DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the Amendment dated 20 January 2026, has been entered.
Status of the Claims
The pending claims in the present application are claims 1, 2, 4-6, 8, 9, 11-13, 15, 16, 18-20, and 22-27 of the Amendment.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 21 October 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4-6, 8, 9, 11-13, 15, 16, 18-20, 22, 24, and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1, 2, 4-6, and 22 constitutes a process under 35 USC 101, the “system” of claims 8, 9, 11-13, and 24 constitutes a machine under the statute, and the “non-transitory computer-readable storage medium” of claims 15, 16, 18-20, and 26 constitutes a manufacture under the statute. Accordingly, claims 1, 2, 4-6, 8, 9, 11-13, 15, 16, 18-20, 22, 24, and 26 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“A ... method, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... automatically processing a set of messages ... to identify an intent associated with a member, wherein the set of messages is exchanged between the member and a representative ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... identifying member characteristics and historical information corresponding to tasks previously performed on behalf of the member, wherein the member characteristics and the historical information are identified based on a member profile; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... identifying different tasks previously performed on behalf of a set of other members, wherein the different tasks are identified based on the member characteristics; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... processing the intent, the member profile, and the different tasks ... to identify a set of tasks for the member and task data corresponding to the set of tasks for presentation to the member, ... identifies the set of tasks and the task data as new messages between the member and representative are continually exchanged ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generating a set of recommendations for performance of the set of tasks, wherein the set of recommendations is generated as ... continues to process the new messages to identify additional tasks for the member; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generating ... to present the set of recommendations according to the task data, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... monitoring member interactions ..., wherein the member interactions include member selection of a task from the set of tasks for performance on behalf of the member; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... modifying the task data based on the member interactions ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... updating ... to present the modified task data; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... continuously and automatically updating the member profile ..., wherein the member profile ... as updated according to the member interactions and the modified task data.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: commercial interactions, including marketing or sales activities or behaviors associated with providing services to customers; and managing relationships or interactions between people, and in particular, between members and representatives, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “identifying” and “monitoring” steps), and evaluation, judgment, and/or opinion (e.g., the recited “processing,” “generating,” “modifying,” and “updating” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “method” is “computer-implemented” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “processing” involves “using natural language processing (NLP)” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “exchanged” takes place “through a communication interface associated with the member and the representative” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “processing” is “through a machine learning algorithm” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “identifies” is performed by “the machine learning algorithm” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “exchanged” is “through the communication interface” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “process” is performed by “the machine learning algorithm” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generating” is of “a set of task-specific interfaces corresponding to the set of tasks” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “present” is by “the set of task-specific interfaces” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... wherein the set of task-specific interfaces is distinct from the communication interface ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “interactions” are “with the set of task-specific interfaces” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “updating” is of “the set of task-specific interfaces” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “updating” is of “the machine learning algorithm” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2, 4-6, and 22, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein the member profile is processed through a trained clustering algorithm to identify the set of other members according to a set of vectors” of claim 2, the “wherein the set of recommendations includes a ranking of the set of tasks, and wherein different tasks from the set of tasks are ranked according to corresponding likelihoods of the member selecting the tasks for performance on behalf of the member” of claim 4, the “wherein the set of recommendations includes a ranking of the set of tasks, and wherein tasks from the set of tasks are ranked according to corresponding levels of urgency for completing the tasks” of claim 5, the “identifies the set of tasks and the task data according to a cognitive load associated with the member” of claim 6, and the “further comprising: detecting a request for additional task data associated with the set of tasks, wherein the request is detected ...; evaluating the member profile to determine a portion of the additional task data to present ... without increasing a likelihood of cognitive overload for the member; and updating ... to present the portion” of claim 22). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “computer-implemented” of claim 2, the “computer-implemented” of claim 4, the “computer-implemented” of claim 5, the “computer-implemented ... the machine learning algorithm” of claim 6, and the “computer-implemented ... through the set of task-specific interfaces; ... through the set of task-specific interfaces ...; and ... the set of task-specific interfaces” of claim 22). Accordingly, claims 2, 4-6, and 22 also are rejected as ineligible under 35 USC 101.
Regarding claims 8, 9, 11-13, and 24, while the claims are of different scope relative to claims 1, 2, 4-6, and 22, the claims recite limitations similar to the limitations of claims 1, 2, 4-6, and 22. As such, the rejection rationales applied to reject claims 1, 2, 4-6, and 22 also apply for purposes of rejecting claims 8, 9, 11-13, and 24. Limitations recited by claims 8, 9, 11-13, and 24 that do not have a counterpart in claims 1, 2, 4-6, and 22, such as the recited “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” limitations of claim 8, fail to warrant a finding of eligibility, because such limitations amount to additional elements that fail to meet the criteria of Step 2A, Prong Two and Step 2B, for the same reasons as the additional elements of claims 1, 2, 4-6, and 22. Claims 8, 9, 11-13, and 24 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding pending claims 15, 16, 18-20, and 26, while the claims are of different scope relative to claims 1, 2, 4-6, 8, 9, 11-13, 22, and 24, the claims recite limitations similar to the limitations of claims 1, 2, 4-6, 8, 9, 11-13, 22, and 24. As such, the rejection rationales applied to reject claims 1, 2, 4-6, 8, 9, 11-13, 22, and 24 also apply for purposes of rejecting claims 15, 16, 18-20, and 26. Claims 15, 16, 18-20, and 26 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 8, 13, 15, 20, and 22-27 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by WIPO Int’l Pub. No. 2021/181095 A1 to Yusuf et al. (hereinafter referred to as “Yusuf”).
Regarding claim 1, Yusuf discloses the following limitations:
“A computer-implemented method, comprising: ...” - Yusuf, discloses, “FIG. 1 schematically shows a concierge network or platform 100 in which the method and system for assisting agents to provide personalized services can be implemented. A platform 100 may include one or more user devices 101-1, 101-2, 101-3, a server 120, an agent responder system 121, one or more third-party systems 130, and a database 111, 123. Each of the components 101-1, 101-2, 101-3, 111, 123, 120, 130 may be operatively connected to one another via a network 110 or any type of communication link” (para. [0041]), and “User device 101-1, 101-2 may be a computing device configured to perform one or more operations” (para. [0048]).
“... automatically processing a set of messages using natural language processing (NLP) to identify an intent associated with a member, wherein the set of messages is exchanged between the member and a representative through a communication interface associated with the member and the representative; ...” - See the aspects of Yusuf that have been cited above. Yusuf also discloses, “predictive models (e.g., natural language processing) for analyzing input data (e.g., customer request data) transmitted from the customer device, generating personalized recommendations to be further customized by agents 103-1, 103-2 via one or more user devices 101-1, 101-2, 101-3, generating personalized feedback survey, and extracting insight (e.g., implicit customer preference)” (para. [0044]), “the client terminal or customer application may comprise a data processing module to provide functions such as ingesting of data streams (e.g., audio/video streams, chat messages, etc.)” (para. [0059]), “natural language processing, sentiment analysis and the like may be employed to pre-process the customer data and customer feedback data” (para. [0060]), “Suitable data processing techniques such as voice recognition, facial recognition, natural language processing, sentiment analysis and the like may be employed to pre-process the raw customer request data and/or user feedback data” (para. [0081]), “The one or more communication channels may include, but not limited to, a website channel, email channel, text message channel, digital virtual assistant, smart home device such as Alexa®, interactive voice response (IVR) systems, social media channel and messenger APIs (application programming interfaces) such as Facebook channel, Twilio SMS channel, Skype channel, Slack channel, WeChat channel, Telegram channel, Viber channel, Line channel, Microsoft Team channel, Cisco Spark channel, and Amazon Chime channel, and various others” (para. [0082]), and “The customer request data may be received via a customer interface (e.g., chat hot, instant messaging, etc.) as described above. The captured customer request data may be processed to extract and identify qualified request data points (operation 303)” (para. [0090]). The system processing chat messages using NLP to identify customer requests, insights, sentiments, data, feedback, and the like, wherein the chat messages are exchanged between the agent and the customer through the text messaging communication channel, in Yusuf, reads on the recited limitation.
“... identifying member characteristics and historical information corresponding to tasks previously performed on behalf of the member, wherein the member characteristics and the historical information are identified based on a member profile; ...” - See the aspects of Yusuf that have been cited above. Yusuf also discloses, “Relevant data may comprise user data (e.g., agent ID, specialties, expertise/skills, training credentials, ratings, availability, geolocation, service category/field, etc.), customer profile data (e.g., customer preferences, personal data such as identity, age, gender, contact information, demographic data, ratings, subscription, member redemption of loyalty points, etc ), augmented customer data records (e.g., labeled with additional data related to customer intent, service type, expert insight, customer segmentation, etc ), historical data (e.g., social graph, transportation history, transportation subscription plan data, purchase or transaction history, loyalty programs, etc.), and various other data as described elsewhere herein” (para. [0068]), and “The augmented customer database 510 may store customer data and augmented data. In some embodiments, the customer data (e.g., demographic data, purchase data, social graph, etc.) may be augmented with insight data. The customer data may include customer profile data as described elsewhere herein. For instance, the customer data may include customer profile information (e.g., name, address, spouse, age, gender, activation date, etc.), user preferences (questionnaire results, user feedback collected during registration or subscription), App Usage (content viewed / engaged with), Request History (history of requests and statuses), Fulfillment History (all bookings made through the platform), Transaction History (transactional data through platform), and various others” (para. [0103]). The system identifying customer data and historical data relating to fulfillment and transactions of the customer, wherein the data is associated with the customer via the customer profile, in Yusuf, reads on the recited limitation.
“... identifying different tasks previously performed on behalf of a set of other members, wherein the different tasks are identified based on the member characteristics; ...” - See the aspects of Yusuf that have been cited above. The system identifying historical data relating to fulfillment and transactions of all customers, wherein the fulfillment and transactions relate to customer data, including preferences, feedback, and the like, in Yusuf, reads on the recited limitation.
“... processing the intent, the member profile, and the different tasks through a machine learning algorithm to identify a set of tasks for the member and task data corresponding to the set of tasks for presentation to the member, wherein the machine learning algorithm identifies the set of tasks and the task data as new messages between the member and representative are continually exchanged through the communication interface; ...” - See the aspects of Yusuf that have been cited above. Yusuf also discloses, “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” (para. [0075]), “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. In some cases, a predictive model may be pre-trained and implemented on the physical system, and the pre-trained model may undergo continual re-training that involves 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., new insights, model performance, user-specific data, etc.). The continual training process may require customer feedback data or expert input” (para. [0077]), “the implicit user preference and/or the insight data may be extracted from the feedback survey and tracked fulfillment data using a machine learning trained model. For instance, the extracted insight data may be a travel preference extracted from fulfillment data that indicates user-preferred transportation mode (e.g., autonomous vehicle, public transportation (such as train, light rail, or city bus), shuttle, ride-sharing, ride-hailing, shared trip or private trip, walking, bicycle, e-scooter, taxi, etc.), or user experience inside a vehicle (e.g., access to music, game) and the like. Such insight data can be further used to personalize a future service such as using the travel preferences to determine the travel route, transportation mode for a trip, and/or stops (e.g., scenic views, restaurants, coffee shops, etc.) during the travel route” (para. [0104]), and “an exemplary process 600 including pre-request stage 620, request analysis and triage stage 630, proposal and recommendation stage 640, booking, fulfillment stage 650 and post fulfillment stage 660. In some embodiments, the pre-request stage 620 may include operations to collect data such as customer insights 621, onboarding survey 623, feedback from personalized survey 627, customer preferences related to various services 629 (e.g., hotels and hospitality, restaurants and dining, tourism and entertainment, healthcare, experience, travel, service delivery, and various others). Such data may be aggregated and used for generating training datasets for training one or more machine learning models or for generating recommendations/proposals” (para. [0109]). Processing the input data, including customer preferences, customer data, and request data through the machine learning trained model to identify the set of proposals for the customer and the transactions and fulfillment for the recommendations for presenting to the customer, wherein the machine learning trained model is continually trained based on customer data collected via the messaging between the agents and the customers, in Yusuf, reads on the recited limitation.
“... generating a set of recommendations for performance of the set of tasks, wherein the set of recommendations is generated as the machine learning algorithm continues to process the new messages to identify additional tasks for the member; ...” - See the aspects of Yusuf that have been cited above. Generating a set of recommendations for carrying out the set of proposals, wherein the set of recommendations is generated by the machine learning trained model as it processes the customer data being received via messaging between the customer and the agent to identify additional recommendations for the customer, in Yusuf, reads on the recited limitation.
“... generating a set of task-specific interfaces corresponding to the set of tasks, wherein the set of task-specific interfaces is generated to present the set of recommendations according to the task data, and wherein the set of task-specific interfaces is distinct from the communication interface; ...” - See the aspects of Yusuf that have been cited above. See the “Dining” interface in FIG. 14A, and the “DINING” interface in FIG. 16A. The dining interfaces, which are separate from the text messaging communication channels, in Yusuf, read on the recited limitation.
“... monitoring member interactions with the set of task-specific interfaces, wherein the member interactions include member selection of a task from the set of tasks for performance on behalf of the member; ...” - See the aspects of Yusuf that have been cited above. Yusuf also discloses, “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” (para. [0100]). Receiving, processing, and using customer inputs, including inputs in response to proposals communicated via specific interfaces, wherein the inputs include selections of times and dates for reservations for events for the customers to participate in, in Yusuf, reads on the recited limitation.
“... modifying the task data based on the member interactions with the set of task-specific interfaces; ...” - See the aspects of Yusuf that have been cited above. Generating new proposals based on customer inputs received via interfaces for prior proposals, in Yusuf, reads on the recited limitation.
“... updating the set of task-specific interfaces to present the modified task data; and ...” - See the aspects of Yusuf that have been cited above. Generating new interfaces for new proposals, based on customer inputs about prior proposals via prior interfaces, in Yusuf, reads on the recited limitation.
“... continuously and automatically updating the member profile and the machine learning algorithm, wherein the member profile and the machine learning algorithm as updated according to the member interactions and the modified task data.” - See the aspects of Yusuf that have been cited above. Storing customer inputs and customer data, and continually re-training and improving the machine learning trained model, wherein the customer profiles and the machine learning trained model are updated based on customer inputs and recommendations and proposals generated based thereon, in Yusuf, reads on the recited limitation. The examiner takes the position that the steps in Yusuf are performed continuously.
Regarding claim 6, Yusuf discloses the following limitations:
“The computer-implemented method of claim 1, wherein the machine learning algorithm identifies the set of tasks and the task data according to a cognitive load associated with the member.” - See the aspects of Yusuf that have been cited above. Trained machine learning models making recommendations and provided associated data, based on customer preferences and insights, in Yusuf, reads on the recited limitation.
Regarding claim 8, while the claim is of different scope relative to claim 1, the claim recites limitations similar to those recited by claim 1. As such, the rationales applied to reject claim 1 also apply for purposes of rejecting claim 8. Limitations recited by claim 8 that do not appear to have a counterpart in claim 1, such as the recited “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” limitations of claim 8, are disclosed by Yusuf (see, e.g., para. [0049]). Claim 8 is, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
Regarding claim 13, while the claim is of different scope relative to claim 6, the claim recites limitations similar to those recited by claim 6. As such, the rationales applied to reject claim 6 also apply for purposes of rejecting claim 13. Claim 13 is, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
Regarding claim 15, while the claim is of different scope relative to claims 1 and 8, the claim recites limitations similar to those recited by claims 1 and 8. As such, the rationales applied to reject claims 1 and 8 also apply for purposes of rejecting claim 15. Claim 15 is, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
Regarding claim 20, while the claim is of different scope relative to claims 6 and 13, the claim recites limitations similar to those recited by claims 6 and 13. As such, the rationales applied to reject claims 6 and 13 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
Regarding claim 22, Yusuf discloses the following limitations:
“The computer-implemented method of claim 1, further comprising: detecting a request for additional task data associated with the set of tasks, wherein the request is detected through the set of task-specific interfaces; ...” - See the aspects of Yusuf that have been cited above. Yusuf also discloses “Skip to chat” (FIG. 15) and the chat window for “DINING” (FIG. 16A). The system continually determining customer inputs, preferences, and requests from messages sent between the agents and customers via the chat of the dining interfaces, in Yusuf, reads on the recited limitation.
“... evaluating the member profile to determine a portion of the additional task data to present through the set of task-specific interfaces without increasing a likelihood of cognitive overload for the member; and ...” - See the aspects of Yusuf that have been cited above. The system using the customer profile data and request data to present and update recommendation and proposal windows by, for example, removing options, in Yusuf, reads on the recited limitation.
“... updating the set of task-specific interfaces to present the portion.” - See the aspects of Yusuf that have been cited above. Updating the information displayed by the dining or other specific interfaces, based on customer inputs, including feedback about recommendations and proposals presented, in Yusuf, reads on the recited limitation.
Regarding claim 23, Yusuf discloses the following limitations:
“The computer-implemented method of claim 1, wherein the set of task-specific interfaces is implemented with corresponding task-specific communications sessions associated with the set of tasks, and wherein the corresponding task-specific communications sessions are implemented to reduce messages exchanged through the communication interface.” - See the aspects of Yusuf that have been cited above. Customer interfaces by which customers provide information to agents, wherein interfaces correspond to, for example, scheduling meals at specific restaurants and rooms at specific hotels (see, e.g., FIGS. 14A, 14B, 15, and 16A), wherein the interfaces including chat windows separate from the other forms of messaging-based communication channels, in Yusuf, read on the recited limitation.
Regarding claims 24 and 25, while the claim is of different scope relative to claims 22 and 23, the claims recite limitations similar to those recited by claims 22 and 23. As such, the rationales applied to reject claims 22 and 23 also apply for purposes of rejecting claims 24 and 25. Claims 24 and 25 are, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
Regarding claims 26 and 27, while the claim is of different scope relative to claims 22-25, the claims recite limitations similar to those recited by claims 22-25. As such, the rationales applied to reject claims 22-25 also apply for purposes of rejecting claims 26 and 27. Claims 26 and 27 are, therefore, also rejected under 35 USC 102(a)(2) as anticipated by Yusuf.
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.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yusuf, in view of U.S. Pat. App. Pub. No. 2020/0334737 A1 to Miller (hereinafter referred to as “Miller”).
Regarding claim 2, the combination of Yusuf and Miller (hereinafter referred to as “Yusuf/Miller”) teaches limitations below that do not appear to be disclosed in their entirety by Yusuf:
“The computer-implemented method of claim 1, wherein the member profile is processed through a trained clustering algorithm to identify the set of other members according to a set of vectors.” - See the aspects of Yusuf that have been cited above. Yusuf also discloses “The processed customer data and customer feedback data may be used to form at least part of the input feature vector to train a predictive model” (para. [0060]), and “customer segmentation” (para. [0069]). Customer profiles, other customers in customer segments, and vectors, in Yusuf, read on the recited “member profile,” “identify the set of other members,” and “set of vectors” limitations. Yusuf does not, however, provide specifics about these elements. Miller, on the other hand, discloses, “the service provider 106 may include a clustering module 110 which assigns a user to a cluster 118 of similar users. Cluster 118 of similar users may have stored preferences and/or purchase history in cluster data 120. It should be noted that clustering module 118 may use any suitable clustering technique to assign a user to a cluster 118” (para. [0026]), “the clustering module 210 may, in conjunction with the processor 204, be configured to determine, based on data related to a user (e.g., demographic information, item reviews, item purchase history information, etc.), a ‘cluster’ or group of consumers to which the user likely belongs. The clustering module 210 may assign a consumer to a cluster based on item purchases, geographic vicinity, economic similarities, physical attributes (height, weight, gender), or any other suitable user attributes or combination of user attributes” and “a clustering module of the service provider may use a clustering algorithm (e.g., k-means clustering) to assign clusters within a set of users (e.g., all users of the service)” (para. [0035]), and “Once mapped to a multidimensional space 410, point 412, or a vector {right arrow over (v)} associated with point 412, can then be used to group the user with users who have been similarly affected by the product (or a product having similar attribute values 406)” (para. [0059]). The clustering of consumers by k-means clustering and the like, in Miller, reads on the recited “processed through a trained clustering algorithm” limitation.
Miller discloses “a system and techniques directed toward identifying personalized recommendations for a user” (see Abstract), similar to the claimed invention and to Yusuf. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the determining of customer segments based on vectors, in Yusuf, to have included the use of machine learning clustering algorithms, as in Miller, for identifying personalized recommendations for users, as taught by Miller (see Abstract).
Regarding claim 9, while the claim is of different scope relative to claim 2, the claim recites limitations similar to those recited by claim 2. As such, the rationales applied to reject claim 2 also apply for purposes of rejecting claim 9. Claim 9 is, therefore, also rejected under 35 USC 103 as obvious in view of Yusuf/Miller.
Regarding claim 16, while the claim is of different scope relative to claims 2 and 9, the claim recites limitations similar to those recited by claims 2 and 9. As such, the rationales applied to reject claims 2 and 9 also apply for purposes of rejecting claim 16. Claim 16 is, therefore, also rejected under 35 USC 103 as obvious in view of Yusuf/Miller.
Claims 4, 5, 11, 12, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yusuf, in view of U.S. Pat. No. 9,280,252 B2 to Brandmaier et al. (hereinafter referred to as “Brandmaier”).
Regarding claim 4, the combination of Yusuf and Brandmaier (hereinafter referred to as “Yusuf/Brandmaier”) teaches limitations below that do not appear to be disclosed in their entirety by Yusuf:
“The computer-implemented method of claim 1, wherein the set of recommendations includes a ranking of the set of tasks, and wherein different tasks from the set of tasks are ranked according to corresponding likelihoods of the member selecting the tasks for performance on behalf of the member.” - See the aspects of Yusuf that have been cited above. The recommendations in Yusuf read on the recited “set of recommendations” limitation. While FIG. 8 of Yusuf shows numbered recommendations (“1. ZZ’s Clam Bar” and “2. Carbone”), the significance of the numbering is not explained in more detail. Brandmaier, on the other hand, discloses, “the insurance recommendation system may advantageously rank the insurance tasks 500a-c to determine which insurance tasks are relatively more relevant to the users associated with the user groups 502a-d and which insurance tasks are relatively less relevant to those users” (col. 9, ll. 57-61), and “Similar to the insurance tasks 500a-c in FIG. 5A, the insurance tasks 552a-c in FIG. 5B are also associated with a set of relevancy scores 556. The relevancy scores 554a-d for the insurance task 552b likewise quantify the relevancy of the insurance task to the respective user attributes 550a-d the insurance task is paired with. In this way, the recommendation engine may advantageously rank the insurance tasks 552a-c to determine which insurance tasks are relatively more relevant to the users respectively associated with the user attributes 550a-d and which insurance tasks are relatively less relevant to users having those attributes” (col. 10, l. 67 to col. 11, l. 11). The ranking of different recommended tasks based on relevancy of the tasks to users, wherein the tasks are to be performed on for the users, in Brandmaier, reads on the rest of the recited limitation.
Brandmaier teaches recommending tasks to users (see abstract), similar to the claimed invention and to Yusuf. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the recommendations, of Yusuf, to include the relevancy rankings, of Brandmaier, to distinguish between tasks that are more relevant or less relevant to users, as taught by Brandmaier (see col. 9, ll. 57-61).
Regarding claim 5, Yusuf/Brandmaier teaches the following limitations:
“The computer-implemented method of claim 1, wherein the set of recommendations includes a ranking of the set of tasks, and wherein tasks from the set of tasks are ranked according to corresponding levels of urgency for completing the tasks.” - See the aspects of Yusuf and Brandmaier that have been cited above. The recommendations in Yusuf read on the recited “set of recommendations” limitation. The ranking of recommended tasks based on the relevancy of the tasks to the user, in Brandmaier, reads on the rest of the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 4, also apply to this rejection of claim 5.
Regarding claims 11 and 12, while the claims are of different scope relative to claims 4 and 5, the claims recite limitations similar to those recited by claims 4 and 5. As such, the rationales applied to reject claims 4 and 5 also apply for purposes of rejecting claims 11 and 12. Claims 11 and 12 are, therefore, also rejected under 35 USC 103 as obvious in view of Yusuf/ Brandmaier.
Regarding claims 18 and 19, while the claims are of different scope relative to claims 4, 5, 11, and 12, the claims recite limitations similar to those recited by claims 4, 5, 11, and 12. As such, the rationales applied to reject claims 4, 5, 11, and 12 also apply for purposes of rejecting claims 18 and 19. Claims 18 and 19 are, therefore, also rejected under 35 USC 103 as obvious in view of Yusuf/Brandmaier.
Response to Arguments
In view of the amendments to the claims, the claim rejections under 35 USC 101, 102, and 103 have been modified, but continue to be asserted, per the explanations provided in the 35 USC 101, 102, and 103 sections above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. App. Pub. No. 2021/0117214 A1 to Presant et al. discloses, “a method includes receiving one or more inputs associated with proactive triggers associated with a first user, determining whether the first user is eligible to receive proactive suggestions based on one or more proactive policies, generating one or more proactive suggestions based on the one or more inputs and user context data associated with the first user, selecting one or more of the proactive suggestions based on task history data associated with the first user, and sending instructions for presenting proactive content to the first user to a client system associated the first user, wherein the proactive content comprises the selected proactive suggestions.” (Abstract.)
Brachten, Florian, et al. "On the ability of virtual agents to decrease cognitive load: an experimental study." Information Systems and e-Business Management 18.2 (2020): 187-207.
De Melo, Celso M., et al. "Reducing cognitive load and improving warfighter problem solving with intelligent virtual assistants." Frontiers in psychology 11 (2020): 554706.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624