosuf et al. does not specifically disclose a proposal template for each type of service.
However, Zhang et al. discloses to: update the [developer] interface to display one or more new [developer] interface elements corresponding to the proposal, wherein the one or more new [developer] interface elements include a template generation option associated with the particular proposal, and wherein when the template generation option is selected, a proposal template is created for generating similar proposals and similar proposal tasks (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction; Paragraph 0148, According to one embodiment of the present teaching, the visual input based program integrator 1012 may store the customized virtual agent as a template, and retrieve the template from the customized task database 139 when a developer is developing a different but similar virtual agent. In this case, the bot design programming interface manager 1002 may present the template to the developer via another bot design programming interface, such that the developer can directly modify the template, e.g. by modifying some parameters, instead of selecting and building all modules of the virtual agent from beginning);
continuously messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks; and retrain the proposal task generation machine learning algorithm based on the feedback and the proposal template (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. More specifically, based on machine learning and AI technique, the disclosed system can learn how to strategically ask user questions, present intermediate candidates to the users based on historical human-human or human-machine or machine-machine conversation data, together with human or machine action data that involves calling third party applications, services or databases. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction. Moreover, the disclosed system can provide more effective products/services recommendations in the conversation by using not only user transaction history and user demographic information that are normally used in traditional recommendation engines, but also additional contextual information about the user needs, such as possible user initial request (i.e. a user query) or supplemental information collected while talking with the user. The disclosed system is also capable of using those information as well as users' implicit feedback signals (such as clicks and conversions) when interacting with our recommendation results to more effectively learn users' interests, persuade them for certain conversions, collect their explicit feedback (such as rating), as well as actively solicit additional sophisticated user feedback such as their suggestions for future product/service improvement; Paragraph 0056, The virtual agent development engine 170 may also store the customized tasks into the customized task database 139, which can provide previously generated tasks as a template for future task generation or customization during virtual agent development; Examiner notes that the initial template may be modified over time based on new users’ interests and/or suggestions for future product/service improvement).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for using a proposal task generation machine learning algorithm to generate one or more proposal tasks performable based on a set of project parameters and a member profile (e.g., type of service and member preferences), wherein the one or more proposal tasks are displayed to a representative interface of the invention of Yosuf et al. to further specify a template for similar proposals and similar proposal tasks of the invention of Zhang et al. because doing so would allow the method to provide previously generated tasks for a specific type of service as a template for future task generation (see Yosuf et al., Paragraphs 0056 & 0129, a virtual agent for booking a flight). 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), Yosuf et al. discloses a non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by a computer system, cause the computer system to (Paragraph 0005, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers; Paragraph 0015, Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein; Paragraph 0065, The processor(s) can be a single or multiple microprocessors, field programmable gate arrays (FPGAs), or digital signal processors (DSPs) capable of executing particular sets of instructions. Computer-readable instructions can be stored on a tangible non-transitory computer-readable medium; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014, 0043, & 0047):
update a representative interface to display a set of representative interface elements, associated with a set of proposals, wherein the set of proposals corresponds to different tasks performable on behalf of a member, wherein the set of proposals is automatically generated based on a set of project parameters and a member profile (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer. The proposal may include information automatically generated by the agent responder system with or without agent's edits. In some cases, the information may include objective information such as the selected recommendation of a service meeting the customer request and the predicted customer intent, and subjective information such as insights and implicit customer preference extracted from customer data. The agent responder system 121 may assist and guide an agent to complete a proposal in a streamlined fashion via an agent responder user interface, and deliver the proposal to the customer via a customer user interface (e.g., in-app messaging, chat bot, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025), and wherein the representative interface is updated through a representative computing device (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0025);
monitor representative interaction with the set of representative interface elements to detect selection of a subset of proposals from the set of proposals (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079);
update a member interface to display the subset of proposals and a set of member interface elements associated with the subset of proposals, wherein the member interface is updated through a member computing device (Paragraph 0047, In some embodiments, a user (e.g., agent) 103-1, 103-2 may be associated with one or more user devices 101-1, 101-2, 101-3. In some cases, a user (e.g., agent) may communicate with customers using a user device. For example, the user 103-1 may receive one or more customer requests, receive one or more auto-generated recommendations, edit and customize recommendations or proposals within an agent responder user interface rendered on the user device 101-1. The user 103-1 may also communicate with the customer via an instant communication channel running on the user device 101-1. The instant communication channel may be provided in a customer software or customer user interface provided by the platform 100; Paragraph 0051, The GUI may show customer requests, recommendation and images, interactive elements relating to a proposal or customer request (e.g., editable fields, tasks status, customer feedback, etc.); Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0029, 0033, and 0101), and wherein the member interface and the representative interface are distinct (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 and 0101);
monitor member interactions with the set of member interface elements to detect acceptance of a proposal from the subset of proposals (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; Examiner notes that in Figure 15, the member accepted Ella Canta from the subset of proposals; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101);
process the set of project parameters and the proposal through a proposal task generation machine learning algorithm to generate one or more proposal tasks performable according to the proposal (Paragraph 0031, Current virtual assistant or digital assistant systems may not be able to provide satisfactory user experience as a customer is usually requested to provide detailed inputs such as location, date, time, restaurant name, so that a digital assistant perform the search. The present disclosure provides a concierge network addressing the above needs by leveraging human agents knowledge, machine learning-based auto-processing and insight extraction; Paragraph 0032, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers. Moreover, the provided system allows for live communication experience with customers, and offers recommendations, booking, transaction, and fulfillment through an integrated platform in a convenient manner; Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.). A user of the provided concierge network or platform may also include individual customers who seek of personalized services such as travel, dining, concerts, trips, activities, events tailored to the customer that the personalized service may not be readily available or easily accessed by performing a search; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014 & 0016), wherein the proposal task generation machine learning algorithm is trained using a dataset of different project parameters corresponding to previously provided proposals and previously performed proposal tasks (Paragraph 0036, The provided systems may employ artificial intelligence techniques to analyze customer request to extract data points, triage the requests based on the extracted data points, generate recommended proposals with editable fields and insight data extracted from customer feedback, and guide human assistant to customize the recommended proposals in an optimized flow. In some cases, personalized feedback survey may also be generated using artificial intelligence techniques. Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0018));
update the representative interface to display one or more new representative interface elements corresponding to the proposal, wherein the one or more new representative interface elements include a [input field] generation option associated with the proposal (Paragraph 0043, In some embodiments, the recommendations may be provided to an agent via a graphical user interface (GUI) on a user device 101-1, 101-2, 101-3 for the agent to select and/or further customize one or more selected recommendation as proposal(s) to the customer; Paragraph 0097, The one or more recommended proposals may be provided to an agent via an interactive graphical user interface such that the agent is prompted to edit one or more fields of the proposals. The proposal may include information such as the details of the service (e.g., time, location, hotel room, restaurant seat, etc.) and personalized insights (e.g., recommended dishes, events). The user (e.g., agent) may be permitted to modify one or more fields of the recommended proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0025 & 0079), and wherein when the [input field] generation option is selected, a proposal … is created for generating similar proposals and similar proposal tasks (Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.); Paragraph 0075, In some embodiments, the machine learning module 201 may be configured to train one or more predictive models. The one or more predictive models may be trained to process customer request data, generate personalized recommendations, and various other functions described herein. In some cases, the input data to the one or more predictive models may comprise customer request about a service. The customer request may include limited information such as a request of concierge-type service. For instance, the request may not include all the information sufficient for performing search of a service by a conventional digital assistant system; Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0016, 0057, & 0059);
continuously messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, and 0094);
and retrain the proposal task generation machine learning algorithm based on the feedback and the proposal [fields] (Paragraph 0036, Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a customer intent, generating customizable proposals and/or personalized survey, and various other functionalities as described above. A machine learning algorithm may be a neural network, for example. Examples of neural networks that may be used with embodiments herein may include a deep neural network, convolutional neural network (CNN), and recurrent neural network (RNN). In some cases, a machine learning algorithm trained model may be pre-trained and implemented on the provided agent responder system, and the pre-trained model may undergo continual re-training that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment over time (e.g., changes in the customer/user data, insight data, model performance, third-party data, etc.); Paragraph 0077, The machine learning module 201 may be capable of providing a personalized predictive model for a service or a type of service (e.g., hotel, travel, events, etc.), to generate recommendations by processing customer request data. The predictive model may be continually trained and improved using proprietary data or relevant data (e.g., feedback data, customer data collected from past service history or from the same customer segmentation) so that the output can be better adapted to the specific customer or type of service; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0018 & 0059).
Yosuf et al. discloses to: display one or more proposal tasks to a member and/or a representative interface; select a service type using a member interface (see Yosuf et al., Paragraph 0075, The customer request may include limited information such as a request of concierge-type service); and provide one or more proposal tasks based on the member selected service type (see Yosuf et al., Paragraph 0077, provide a personalized predictive model for a service or a type of service). Although the Yosuf et al. discloses all the limitations above and to provide one or more proposal tasks for a specify type of service, Yosuf et al. does not specifically disclose a proposal template for each type of service.
However, Zhang et al. discloses to: update the [developer] interface to display one or more new [developer] interface elements corresponding to the proposal, wherein the one or more new [developer] interface elements include a template generation option associated with the particular proposal, and wherein when the template generation option is selected, a proposal template is created for generating similar proposals and similar proposal tasks (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction; Paragraph 0148, According to one embodiment of the present teaching, the visual input based program integrator 1012 may store the customized virtual agent as a template, and retrieve the template from the customized task database 139 when a developer is developing a different but similar virtual agent. In this case, the bot design programming interface manager 1002 may present the template to the developer via another bot design programming interface, such that the developer can directly modify the template, e.g. by modifying some parameters, instead of selecting and building all modules of the virtual agent from beginning);
continuously messages exchanged between the member and a representative associated with the representative interface to obtain feedback corresponding to performance of the one or more proposal tasks; and retrain the proposal task generation machine learning algorithm based on the feedback and the proposal template (Paragraph 0046, In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives or acting as virtual representatives/agents to communicate with customers in a more efficient and effective way, to achieve similar or even better customer satisfaction with minimum human involvement; Paragraph 0048, The disclosed system in the present teaching can integrate various intelligent components into one comprehensive online dialogue system to generate high-quality automatic responses for effectively assisting human representatives/agents to accomplish complex service tasks and/or address customer's information need in an efficient way. More specifically, based on machine learning and AI technique, the disclosed system can learn how to strategically ask user questions, present intermediate candidates to the users based on historical human-human or human-machine or machine-machine conversation data, together with human or machine action data that involves calling third party applications, services or databases. The present teaching has disclosed both statistical learning and template based approach as well as deep learning models (e.g. a sequence to sequence language generation model, a sequence to structured data generation model, a reinforcement learning model, a sequence to user intention model) for generating higher quality and better utterance/response messages for the conversation and interaction. Moreover, the disclosed system can provide more effective products/services recommendations in the conversation by using not only user transaction history and user demographic information that are normally used in traditional recommendation engines, but also additional contextual information about the user needs, such as possible user initial request (i.e. a user query) or supplemental information collected while talking with the user. The disclosed system is also capable of using those information as well as users' implicit feedback signals (such as clicks and conversions) when interacting with our recommendation results to more effectively learn users' interests, persuade them for certain conversions, collect their explicit feedback (such as rating), as well as actively solicit additional sophisticated user feedback such as their suggestions for future product/service improvement; Paragraph 0056, The virtual agent development engine 170 may also store the customized tasks into the customized task database 139, which can provide previously generated tasks as a template for future task generation or customization during virtual agent development; Examiner notes that the initial template may be modified over time based on new users’ interests and/or suggestions for future product/service improvement).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for using a proposal task generation machine learning algorithm to generate one or more proposal tasks performable based on a set of project parameters and a member profile (e.g., type of service and member preferences), wherein the one or more proposal tasks are displayed to a representative interface of the invention of Yosuf et al. to further specify a template for similar proposals and similar proposal tasks of the invention of Zhang et al. because doing so would allow the method to provide previously generated tasks for a specific type of service as a template for future task generation (see Yosuf et al., Paragraphs 0056 & 0129, a virtual agent for booking a flight). 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 3, 10, and 17 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Yusuf et al. further discloses wherein: the proposal is associated with a third-party entity (Paragraph 0069, In some instances, the agent responder system 121 may retrieve data from the database systems 111, 123 which are in communication with the one or more external systems (e.g., location data sources, mobility service providers, vehicle dispatching system, etc.) or third-party systems 130 (e.g., third-party commerce entities such as food, restaurants, hospitality, ticketing event entities, theaters, digital service providers, etc.); See provisional application # 62/987,822, filed on 03/10/2020, Paragraph 0051);
and the computer-implemented method further comprises automatically coordinating with the third-party entity for the performance of the one or more proposal tasks (Paragraph 0095, The third-party resource 409 may comprise a centralized partner communication gateway to enable communication with service suppliers such as restaurant partner, event partner, flight partner, hotel partner, retail partner and various others. For instance, APIs may be used to enable real-time digital communications (e.g., email 413, messaging) between the virtual agents and the third-party partners. For instance, queries generated based on the extracted request data points may be communicated to the third-party service suppliers via the smart inbox 413 and the relevant information may be received and used for generating recommendations and/or proposals to the customer by the auto extractor 407 and chat bot 411; Paragraph 0096, Next, proceeding with the recommendation and proposal stage, the agent responder system may perform availability checking (operation 415). The availability information may be obtained from the smart inbox 413, third-party inventory (e.g., through third-party APIs integration points) based on the extracted request data points. In some embodiments, the input feature data to be processed by a machine learning trained model (operation 419) may be generated based on the availability information, the extracted request data points, and customer data. The machine learning algorithm trained model may output one or more recommended proposals to the agent for selection (operation 421) or further customization; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0077-0078).
Regarding claims 4, 11, and 18 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Yusuf et al. further comprising: detecting, based on the member interactions with the set of member interface elements, a counter-proposal to the one or more proposals from the set of proposals (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101);
and processing the counter-proposal through the proposal task generation machine learning algorithm to determine whether the counter-proposal is acceptable according to the set of project parameters (Paragraph 0031, Current virtual assistant or digital assistant systems may not be able to provide satisfactory user experience as a customer is usually requested to provide detailed inputs such as location, date, time, restaurant name, so that a digital assistant perform the search. The present disclosure provides a concierge network addressing the above needs by leveraging human agents knowledge, machine learning-based auto-processing and insight extraction; Paragraph 0032, The present disclosure provides systems and methods for providing deep learning-based recommendations to human agents, leveraging human agents' knowledge and implicit insight of customer preferences to provide personalized experience to customers. Moreover, the provided system allows for live communication experience with customers, and offers recommendations, booking, transaction, and fulfillment through an integrated platform in a convenient manner; Paragraph 0034, A user of the provided system may be an individual human agent, or the user may be an entity (e.g., business, travel organization, etc.), a group of human agents who are responding to and fulfilling customer requests to provide concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements etc.) and highly personalized services tailored (e.g., favorite seat in a restaurant, temperature in a hotel room, special local events such as swim with orca whales, etc.). A user of the provided concierge network or platform may also include individual customers who seek of personalized services such as travel, dining, concerts, trips, activities, events tailored to the customer that the personalized service may not be readily available or easily accessed by performing a search; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0014 & 0016).
Regarding claims 5, 12, and 19 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Although Yusuf et al. further discloses wherein the set of proposals is automatically generated in real-time in response to receiving a completed project [input] corresponding to the set of project parameters (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082), Yusuf et al. does not specifically disclose wherein the set of proposals are automatically generated in real-time in response to receiving a completed project template corresponding to the project corresponding to the set of project parameters.
However, Zhang et al. discloses wherein the set of proposals are automatically generated in real-time in response to receiving a completed project template corresponding to the set of project parameters (Paragraph 0056, The virtual agent development engine 170 may also store the customized tasks into the customized task database 139, which can provide previously generated tasks as a template for future task generation or customization during virtual agent development; Paragraph 0085, The task structure learning engine 356 may be designed to learn, based on the training seeds 359 and the actual conversation data, structures associated with different tasks. A structure associated with a task may refer to the structure of different types of information needed to carry out the task. For example, for a weather agent to complete the task to provide weather information to a user, a structure associated with this task may specify the types of information that can be gathered to provide the weather information requested. Some of such types of information to be gathered may be necessary and some may be optional. For example, location is a piece of information that may be necessary in order to provide weather information, while information about time of day may not be necessary. As another example, for a task for making flight information, a structure for this task may indicate that necessary information to complete the task may include source, destination, choice of one-way or round trip, and date(s) of travel and that optional information may include price range, number of stops, etc.).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for using a proposal task generation machine learning algorithm to generate one or more proposal tasks performable based on a set of project parameters and a member profile (e.g., type of service and member preferences), wherein the one or more proposal tasks are displayed to a representative interface of the invention of Yosuf et al. to further specify wherein the set of proposals are automatically generated in real-time in response to receiving a completed task template corresponding to the task of the invention of Zhang et al. because doing so would allow the method to indicate necessary information to complete the task based on structures/templates associated with different tasks (see Zhang et al., Paragraph 0085). 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 6, 13, and 20 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Yusuf et al. further discloses wherein the set of proposals is associated with a set of vendors, wherein the set of vendors is automatically selected based on prior performance of other tasks according to other project parameters similar to the set of project parameters (Paragraph 0095, The third-party resource 409 may comprise a centralized partner communication gateway to enable communication with service suppliers such as restaurant partner, event partner, flight partner, hotel partner, retail partner and various others. For instance, APIs may be used to enable real-time digital communications (e.g., email 413, messaging) between the virtual agents and the third-party partners. For instance, queries generated based on the extracted request data points may be communicated to the third-party service suppliers via the smart inbox 413 and the relevant information may be received and used for generating recommendations and/or proposals to the customer by the auto extractor 407 and chat bot 411; Paragraph 0096, Next, proceeding with the recommendation and proposal stage, the agent responder system may perform availability checking (operation 415). The availability information may be obtained from the smart inbox 413, third-party inventory (e.g., through third-party APIs integration points) based on the extracted request data points. In some embodiments, the input feature data to be processed by a machine learning trained model (operation 419) may be generated based on the availability information, the extracted request data points, and customer data. The machine learning algorithm trained model may output one or more recommended proposals to the agent for selection (operation 421) or further customization; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0077-0078), and wherein the member interactions with the set of member interface elements indicate a selection of a vendor from the set of vendors for performance of the one or more proposal tasks (Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; Examiner notes that in Figure 15, the member accepted Ella Canta from the subset of proposals; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101).
Regarding claims 7, 14, and 21 (Currently Amended), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Yusuf et al. further comprising: detecting, based on the member interactions with the set of member interface elements, a rejection of one or more proposals from the subset of proposals; processing the rejection through the proposal task generation machine learning algorithm to generate a new set of proposals; and updating the member interface to display a new set of member interface elements corresponding to the new set of proposals (Paragraph 0100, In some cases, additional information may be obtained via in-app messaging or communication with the customer to generate different proposals (operation 417). For instance, customer may provide input in response to an initial proposal. The additional customer input may be analyzed by the NPL engine to extract opinion such as deny or not satisfied, or request data points such as additional requirement, and such additional customer input data may be used to produce a new proposal; Paragraph 0106, FIG. 6A shows an exemplary process 600 in the booking, fulfillment stage and a post fulfillment stage. After the proposals are completed by the agent, one or more proposals may be delivered to the customer via the customer interface. A customer may select a proposal to proceed with (operation 601); Paragraph 0112, For instance, any of the steps can be repeated any number of times until a proposal is accepted by a customer; Paragraph 0119, FIG. 15 shows a graphical user interface displaying proposals to a customer. The proposals are generated using the methods as described above; See provisional application # 62/987,822, filed on 03/10/2020, Paragraphs 0082, 0088, 0094, and 0101)).
Regarding claims 22-24 (Previously Presented), which are dependent of claims 1, 8, and 15, the combination of Yusuf et al. and Zhang et al. discloses all the limitations in claims 1, 8, and 15. Yosuf et al. discloses: displaying one or more proposal tasks to a member and/or or a representative interface; selecting a service type using a member interface (see Yosuf et al., Paragraph 0075, The customer request may include limited information such as a request of concierge-type service); and providing one or more proposal tasks based on the member selected service type (see Yosuf et al., Paragraph 0077, providing a personalized predictive model for a service or a type of service). Although Yosuf et al. discloses all the limitations above and providing one or more proposal tasks based on similar proposals (e.g., a specific type of service), Yosuf et al. does not specifically disclose wherein the one or more new representative interface elements include a template generation option associated with the particular proposal.
However, Zhang et al. discloses updating the [developer] interface to display a button corresponding to other proposals from the subset of proposals, wherein selection of the button is used to allow for creation of other proposal templates corresponding to the other proposals (Paragraph 0148, According to one embodiment of the present teaching, the visual input based program integrator 1012 may store the customized virtual agent as a template, and retrieve the template from the customized task database 139 when a developer is developing a different but similar virtual agent. In this case, the bot design programming interface manager 1002 may present the template to the developer via another bot design programming interface, such that the developer can directly modify the template, e.g. by modifying some parameters, instead of selecting and building all modules of the virtual agent from beginning).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for using a proposal task generation machine learning algorithm to generate one or more proposal tasks performable based on a set of project parameters and a member profile (e.g., type of service and member preferences), wherein the one or more proposal tasks are displayed to a representative interface of the invention of Yosuf et al. to further specify wherein the one or more new interface elements include a template generation option associated with the particular proposal of the invention of Zhang et al. because doing so would allow the method to provide previously generated tasks as a template for future task generation (see Yosuf et al., Paragraph 0056). 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.
Sim et al. (US 2021/0373943 A1) – discloses from information 206 and information 208, input 204 is determined. Input 204 includes, without limitation, a list of subtasks 210, subtask definitions 212, the state of each subtask (e.g., not started, in progress, complete), dependencies between subtasks 216A, empty slots 218, and other dependencies 219. The list of subtasks 210 includes each action that needs to be taken to complete the task. For example, if the task is to book a business trip, subtasks might include book airfare, book hotel, book transportation at destination, and schedule meeting. If the task is to schedule a wedding reception, the subtasks might be choose a date, book a venue, book a caterer, order flowers, order cake, and book a photographer. The list of subtasks may come from the user information 206 or other resources 208 (see at least Paragraph 0027).
Gonzalez et al. (US 2018/0053121 A1) – discloses 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 (see at least Paragraph 0018).
Li (CN 113158043 A) – discloses to obtain user preference by obtaining user information, performs reinforcement learning and action-evaluation environment to obtain knowledge, improves action plan to adapt to requirement of travel resource recommendation according to user preference (see at least Abstract).
Chiang (Chiang, H.S. and Huang, T.C., 2015. User-adapted travel planning system for personalized schedule recommendation. Information Fusion, 21, pp.3-17) – discloses a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users (see Abstract).
Kabassi (Kabassi, K., 2010. Personalizing recommendations for tourists. Telematics and Informatics, 27(1), pp.51-66. (Year: 2010)) – discloses different criteria and characteristics are taken into account in order to find those that are believed to be the best for the user interacting with the system. For this purpose, the systems have been categorized with respect to the services they provide in order to discover the criteria that are used for evaluating different services (see Tables 2-6).
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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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624