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 .
Continued Examination Under 37 CFR 1.114
Claims 1-3, 10, 12, 20 and 22 are presently amended.
Claims 7, 9 and 18-19 are cancelled.
Claims 23-24 are newly added.
Claims 1-6, 8, 10-17 and 19-24 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant's arguments filed 6/26/2026 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, but are not persuasive for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “Claim 1 describes receiving map application data via inter-application data sharing that detects travel intent…” " and such claim language describes technical operations such that claim 1 does not recite activities that can be performed in the human mind or that constitute managing personal behavior.
Further, claim 1 is not directed to an abstract idea because claim 1 recites improvements in the functioning of the system by enabling predictions of a subject's intended function to be performed at the physical location before the subject arrives at the physical destination. The MPEP states "the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement," and that the claim itself does not need to explicitly recite the improvement (e.g., "thereby improving computer functionality"), but it must include the components or steps that provide the improvement. MPEP § 2106.04(d)(1)…” [Arguments, pages 8-9].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully maintains that the present claims recite a judicial exception. In particular, Examiner observes that, when considered as a whole, the claims are directed towards providing options to users based on their intent to travel to a destination.
Examiner respectfully maintains that these activities set forth steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, displaying, to a user, options to perform a function based on determined intent is considered describe managing personal behavior and following rules or instructions. Examiner maintains that the present invention does not recite significantly more than the judicial exception for the reasons set forth in the rejection and in the response to the arguments below. Thus, claims 1, 10 and 20 recite concepts identified as abstract ideas, namely certain methods of organizing human activity. As such, Examiner remains unpersuaded.
Second, Applicant argues that “…The specification describes the problem that users travel to physical destinations to perform functions without the entity knowing what function they intend to perform, resulting in wasted time, unnecessary carbon emissions, and inefficient use of entity resources. Specification at [0022]-[0024]… Claim 1 recites "predicting... a function, associated with the subject intent, to be performed at the physical destination" and "generating a notification, in the application, of one or more options for performing the function" and "causing output of an interface to input certain data associated with the subject... wherein the data associated with the subject is required to perform the function," which plainly embody the improvements described in the specification to reduce, in part,
the amount of resources utilized in the process of performing the subject's intended function. Thus, claim 1 integrates any alleged judicial exception into a practical application such that claim 1 is not directed to an abstract idea and recites patent eligible subject matter under 35 U.S.C. § 101” [Arguments, pages 9-10].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully maintains that the present claims recite a judicial exception without significantly more. Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
Examiner respectfully maintains that the claims, in light of the recited additional elements, do not integrate the judicial exception into a practical application because the additional elements are not recited at a level of particular considered to demonstrate significantly more. For example, Examiner observes that the computing components, machine learning model and interface are not described in any particular detail.
A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes "the effect or result dissociated from any method by which maintaining the state is accomplished" and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result). See also O’Reilly v. Morse, 56 U.S. 62 (1854) (finding ineligible a claim for "the use of electromagnetism for transmitting signals at a distance"); The Telephone Cases, 126 U.S. 1, 209 (1888) (finding a method of "transmitting vocal or other sound telegraphically ... by causing electrical undulations, similar in form to the vibrations of the air accompanying the said vocal or other sounds," to be ineligible, because it "monopolize[d] a natural force" and "the right to avail of that law by any means whatever.").
With respect to the field of the present invention, Examiner observes that the claims are generally related to a “function to be performed” at a physical destination. Examiner respectfully maintains that applying a generic machine learning model to perform a generic function at a physical location does not provide any meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result. Thus, Examiner respectfully maintains that the present claims recite a judicial exception without significantly more. As such, Examiner remains unpersuaded.
Third, Applicant argues that “…Even if claim 1 is directed to an abstract idea and not directed to a practical application (a point Applicant docs not concede), claim 1 amounts to significantly more by reciting a non-generic, non-conventional series of steps. Under Step 2B, the claim must be evaluated to determine whether it includes additional elements that amount to significantly more than the judicial exception…
Claim 1 recites steps that describe receiving map application data via a data sharing communication from a map application, detecting a subject intent to travel to a physical destination, predicting a function by a machine learning model trained on a comparison of one or more previous interactions of the subject with the entity and one or more available services at the physical destination, generating a notification of options for performing the function, and causing output of an interface to collect data required for the function. Notably, the machine learning model trained to predict functions "based on a comparison of one or more previous interactions of the subject with the entity and one or more available services at the physical destination associated with the entity," which specifies how the machine learning model operates and what data it uses to generate predictions… claim 1 amounts to significantly more than any alleged judicial exception and thus recites patent eligible subject matter under 35 U.S.C. § 101…”[Arguments, pages 10-11].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully maintains that the present claims recite a judicial exception without significantly more. As stated in response to the above argument, Examiner maintains that applying a generic machine learning model to perform a generic function at a physical location does not provide any meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result. Thus, the claims are not considered to amount to an improvement to any particular field of technology or to the functioning of computers because the additional elements, when considered individually and in combination, are not recited at a level of specificity required to demonstrate the alleged improvement.
Relevant examples that the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
iv. Delivering broadcast content to a portable electronic device such as a cellular telephone, when claimed at a high level of generality, Affinity Labs of Tex. v. Amazon.com, 838 F.3d 1266, 1270, 120 USPQ2d 1210, 1213 (Fed. Cir. 2016); Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
The additional recited elements do not amount to significantly more than the abstract idea for the reasons discussed in Step 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
Thus, both individually or in combination, these limitations do not add significantly more to the judicial exception. As such, Examiner remains unpersuaded.
35 USC § 103 Rejections
First, Applicant argues that “Claim 1 recites "predicting, by a machine learning model trained to predict subject functions associated with the entity based on a comparison of one or more previous interactions…” which is not taught or suggested by the combination of Maugans, Chauhan and Venetianer.
The Office alleges that Maugans teaches "one or more available services at the physical destination associated with the entity" because it allegedly describes "services available at a particular location" and that "online data can be combined with physical data including data from a POS terminal at a physical location, wherein the POS terminal represents a sales service available at the particular physical location."…
However, such disclosure merely describes using POS terminal purchasing data as an input to identify buying patterns from user browsing behavior. The mere existence of a POS terminal at a location, from which Maugans describes collecting associated transaction data, plainly does not teach or
suggest "one or more available services at a physical location associated with an entity" as recited in
claim 1.
Additionally, where Maugans describes that determining "targeted products and/or services for a platform user" by "scraping/parsing a platform user's webpage for key elements" and that these elements "serve as a base reference point when analyzing other webpages to determine in-market or interest status for a consumer," such disclosure merely describes determining what products or services a webpage publisher offers so the system can identify consumers who are in-market for those product or service offerings. Maugans at [0087]. Such disclosure similarly does not teach or suggest "one or more available services" at a specific physical destination associated with an entity as recited in claim 1…
Maugans is describing a pattern-matching technique based on user browsing data to determine whether someone is likely to buy an online product or service, which does not teach or suggest a comparison of a specific subject's interaction history against a specific location's available services to predict what function the subject intends to perform at that location as recited in claim 1… Chauhan does not cure the deficiencies of Maugans… Venetianer does not cure the deficiencies of Maugans and Chauhan…” [Arguments, pages 11-13].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the art of record renders the present claims obvious. First, with respect to the argument that Maugans does not teach or suggest "one or more available services at a physical location associated with an entity", Examiner disagrees and directs the Applicant to (Maugans, ¶ 58, One possible embodiment of the software application may be provided by “In-Market-Audiences™” suite of products and services. Accordingly, the user 105 may provide, for example, indication of a list of webpages, indication of one or more products and/or services offered (discloses indication of available services) by a webpage of the list of webpages and indication of one or more campaigns to be targeted to the consumers with a detected interest level (hereinafter referred to “in-market consumers”). In response, the platform may identify in-market consumers based on online behavior and enable the user 105 to view the in-market consumers along with confidence values associated with products and/or services corresponding to which the in-market consumers are identified), and to (Id., ¶ 62, In one example, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, a neighborhood, a municipality, a mileage radius, or other type of location information).
Here, Maugans discloses an indication of one or more products and/or services offered by a webpage. Examiner combines this disclosure with the POS terminal and user-location-mapping elements of Maugans (Id., ¶ 62) to render obvious the indication of "one or more available services at a physical location associated with an entity" as recited in claim 1.
Thus, Examiner respectfully maintains that Maugans alone renders the above-argued limitations of claim 1 obvious. As such, Examiner remains unpersuaded.
Second, Applicant argues that “Claim 5 recites "determining, by the application and based on the current location data associated with the subject, that the physical destination is not a location, among locations with an ATM, that the subject would arrive at the soonest" and "instructing the map application, via the application, to reroute the subject to the location with an ATM that the subject would arrive at the soonest," which is not taught or suggested by Chauhan…
Chauhan describes a proximity consideration, but it is merely one weighting factor among several possible weighting factors (e.g., queue size, slot availability, channel preference, language reference, customer type) for selecting a resolution channel type. Chauhan at [0051]. Further, Chauhan describes a system health monitor that "may automatically direct the customer to an alternative channel (mobile application, website, etc.)" when the current channel has a system health issue. Chauhan at [0043]. However, such disclosure merely describes directing a customer to a different software interface when the current one fails, not one application instructing a separate map application to change the subject's physical travel destination as recited in claim 5…” [Arguments, pages 13-14].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and directs the Applicant to (Chauhan, ¶ 51, Time-to-reach 620 for a given channel may be influenced by factors such as queue size and slot availability for the channel and, if applicable, the proximity of the customer's current location to the channel. Proximity to the channel may be particularly relevant for channels such as an in-person appointment 680 or an ATM 690. (discloses deposit using an ATM) A customer's channel preference 630 and language preference 640 may be determined from user input and/or through machine learning based on the user's previous interactions with the computing platform).
Here, in contrast to the above-argument, Chauhan routes the users to the preferred channel (e.g. an ATM) based not only on system health issues but also based on the issue which needs to be resolved and on the proximity to the user’s location. Examiner combines these elements of Chauhan with the “map-based visualization” elements of the primary reference Maugans to render the above-argued limitation obvious. As such, Examiner remains unpersuaded.
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 1-6, 8, 10-17 and 19-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-6, 8, 10-17 and 19-24 are directed to statutory categories, namely a process (claims 1-6, 8-9 and 21), an article of manufacture (claims 10-17, 19 and 22) and a machine (claim 20).
Step 2A, Prong 1: Claims 1, 10 and 20 in part, recite the following abstract idea:
… A method comprising: receiving, via a data sharing communication from …associated with a subject, map application data; detecting, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination, predicting by… trained to predict subject functions associated with the entity based on a comparison of one or more pervious interactions of the subject with the entity and one or more available services at the physical destination associated with the entity, a function, associated with the subject intent, to be performed at the physical destination, wherein the function requires certain data associated with the subject; based on the function to be performed, generating a notification, in… of one or more options for performing the function; and based on receiving a selection of the one or more options for performing the function, causing output of … to input certain data associated with the subject, … wherein the data associated with the subject is required to perform the function. [Claim 1],
…perform steps comprising: receiving, via a data sharing communication from …and associated with an entity, map application data comprising a physical destination associated with an entity; detecting, based on the map application data, a subject intent to travel to a physical destination associated with the entity; based on detecting the subject intent to travel to the physical destination, predicting by… trained to predict subject functions associated with the entity based on a comparison of one or more pervious interactions of the subject with the entity and of one or more available services at the physical destination associated with the entity, a function, associated with the subject intent, to be performed at the physical destination associated with the entity, wherein the function requires certain data associated with the subject; based on the function to be performed, sending, to the subject, a notification of one or more options for performing the function; and based on receiving a selection of the one or more options for performing the function, causing output of… for collecting certain data associated with the subject required to perform the function the subject intends to perform [Claim 10],
…receive, via a data sharing communication from … and associated with an entity, map application data comprising a physical destination associated with an entity; detect, based on the map application data, a subject intent to travel to the physical destination associated with the entity;
based on detecting the subject intent to travel to the physical destination, predicting by… trained to predict subject functions associated with the entity based on a comparison of one or more pervious interactions of the subject with the entity and of one or more available services at the physical destination associated with the entity, a function, associated with the subject intent, to be performed at the physical destination associated with the entity, wherein the function requires certain data associated with the subject; and based on the function to be performed, sending, to the subject, one or more options for performing the function without travelling to the physical destination; and based on a selection of the one or more options for performing the function without travelling to the physical destination, causing output of … required for performing the function [Claim 20].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, displaying to a user options to perform a function based on determined intent is considered describe managing personal behavior and following rules or instructions. As such, claims 1, 10 and 20 recite concepts identified as abstract ideas.
The dependent claims recite limitations relative to the independent claims, including, for example:
…prior to generating the notification, prompting the subject, via…, to confirm the function to be performed, and receiving subject input, based on the prompting, confirming the function to be performed. [Claim 2],
…scheduling, in the application and based on the function to be performed, one of an appointment for the subject with a customer service agent at the physical destination or an appointment with a customer service agent that is not in-person; and based on scheduling the appointment, generating a notification, …, to notify the subject of the appointment [Claim 3],
…wherein the function is depositing a check into an account associated with the subject, and an option comprises depositing the check, using the application, into the account associated with the subject, and wherein the method further comprises: providing the subject …, instructions for depositing the check using the application [Claim 4],
…wherein another option comprises depositing the check, using…, into the account associated with the subject, wherein the map application data further comprises current location data associated with the subject, and wherein the method further comprises: determining, by the application and based on the current location data associated with the subject, that the physical destination is not a location, among locations with …, that the subject would arrive at the soonest; and instructing the map application, via …, to reroute the user to the location with … that the subject would arrive at the soonest [Claim 5],
…wherein the map application data comprises current location data associated with the subject, and the method further comprises: determining a plurality of travel options for traveling to the physical destination, and notifying the subject of the plurality of travel options [Claim 6],
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1, 10 and 20 only recite the following additional elements –
…a map application executing on a mobile computing device, and via an application on the mobile computing device…; …a machine learning model… ; … the application… an interface… via the mobile computing device… [Claim 1],
… One or more non-transitory media storing instructions that, when executed by one or more processors, cause the one or more processors to… a map application executing on a mobile computing device of a subject, and via an application on the mobile computing device…; …a machine learning model… an interface [Claim 10],
An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to… a map application executing on a mobile computing device associated with a subject, via an application on the mobile computing device…; a machine learning model… a service of the application… [Claim 20].
The dependent claims recite the following new additional elements –
…at least one of an email, a text message or a push notification… [Claims 2 and 11],
…an ATM… [Claim 5],
The apparatus and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1, 10 and 20 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent claims 1, 10 and 20 only recite the following additional elements –
…a map application executing on a mobile computing device, and via an application on the mobile computing device…; …a machine learning model… ; … the application… an interface… via the mobile computing device… [Claim 1],
… One or more non-transitory media storing instructions that, when executed by one or more processors, cause the one or more processors to… a map application executing on a mobile computing device of a subject, and via an application on the mobile computing device…; …a machine learning model… an interface [Claim 10],
An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to… a map application executing on a mobile computing device associated with a subject, via an application on the mobile computing device…; a machine learning model… a service of the application… [Claim 20].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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 1-5, 10-16 and 19-24 are rejected under 35 U.S.C. 103 as being unpatentable over Maugans, III et al., U.S. Publication No. 2019/0213612 [hereinafter Maugans] in view of Chauhan et al., U.S. Publication No. 2018/0357083 [hereinafter Chauhan] and in further view of Venetianer et al., U.S. Publication No. 2021/0358250 [hereinafter Venetianer].
Regarding Claim 1, Maugans discloses …A method comprising: receiving, via a data sharing communication from a map application executing on a mobile computing device, and via an application on the mobile computing device associated with a subject, map application data (Maugans, ¶ 43, Consistent with embodiments of the present disclosure, an online platform for providing map-based visualizations of user interaction data (discloses map application data) (also referred to herein as the “platform”) may be provided. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope. The online platform may be used by individuals or companies to identify aspects about the world of consumers. The aspects may include, by way of non-limiting example, who may be in-market for a product and/or a service with a calculated degree of confidence. Accordingly, targeted information, such as, but not limited to, advertisements may be presented to such consumers in order to aid the consumers to make informed buying decisions while also enhancing the likelihood of a user making a purchase of the product and/or service. Although embodiments of the present disclosure may be disclosed with reference to a “webpage publisher,” “advertiser,” “agency,” or a “content provider” as a platform user, any individual or entity may be a platform user), (Id., ¶ 58, FIG. 1A illustrates one possible operating environment through which a platform consistent with embodiments of the present disclosure may be provided. By way of non-limiting example, an online platform 100 for predicting consumer interest level may be hosted on, for example, a cloud computing service 1300. In some embodiments, the platform 100 may be hosted on a server 1300. The centralized server may communicate with other network entities, such as, for example, a plurality of webpage servers (e.g. web server 1 and 2) hosting a plurality of webpages and a user device (e.g. laptop computer, smartphone, tablet computer, desktop computer etc.). Additionally, in some embodiments, the centralized server may also communicate with other entities such as databases, wearable devices, Point Of Sales (POS) terminals etc. In general, the centralized server may be configured to communicate with any entity capable of providing user behavior data that is representative of a buying intention of a consumer. A user 105, such as a manager of the online platform 100 and/or an administrator of a webpage may access platform 100 through a software application. The software application may be embodied as, for example, but not be limited to, a webpage, a web application, a desktop application (discloses map application executing on a mobile device), and a mobile application compatible with a computing device 1300), (Id., ¶ 89, when a user engages the platform 100, a code, such as Javascript code, embedded in the webpage may execute a method 1200 such that the online platform for providing map based visualizations of user interaction data presents an illustrative display screen representing the behavior data as illustrated in FIG. 3. Accordingly, data presentation screen 300, displays an embodiment of a B2B view. Additionally, callout 302, shows a graphical map display in accordance with the present disclosure. Furthermore, callout 304, indicates a representation of the B2B menu in accordance with the present disclosure);
…predicting, by a machine learning model trained to predict subject functions associated with the entity based on a comparison of one or more previous interactions of the subject with the entity and one or more available services at the physical destination associated with the entity a function, associated with the subject intent to be performed, at the physical destination, wherein the function requires certain data associated with the subject (Maugans, ¶ 42, while many aspects and features relate to, and are described in, the context of in-market status with respect to products and/or services, embodiments of the present disclosure are not limited to use only in this context. For example, the platform may also be used to identify fine-grained interests and/or intentions of performing actions (e.g. attending an event, (discloses intent to travel to a location) performing a physical activity, meeting a person, etc.). Furthermore, it should be understood that the location of any user may be an approximation based on a number of factors), (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) (discloses machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 45, Embodiments of a platform described herein may provide visualizations for the various aspects of the consumers. Possible user of the platform may be, for example, webpage publisher, advertiser, agency, or a content provider in order to determine, for example, which advertisement media to present, how to target the advertisements, and who to display the advertisements to. A webpage publisher may join a network of publishers (e.g. an ad-network) in order to collectively determine in-market status of consumers. The network may be associated with a plurality of webpages, each tracking-enabled. Tracking the consumers' online behavior may include identifying webpages visited by the consumers when those webpages are, for example, part of the network. In an instance, this may be accomplished by aggregating a large network of webpage publishers who have a common element on their webpage. The common element may be the use of a network recognized cookie, or identifier, for each visitor who accesses any one webpage of the network of webpages. In some instances, multiple such networks of webpages may associate, share or sell information amongst each other to build larger networks, thereby expanding the webpage base for online behavioral tracking), (Id., ¶ 58, One possible embodiment of the software application may be provided by “In-Market-Audiences™” suite of products and services. Accordingly, the user 105 may provide, for example, indication of a list of webpages, indication of one or more products and/or services offered (discloses indication of available services) by a webpage of the list of webpages and indication of one or more campaigns to be targeted to the consumers with a detected interest level (hereinafter referred to “in-market consumers”). In response, the platform may identify in-market consumers based on online behavior and enable the user 105 to view the in-market consumers along with confidence values associated with products and/or services corresponding to which the in-market consumers are identified), (Id., ¶ 62, In one example, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. (discloses sales services available at a physical location) The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (discloses comparing previous interactions with available sales services) (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. (discloses data associated with the subject) Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, a neighborhood, a municipality, a mileage radius, or other type of location information), (Id., ¶ 87, According to some embodiments, the method may include a setup or ‘on-boarding’ phase, in which the targeted products and/or services for a platform user may be determined. In some embodiments, determining the products and/or services may be performed automatically by scraping/parsing a platform user's webpage for key elements. Alternatively, the platform user may provide inputs indicating the target products and/or services. Accordingly, in some instances, key elements associated with the webpage offering the products and/or services may be determined. These key elements may then serve as a base reference point when analyzing other webpages to determine in-market or interest status for a consumer. Having the base reference point set, the platform may be enabled to perform at least one of the following stages, in any order, at any time), (Id., ¶ 110, Upon analysis, the method 1200 may further include a stage 1212 displaying profile behavior data of tracked users upon selecting a data point on the location based user interface. For example, displaying profile (i.e. behavior data) of tracked users upon selecting a data-point on the location based user interface);
and based on receiving a selection of the one or more options for performing the function, causing output of an interface to input certain data, associated with the subject, via the mobile computing device, wherein the data associated with the subject is required to perform the function (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 75, Having determined the various categories and in-market status of the consumers, user interface module 145 may provide a visualization of the categorized and in-market data on the consumers. The visualization may further be geographic-specific, plotting consumer identifiers on a map. The consumers that are displayed may be determined by a plurality of filters and parameters specified by user 105 through a filter component 147, enabling a user to select parameters associated with the consumers they'd like to identify within the visualization).
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …detecting, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…; based on the function to be performed, generating a notification, in the application, of one or more options for performing the function.
However, Chauhan discloses … based on the function to be performed, generating a notification, in the application, of one or more options for performing the function. (Chauhan, ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. (discloses notification with options for performing the function) The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A.), (Id., ¶ 36, Information about the SQL Server status may be presented in a graphical user interface (GUI) format where status information for all of the listed database servers is presented in one integrated view in an automated manner. A monitoring process may read a list of SQL Server Instances from a designated Server detail repository (in form of a database) of organization or from a flat text input file and then connects to each listed SQL server to query the System Catalogs of the SQL Server engine. Because the monitoring process runs from a central server, configuration demand at the SQL server's side is circumvented. The monitoring process interprets the received information from the SQL servers and updates the GUI. By monitoring and obtaining additional information about SQL features for specified servers through the GUI, the database administrator or any other user (or self-learning analytics engine) may then report and/or fix detected issues. The processes may use a 32-bit operating system, thus circumventing a complicated monitoring infrastructure that demands extra skill sets and significant cost with infrastructure dependency).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and options elements of Chauhan in the analogous art of autonomous helper agents.
The motivation for doing so would have been to improve “the accuracy of a customer feedback system, in terms of relevance to a matter at hand, … by automatically selecting topics that are pertinent to input received from the customer. In another example, the effectiveness of a customer feedback system is improved by automatically identifying appropriate times for soliciting customer feedback... In yet another example, the versatility of a customer feedback system is improved by automatically cataloging features that are not presently available to address a particular customer need, so as to help identify a need for new product features.” (Chauhan, ¶ 16), wherein such improvements benefit Maugans method which seeks provide an improved ability “to determine if a user visiting the webpage is ‘in-market’ for products and/or services that the webpage publisher provides. This is advantageous because if the user is ‘in-market’, the webpage publisher may execute an appropriate marketing or sales campaign to increase the likelihood that the user converts to a customer. (See the '193 disclosure.)” [Chauhan, ¶ 16; Maugans, ¶47].
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans and Chauhan does not explicitly disclose …detecting, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…
However, Venetianer discloses … predicting, based on the map application data, that a user intends to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination… (Venetianer, ¶ 26, the personalized intent prediction systems described herein may employ Artificial Intelligence (AI) and Machine Learning (ML) techniques that enable performance improvements over existing intent prediction methods. For example, in some embodiments, a neural network or machine learning model may be trained to more accurately predict, based on personal statistical models, whether a particular person intends to enter a secure location or access restricted equipment (discloses intent to travel to a physical destination) than when using existing intent prediction techniques that are based solely on aggregated trajectory information associated with multiple persons. The neural network or machine learning model may be trained over a myriad of statistical, historical, temporal, and contextual information associated with historical approaches by the particular person to determine whether the particular person intends to enter the secure location or access the restricted equipment on a detected approach. Unlike with existing intent prediction methods, the personalized intent prediction systems described herein explicitly model and predict the intent of individual users based on learned approach behaviors matched with their identities, providing an extra layer of security for access control systems), (Id., ¶ 43, In at least some embodiments, intent prediction processing unit 120 may include a microprocessor configured to execute program instructions that implement a neural network or machine learning model trained for personalized intent prediction. More specifically, the neural network or machine learning model may be trained for generating a personalized intent score 125 indicating the likelihood that a given person intends to enter a secure location or access restricted equipment based, at least in part, on a personal statistical model for the given person. In some embodiments, the intent prediction processing unit 120 may include a graphics processing unit (GPU) or a vision processing unit or video processing unit, either of which may be referred to as a VPU, configured to perform certain aspects of a process for personalized intent prediction. In some embodiments, other program instructions, when executed by the microprocessor, may perform a process for training a neural network or machine learning model to perform such predictions. Selected elements of an example intent prediction processing unit 120 are illustrated in FIG. 6 and described in more detail below. In other embodiments, system 100 may include more, fewer, or different elements than those illustrated in FIG. 1).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans and the notification and options elements of Chauhan to include the travel intent prediction elements of Venetianer in the analogous art of personalized intent predictions.
The motivation for doing so would have been to improve user experience by “employ[ing] Artificial Intelligence (AI) and Machine Learning (ML) techniques that enable performance improvements over existing intent prediction methods. For example, in some embodiments, a neural network or machine learning model may be trained to more accurately predict, based on personal statistical models” (Venetianer, ¶ 26), wherein such improvements would benefit Chauhan’s method which seeks to improve “the accuracy of a customer feedback system, in terms of relevance to a matter at hand, … by automatically selecting topics that are pertinent to input received from the customer. In another example, the effectiveness of a customer feedback system is improved by automatically identifying appropriate times for soliciting customer feedback... In yet another example, the versatility of a customer feedback system is improved by automatically cataloging features that are not presently available to address a particular customer need, so as to help identify a need for new product features.” (Chauhan, ¶ 16), and wherein such improvements would further benefit Maugans’ method which seeks provide an improved ability “to determine if a user visiting the webpage is ‘in-market’ for products and/or services that the webpage publisher provides. This is advantageous because if the user is ‘in-market’, the webpage publisher may execute an appropriate marketing or sales campaign to increase the likelihood that the user converts to a customer. (See the '193 disclosure.)” [Venetianer, ¶ 26; Chauhan, ¶ 16; Maugans, ¶47].
Regarding Claim 2, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …further comprising: prior to generating the notification, prompting the subject, via at least one of an email, a text message or a push notification, to confirm the predicted function, and receiving subject input, based on the prompting, confirming the predicted function.
However, Chauhan discloses …further comprising: prior to generating the notification, prompting the subject, via at least one of an email, a text message or a push notification, to confirm the function to be performed, and receiving subject input, based on the prompting, confirming the function to be performed (Chauhan, ¶ 19, In accordance with one or more embodiments, the computing platform, responsive to receiving the first content stream associated with the customer session, via the communication interface, identifies an existing feature, generates an inquiry asking the customer whether he or she wishes to engage the existing feature, and causes the inquiry to be displayed on the remote client device. In the event that an existing feature is not available or one cannot be identified based on the information provided by the customer, the computing platform may automatically update a feature catalog to include information regarding the customer session. When a new feature relevant to the customer session is developed or in the process of development, the computing platform may automatically generate and transmit a communication to the customer (e.g., by e-mail, text message, or the like) to alert the customer (discloses text message)), (Id., ¶ 41, Once the feedback is collected and sorted, it then may be processed to determine an appropriate next course of action to assist the customer. A natural language processing (NLP) system 330 may be used for suggestion discovery, such as is described in Application Publication No. US 2018/0145996 A1, entitled “Network Security Database Sorting Tool,” the disclosure of which is hereby incorporated by reference in its entirety. The NPL processing system basically involves identifying and prioritizing keywords to help decipher a particular verbal response. This step may involve a “handshake” during which the customer is asked to confirm that his or her feedback relates to the topic identified by the NPL processing system. (discloses user confirmation) In the case of a “no” response, the customer may be prompted to rephrase the response to better articulate the particular topic or issue of concern).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and options elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 3, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …further comprising: scheduling, in the application and based on the function to be performed, one of an appointment for the subject with a customer service agent at the physical destination or an appointment with a customer service agent that is not in-person; and based on scheduling the appointment, generating a notification, in the application, to notify the subject of the appointment.
However, Chauhan discloses …further comprising: scheduling, in the application and based on the function to be performed, one of an appointment for the subject with a customer service agent at the physical destination or an appointment with a customer service agent that is not in-person (Chauhan, ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment (discloses in-person appointments) 594, or pursue other available channels such as online assistance), (Id., ¶ 50, In accordance with one or more aspects, a shortest path toward issue resolution may be constructed for each channel by assigning weightage to key parameters of the channel and building a minimum-spanning tree. With reference to FIG. 6, a filtered channel for a given customer 601 may include such criteria as time-to-reach 620, channel preference 630, language preference 640, and customer type 650. Available channels may include, for example, a virtual assistant 660, online assistance 670, an in-person appointment 680, and an automated teller machine (ATM) 690. A weightage then may be assigned to each path based on the preselected criteria. The sums of the possible paths “A,” “B,” “C,” “D,” and so on, may be computed as illustrated at steps 692, 694, 696, and 698, to construct a minimum-spanning tree. Based on the minimum-spanning tree, the channel which takes the shortest time to reach may be identified and selected);
and based on scheduling the appointment, generating a notification, in the application, to notify the subject of the appointment (Id., ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user (discloses generating a notification) that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A), (Id., ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment 594, or pursue other available channels such as online assistance).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and scheduling elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 4, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose … wherein the function is depositing a check into an account associated with the subject, and an option comprises depositing the check, using the application, into the account associated with the subject, and wherein the method further comprises: providing the subject, via the application, instructions for depositing the check using the application.
However, Chauhan discloses …wherein the function is depositing a check into an account associated with the subject, and an option comprises depositing the check, using the application, into the account associated with the subject, and wherein the method further comprises: providing the subject, via the application, instructions for depositing the check using the application (Chauhan, ¶ 40, Voice feedback 325 received from the customer may be converted into text to aid further processing. As schematically shown as item 350, text classification techniques may be used to identify the topic/feature and any associated sentiment with a particular customer feedback. The initial gathering of feedback may include identifying the topic 350A and the particular activity or type of transaction 352A (check deposit, (discloses depositing a check) fund transfer, or the like). This step also may include identifying whether the transaction involved customer sentiment 350B and, if so, whether the customer's response was positive or negative 352B. Any customer comments or other submissions 350C may be collected and analyzed for trends 352C, for example to help gauge initial customer response to a new feature offered. A trend graph may be developed to help quantify the sentiment trends of a particular feature), (Id., ¶ 30, one or more application programs 119 used by the computing device 101, according to an illustrative embodiment, may include computer executable instructions for invoking user functionality related to communication including, for example, email, short message service (SMS), and voice input and speech-recognition applications), (Id., ¶ 48, the autonomous helper agent 560 may establish a connection with a solutions database 580 to determine appropriate steps to resolve the issue. An implementer 590 may transmit instructions to the autonomous helper agent 560 identifying steps that may be executed to resolve the issue. The implementer 590 may identify one or more channels 592 for manual assistance. The steps that are taken by the systems administrator 592 toward resolution of the issue may be recorded and transmitted to the solutions database 580 so that subsequent occurrences of the issue may be resolved without needing to again contact the systems administrator 592. Upon resolution of the issue, a suitable notification, such as the user interface illustrated in FIG. 7B, discussed in more detail below, may be transmitted to the user computing device 500).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and check depositing elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 5, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 4…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose … wherein another option comprises depositing the check, using an ATM, into the account associated with the subject, wherein the map application data further comprises current location data associated with the subject, and wherein the method further comprises: determining, by the application and based on the current location data associated with the subject, that the physical destination is not a location, among locations with an ATM, that the subject would arrive at the soonest; and instructing the map application, via the application, to reroute the subject to the location with an ATM that the subject would arrive at the soonest.
However, Chauhan discloses … wherein another option comprises depositing the check, using an ATM, into the account associated with the subject, wherein the map application data further comprises current location data associated with the subject, and wherein the method further comprises: determining, by the application and based on the current location data associated with the subject, that the physical destination is not a location, among locations with an ATM, that the subject would arrive at the soonest; and instructing the map application, via the application, to reroute the subject to the location with an ATM that the subject would arrive at the soonest (Chauhan, ¶ 51, Time-to-reach 620 for a given channel may be influenced by factors such as queue size and slot availability for the channel and, if applicable, the proximity of the customer's current location to the channel. Proximity to the channel may be particularly relevant for channels such as an in-person appointment 680 or an ATM 690. (discloses deposit using an ATM) A customer's channel preference 630 and language preference 640 may be determined from user input and/or through machine learning based on the user's previous interactions with the computing platform), (Id., ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A.), (Id., ¶ 40, Voice feedback 325 received from the customer may be converted into text to aid further processing. As schematically shown as item 350, text classification techniques may be used to identify the topic/feature and any associated sentiment with a particular customer feedback. The initial gathering of feedback may include identifying the topic 350A and the particular activity or type of transaction 352A (check deposit, (discloses depositing a check) fund transfer, or the like). This step also may include identifying whether the transaction involved customer sentiment 350B and, if so, whether the customer's response was positive or negative 352B. Any customer comments or other submissions 350C may be collected and analyzed for trends 352C, for example to help gauge initial customer response to a new feature offered. A trend graph may be developed to help quantify the sentiment trends of a particular feature).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and check depositing elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 10, Maugans discloses …One or more non-transitory media storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising: receiving, via a data sharing communication from a map application executing on a mobile computing device of a subject, and via an application on the mobile computing device and associated with an entity, map application data comprising a physical destination associated with an entity (Maugans, ¶ 43, Consistent with embodiments of the present disclosure, an online platform for providing map-based visualizations of user interaction data (discloses map application data) (also referred to herein as the “platform”) may be provided. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope. The online platform may be used by individuals or companies to identify aspects about the world of consumers. The aspects may include, by way of non-limiting example, who may be in-market for a product and/or a service with a calculated degree of confidence. Accordingly, targeted information, such as, but not limited to, advertisements may be presented to such consumers in order to aid the consumers to make informed buying decisions while also enhancing the likelihood of a user making a purchase of the product and/or service. Although embodiments of the present disclosure may be disclosed with reference to a “webpage publisher,” “advertiser,” “agency,” or a “content provider” as a platform user, any individual or entity may be a platform user), (Id., ¶ 79, Embodiments of the present disclosure provide a hardware and software platform operative by a set of methods and computer-readable media comprising instructions configured to operate the aforementioned modules and computing elements in accordance with the methods. The following depicts an example of a method of a plurality of methods that may be performed by at least one of the aforementioned modules. Various hardware components may be used at the various stages of operations disclosed with reference to each module), (Id., ¶ 58, FIG. 1A illustrates one possible operating environment through which a platform consistent with embodiments of the present disclosure may be provided. By way of non-limiting example, an online platform 100 for predicting consumer interest level may be hosted on, for example, a cloud computing service 1300. In some embodiments, the platform 100 may be hosted on a server 1300. The centralized server may communicate with other network entities, such as, for example, a plurality of webpage servers (e.g. web server 1 and 2) hosting a plurality of webpages and a user device (e.g. laptop computer, smartphone, tablet computer, desktop computer etc.). Additionally, in some embodiments, the centralized server may also communicate with other entities such as databases, wearable devices, Point Of Sales (POS) terminals etc. In general, the centralized server may be configured to communicate with any entity capable of providing user behavior data that is representative of a buying intention of a consumer. A user 105, such as a manager of the online platform 100 and/or an administrator of a webpage may access platform 100 through a software application. The software application may be embodied as, for example, but not be limited to, a webpage, a web application, a desktop application (discloses map application executing on a mobile device), and a mobile application compatible with a computing device 1300), (Id., ¶ 62, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, (discloses destination) a neighborhood, a municipality, a mileage radius, or other type of location information), (Id., ¶ 89, when a user engages the platform 100, a code, such as Javascript code, embedded in the webpage may execute a method 1200 such that the online platform for providing map based visualizations of user interaction data presents an illustrative display screen representing the behavior data as illustrated in FIG. 3. Accordingly, data presentation screen 300, displays an embodiment of a B2B view. Additionally, callout 302, shows a graphical map display in accordance with the present disclosure. Furthermore, callout 304, indicates a representation of the B2B menu in accordance with the present disclosure);
…predicting, by a machine learning model trained to predict subject functions associated with the entity based on a comparison of one or more previous interactions of the subject with the entity and one or more available services at the physical destination associated with the entity a function, associated with the subject intent to be performed, at the physical destination associated with the entity, wherein the function requires certain user data (Maugans, ¶ 42, while many aspects and features relate to, and are described in, the context of in-market status with respect to products and/or services, embodiments of the present disclosure are not limited to use only in this context. For example, the platform may also be used to identify fine-grained interests and/or intentions of performing actions (e.g. attending an event, (discloses intent to travel to a location) performing a physical activity, meeting a person, etc.). Furthermore, it should be understood that the location of any user may be an approximation based on a number of factors), (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) (discloses machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 45, Embodiments of a platform described herein may provide visualizations for the various aspects of the consumers. Possible user of the platform may be, for example, webpage publisher, advertiser, agency, or a content provider in order to determine, for example, which advertisement media to present, how to target the advertisements, and who to display the advertisements to. A webpage publisher may join a network of publishers (e.g. an ad-network) in order to collectively determine in-market status of consumers. The network may be associated with a plurality of webpages, each tracking-enabled. Tracking the consumers' online behavior may include identifying webpages visited by the consumers when those webpages are, for example, part of the network. In an instance, this may be accomplished by aggregating a large network of webpage publishers who have a common element on their webpage. The common element may be the use of a network recognized cookie, or identifier, for each visitor who accesses any one webpage of the network of webpages. In some instances, multiple such networks of webpages may associate, share or sell information amongst each other to build larger networks, thereby expanding the webpage base for online behavioral tracking), (Id., ¶ 58, One possible embodiment of the software application may be provided by “In-Market-Audiences™” suite of products and services. Accordingly, the user 105 may provide, for example, indication of a list of webpages, indication of one or more products and/or services offered (discloses indication of available services) by a webpage of the list of webpages and indication of one or more campaigns to be targeted to the consumers with a detected interest level (hereinafter referred to “in-market consumers”). In response, the platform may identify in-market consumers based on online behavior and enable the user 105 to view the in-market consumers along with confidence values associated with products and/or services corresponding to which the in-market consumers are identified), (Id., ¶ 62, In one example, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. (discloses sales services available at a physical location) The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (discloses comparing previous interactions with available sales services) (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. (discloses data associated with the subject) Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, a neighborhood, a municipality, a mileage radius, or other type of location information), (Id., ¶ 87, According to some embodiments, the method may include a setup or ‘on-boarding’ phase, in which the targeted products and/or services for a platform user may be determined. In some embodiments, determining the products and/or services may be performed automatically by scraping/parsing a platform user's webpage for key elements. Alternatively, the platform user may provide inputs indicating the target products and/or services. Accordingly, in some instances, key elements associated with the webpage offering the products and/or services may be determined. These key elements may then serve as a base reference point when analyzing other webpages to determine in-market or interest status for a consumer. Having the base reference point set, the platform may be enabled to perform at least one of the following stages, in any order, at any time), (Id., ¶ 110, Upon analysis, the method 1200 may further include a stage 1212 displaying profile behavior data of tracked users upon selecting a data point on the location based user interface. For example, displaying profile (i.e. behavior data) of tracked users upon selecting a data-point on the location based user interface);
and based on receiving a selection of the one or more options for performing the function, causing output of an interface for collecting certain data associated with the subject required to perform the function the subject intends to perform (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 75, Having determined the various categories and in-market status of the consumers, user interface module 145 may provide a visualization of the categorized and in-market data on the consumers. The visualization may further be geographic-specific, plotting consumer identifiers on a map. The consumers that are displayed may be determined by a plurality of filters and parameters specified by user 105 through a filter component 147, enabling a user to select parameters associated with the consumers they'd like to identify within the visualization).
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …detecting, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…; based on the function to be performed, sending, to the subject, a notification of one or more options for performing the function.
However, Chauhan discloses …based on the function to be performed, sending, to the subject, a notification of one or more options for performing the function. (Chauhan, ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. (discloses notification with options for performing the function) The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A.), (Id., ¶ 36, Information about the SQL Server status may be presented in a graphical user interface (GUI) format where status information for all of the listed database servers is presented in one integrated view in an automated manner. A monitoring process may read a list of SQL Server Instances from a designated Server detail repository (in form of a database) of organization or from a flat text input file and then connects to each listed SQL server to query the System Catalogs of the SQL Server engine. Because the monitoring process runs from a central server, configuration demand at the SQL server's side is circumvented. The monitoring process interprets the received information from the SQL servers and updates the GUI. By monitoring and obtaining additional information about SQL features for specified servers through the GUI, the database administrator or any other user (or self-learning analytics engine) may then report and/or fix detected issues. The processes may use a 32-bit operating system, thus circumventing a complicated monitoring infrastructure that demands extra skill sets and significant cost with infrastructure dependency).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and options elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans and Chauhan does not explicitly disclose …detecting, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…
However, Venetianer discloses … predicting, based on the map application data, that a user intends to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination… (Venetianer, ¶ 26, the personalized intent prediction systems described herein may employ Artificial Intelligence (AI) and Machine Learning (ML) techniques that enable performance improvements over existing intent prediction methods. For example, in some embodiments, a neural network or machine learning model may be trained to more accurately predict, based on personal statistical models, whether a particular person intends to enter a secure location or access restricted equipment (discloses intent to travel to a physical destination) than when using existing intent prediction techniques that are based solely on aggregated trajectory information associated with multiple persons. The neural network or machine learning model may be trained over a myriad of statistical, historical, temporal, and contextual information associated with historical approaches by the particular person to determine whether the particular person intends to enter the secure location or access the restricted equipment on a detected approach. Unlike with existing intent prediction methods, the personalized intent prediction systems described herein explicitly model and predict the intent of individual users based on learned approach behaviors matched with their identities, providing an extra layer of security for access control systems), (Id., ¶ 43, In at least some embodiments, intent prediction processing unit 120 may include a microprocessor configured to execute program instructions that implement a neural network or machine learning model trained for personalized intent prediction. More specifically, the neural network or machine learning model may be trained for generating a personalized intent score 125 indicating the likelihood that a given person intends to enter a secure location or access restricted equipment based, at least in part, on a personal statistical model for the given person. In some embodiments, the intent prediction processing unit 120 may include a graphics processing unit (GPU) or a vision processing unit or video processing unit, either of which may be referred to as a VPU, configured to perform certain aspects of a process for personalized intent prediction. In some embodiments, other program instructions, when executed by the microprocessor, may perform a process for training a neural network or machine learning model to perform such predictions. Selected elements of an example intent prediction processing unit 120 are illustrated in FIG. 6 and described in more detail below. In other embodiments, system 100 may include more, fewer, or different elements than those illustrated in FIG. 1).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans and the notification and options elements of Chauhan to include the travel intent prediction elements of Venetianer in the analogous art of personalized intent predictions for the same reasons as stated for claim 1.
Regarding Claim 11, this claims recites limitations substantially similar to those in claim 2, and is rejected for the same reasons as stated above.
Regarding Claim 12, the combination of Maugans, Chauhan and Venetianer discloses …The one or more non-transitory media of claim 10…
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans and Chauhan does not explicitly disclose …wherein the instructions further cause the one or more processors to perform the steps of: prior to sending the notification, prompting the subject to confirm the function to be performed, and receiving input associated with the subject, based on the prompting, confirming the function to be performed.
However, Chauhan discloses …wherein the instructions further cause the one or more processors to perform the steps of: prior to sending the notification, prompting the subject to confirm the predicted function, and receiving input associated with the subject, based on the prompting, confirming the predicted function (Chauhan, ¶ 19, In accordance with one or more embodiments, the computing platform, responsive to receiving the first content stream associated with the customer session, via the communication interface, identifies an existing feature, generates an inquiry asking the customer whether he or she wishes to engage the existing feature, and causes the inquiry to be displayed on the remote client device. In the event that an existing feature is not available or one cannot be identified based on the information provided by the customer, the computing platform may automatically update a feature catalog to include information regarding the customer session. When a new feature relevant to the customer session is developed or in the process of development, the computing platform may automatically generate and transmit a communication to the customer (e.g., by e-mail, text message, or the like) to alert the customer (discloses text message)), (Id., ¶ 41, Once the feedback is collected and sorted, it then may be processed to determine an appropriate next course of action to assist the customer. A natural language processing (NLP) system 330 may be used for suggestion discovery, such as is described in Application Publication No. US 2018/0145996 A1, entitled “Network Security Database Sorting Tool,” the disclosure of which is hereby incorporated by reference in its entirety. The NPL processing system basically involves identifying and prioritizing keywords to help decipher a particular verbal response. This step may involve a “handshake” during which the customer is asked to confirm that his or her feedback relates to the topic identified by the NPL processing system. (discloses user confirmation) In the case of a “no” response, the customer may be prompted to rephrase the response to better articulate the particular topic or issue of concern).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and options elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 13, this claims recites limitations substantially similar to those in claim 3, and is rejected for the same reasons as stated above.
Regarding Claim 14, the combination of Maugans, Chauhan and Venetianer discloses …The one or more non-transitory media of claim 10…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose … wherein the instructions further cause the one or more processors to perform the step of: scheduling, based on the function to be performed, an appointment with a customer service agent that is not in-person; and based on scheduling the appointment, generating a notification to notify the subject of the appointment.
However, Chauhan discloses … wherein the instructions further cause the one or more processors to perform the step of: scheduling, based on the function to be performed, an appointment with a customer service agent that is not in-person (Chauhan, ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment (discloses virtual appointments) 594, or pursue other available channels such as online assistance), (Id., ¶ 50, In accordance with one or more aspects, a shortest path toward issue resolution may be constructed for each channel by assigning weightage to key parameters of the channel and building a minimum-spanning tree. With reference to FIG. 6, a filtered channel for a given customer 601 may include such criteria as time-to-reach 620, channel preference 630, language preference 640, and customer type 650. Available channels may include, for example, a virtual assistant 660, online assistance 670, an in-person appointment 680, and an automated teller machine (ATM) 690. A weightage then may be assigned to each path based on the preselected criteria. The sums of the possible paths “A,” “B,” “C,” “D,” and so on, may be computed as illustrated at steps 692, 694, 696, and 698, to construct a minimum-spanning tree. Based on the minimum-spanning tree, the channel which takes the shortest time to reach may be identified and selected);
and based on scheduling the appointment, generating a notification to notify the subject of the appointment (Id., ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user (discloses generating a notification) that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A), (Id., ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment 594, or pursue other available channels such as online assistance).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and scheduling elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 15, the combination of Maugans, Chauhan and Venetianer discloses …The one or more non-transitory media of claim 14…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …wherein the instructions further cause the one or more processors to perform the step of: recommending to the subject, based on the function to be performed, to schedule a time for the appointment with a customer service agent that is not in-person, wherein the scheduling is further based on receiving a scheduled time for the appointment from the subject.
However, Chauhan discloses …wherein the instructions further cause the one or more processors to perform the step of: recommending to the subject, based on the function to be performed, to schedule a time for the appointment with a customer service agent that is not in-person, wherein the scheduling is further based on receiving a scheduled time for the appointment from the subject (Chauhan, ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment (discloses virtual appointments) 594, or pursue other available channels such as online assistance), (Id., ¶ 50, In accordance with one or more aspects, a shortest path toward issue resolution may be constructed for each channel by assigning weightage to key parameters of the channel and building a minimum-spanning tree. With reference to FIG. 6, a filtered channel for a given customer 601 may include such criteria as time-to-reach 620, channel preference 630, language preference 640, and customer type 650. Available channels may include, for example, a virtual assistant 660, online assistance 670, an in-person appointment 680, and an automated teller machine (ATM) 690. A weightage then may be assigned to each path based on the preselected criteria. The sums of the possible paths “A,” “B,” “C,” “D,” and so on, may be computed as illustrated at steps 692, 694, 696, and 698, to construct a minimum-spanning tree. Based on the minimum-spanning tree, the channel which takes the shortest time to reach may be identified and selected);
and based on scheduling the appointment, generating a notification, in the application, to notify the user of the appointment (Id., ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user (discloses generating a notification) that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A), (Id., ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment 594, or pursue other available channels such as online assistance).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and scheduling elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 16, this claims recites limitations substantially similar to those in claim 4, and is rejected for the same reasons as stated above.
Regarding Claim 19, this claims recites limitations substantially similar to those in claim 9, and is rejected for the same reasons as stated above.
Regarding Claim 20, Maugans discloses …An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: receive, via a data sharing communication from a map application executing on a mobile computing device associated with a subject, via an application on the mobile computing device and associated with an entity, map application data comprising a physical destination associated with an entity (Maugans, ¶ 43, Consistent with embodiments of the present disclosure, an online platform for providing map-based visualizations of user interaction data (discloses map application data) (also referred to herein as the “platform”) may be provided. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope. The online platform may be used by individuals or companies to identify aspects about the world of consumers. The aspects may include, by way of non-limiting example, who may be in-market for a product and/or a service with a calculated degree of confidence. Accordingly, targeted information, such as, but not limited to, advertisements may be presented to such consumers in order to aid the consumers to make informed buying decisions while also enhancing the likelihood of a user making a purchase of the product and/or service. Although embodiments of the present disclosure may be disclosed with reference to a “webpage publisher,” “advertiser,” “agency,” or a “content provider” as a platform user, any individual or entity may be a platform user), (Id., ¶ 79, Embodiments of the present disclosure provide a hardware and software platform operative by a set of methods and computer-readable media comprising instructions configured to operate the aforementioned modules and computing elements in accordance with the methods. The following depicts an example of a method of a plurality of methods that may be performed by at least one of the aforementioned modules. Various hardware components may be used at the various stages of operations disclosed with reference to each module), (Id., ¶ 58, FIG. 1A illustrates one possible operating environment through which a platform consistent with embodiments of the present disclosure may be provided. By way of non-limiting example, an online platform 100 for predicting consumer interest level may be hosted on, for example, a cloud computing service 1300. In some embodiments, the platform 100 may be hosted on a server 1300. The centralized server may communicate with other network entities, such as, for example, a plurality of webpage servers (e.g. web server 1 and 2) hosting a plurality of webpages and a user device (e.g. laptop computer, smartphone, tablet computer, desktop computer etc.). Additionally, in some embodiments, the centralized server may also communicate with other entities such as databases, wearable devices, Point Of Sales (POS) terminals etc. In general, the centralized server may be configured to communicate with any entity capable of providing user behavior data that is representative of a buying intention of a consumer. A user 105, such as a manager of the online platform 100 and/or an administrator of a webpage may access platform 100 through a software application. The software application may be embodied as, for example, but not be limited to, a webpage, a web application, a desktop application (discloses map application executing on a mobile device), and a mobile application compatible with a computing device 1300), (Id., ¶ 62, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, (discloses destination) a neighborhood, a municipality, a mileage radius, or other type of location information), (Id., ¶ 89, when a user engages the platform 100, a code, such as Javascript code, embedded in the webpage may execute a method 1200 such that the online platform for providing map based visualizations of user interaction data presents an illustrative display screen representing the behavior data as illustrated in FIG. 3. Accordingly, data presentation screen 300, displays an embodiment of a B2B view. Additionally, callout 302, shows a graphical map display in accordance with the present disclosure. Furthermore, callout 304, indicates a representation of the B2B menu in accordance with the present disclosure);
…predict, by a machine learning model trained to predict subject functions associated with the entity based on a comparison of one or more previous interactions of the subject with the entity and one or more available services at the physical destination associated with the entity a function, associated with the subject intent to be performed, at the physical destination associated with the entity, wherein the function requires certain data associated with the subject (Maugans, ¶ 42, while many aspects and features relate to, and are described in, the context of in-market status with respect to products and/or services, embodiments of the present disclosure are not limited to use only in this context. For example, the platform may also be used to identify fine-grained interests and/or intentions of performing actions (e.g. attending an event, (discloses intent to travel to a location) performing a physical activity, meeting a person, etc.). Furthermore, it should be understood that the location of any user may be an approximation based on a number of factors), (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) (discloses machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 45, Embodiments of a platform described herein may provide visualizations for the various aspects of the consumers. Possible user of the platform may be, for example, webpage publisher, advertiser, agency, or a content provider in order to determine, for example, which advertisement media to present, how to target the advertisements, and who to display the advertisements to. A webpage publisher may join a network of publishers (e.g. an ad-network) in order to collectively determine in-market status of consumers. The network may be associated with a plurality of webpages, each tracking-enabled. Tracking the consumers' online behavior may include identifying webpages visited by the consumers when those webpages are, for example, part of the network. In an instance, this may be accomplished by aggregating a large network of webpage publishers who have a common element on their webpage. The common element may be the use of a network recognized cookie, or identifier, for each visitor who accesses any one webpage of the network of webpages. In some instances, multiple such networks of webpages may associate, share or sell information amongst each other to build larger networks, thereby expanding the webpage base for online behavioral tracking), (Id., ¶ 58, One possible embodiment of the software application may be provided by “In-Market-Audiences™” suite of products and services. Accordingly, the user 105 may provide, for example, indication of a list of webpages, indication of one or more products and/or services offered (discloses indication of available services) by a webpage of the list of webpages and indication of one or more campaigns to be targeted to the consumers with a detected interest level (hereinafter referred to “in-market consumers”). In response, the platform may identify in-market consumers based on online behavior and enable the user 105 to view the in-market consumers along with confidence values associated with products and/or services corresponding to which the in-market consumers are identified), (Id., ¶ 62, In one example, online behavioral data may be combined with purchasing data, retrieved from sales database, POS terminals etc. (discloses sales services available at a physical location) The combination may then be used to identify patterns of online browsing activity representing buying intentions. Such patterns may be identified by performing machine learning over the historical online browsing data (discloses comparing previous interactions with available sales services) (including purchasing data in some embodiments). Subsequently, the machine learning may be used to identify in-market status of the consumer based on currently received online behavior data. (discloses data associated with the subject) Additionally, users may view tracked online behavioral data. In some embodiments, the view may be filtered by various data points and categories. Further views may be correlated with a location, map, or graphical indication of the location associated with the tracked behavior. This location may be longitude, latitude, coordinates, Global Positioning System coordinates, an address, a neighborhood, a municipality, a mileage radius, or other type of location information), (Id., ¶ 87, According to some embodiments, the method may include a setup or ‘on-boarding’ phase, in which the targeted products and/or services for a platform user may be determined. In some embodiments, determining the products and/or services may be performed automatically by scraping/parsing a platform user's webpage for key elements. Alternatively, the platform user may provide inputs indicating the target products and/or services. Accordingly, in some instances, key elements associated with the webpage offering the products and/or services may be determined. These key elements may then serve as a base reference point when analyzing other webpages to determine in-market or interest status for a consumer. Having the base reference point set, the platform may be enabled to perform at least one of the following stages, in any order, at any time), (Id., ¶ 110, Upon analysis, the method 1200 may further include a stage 1212 displaying profile behavior data of tracked users upon selecting a data point on the location based user interface. For example, displaying profile (i.e. behavior data) of tracked users upon selecting a data-point on the location based user interface);
and based on selection of the one or more options for performing the function without travelling to the physical destination, causing output of service of the application required for performing the function (Id., ¶ 44, The present disclosure provides methods of and systems for predicting a consumer as being a part of a particularly category or, for example, in-market, based on collection and scoring (either statistically or using machine learning) of pieces of data extracted from webpages visited by the consumer based on the computer device associated with the consumer, such as a. The present disclosure provides methods of and systems for providing a location-based user interface for viewing online behavior data associated with individuals located in different places), (Id., ¶ 75, Having determined the various categories and in-market status of the consumers, user interface module 145 may provide a visualization of the categorized and in-market data on the consumers. The visualization may further be geographic-specific, plotting consumer identifiers on a map. The consumers that are displayed may be determined by a plurality of filters and parameters specified by user 105 through a filter component 147, enabling a user to select parameters associated with the consumers they'd like to identify within the visualization).
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …detect, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…; based on the function to be performed, sending, to the subject, one or more options for performing the function without travelling to the physical destination.
However, Chauhan discloses …based on the function to be performed, sending, to the subject, one or more options for performing the function without travelling to the physical destination (Chauhan, ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM, as applicable. (discloses notification with options for performing the function) The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A.), (Id., ¶ 36, Information about the SQL Server status may be presented in a graphical user interface (GUI) format where status information for all of the listed database servers is presented in one integrated view in an automated manner. A monitoring process may read a list of SQL Server Instances from a designated Server detail repository (in form of a database) of organization or from a flat text input file and then connects to each listed SQL server to query the System Catalogs of the SQL Server engine. Because the monitoring process runs from a central server, configuration demand at the SQL server's side is circumvented. The monitoring process interprets the received information from the SQL servers and updates the GUI. By monitoring and obtaining additional information about SQL features for specified servers through the GUI, the database administrator or any other user (or self-learning analytics engine) may then report and/or fix detected issues. The processes may use a 32-bit operating system, thus circumventing a complicated monitoring infrastructure that demands extra skill sets and significant cost with infrastructure dependency).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and options elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans and Chauhan does not explicitly disclose …detect, based on the map application data, a subject intent to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination…
However, Venetianer discloses … predicting, based on the map application data, that a user intends to travel to a physical destination associated with an entity; based on detecting the subject intent to travel to the physical destination… (Venetianer, ¶ 26, the personalized intent prediction systems described herein may employ Artificial Intelligence (AI) and Machine Learning (ML) techniques that enable performance improvements over existing intent prediction methods. For example, in some embodiments, a neural network or machine learning model may be trained to more accurately predict, based on personal statistical models, whether a particular person intends to enter a secure location or access restricted equipment (discloses intent to travel to a physical destination) than when using existing intent prediction techniques that are based solely on aggregated trajectory information associated with multiple persons. The neural network or machine learning model may be trained over a myriad of statistical, historical, temporal, and contextual information associated with historical approaches by the particular person to determine whether the particular person intends to enter the secure location or access the restricted equipment on a detected approach. Unlike with existing intent prediction methods, the personalized intent prediction systems described herein explicitly model and predict the intent of individual users based on learned approach behaviors matched with their identities, providing an extra layer of security for access control systems), (Id., ¶ 43, In at least some embodiments, intent prediction processing unit 120 may include a microprocessor configured to execute program instructions that implement a neural network or machine learning model trained for personalized intent prediction. More specifically, the neural network or machine learning model may be trained for generating a personalized intent score 125 indicating the likelihood that a given person intends to enter a secure location or access restricted equipment based, at least in part, on a personal statistical model for the given person. In some embodiments, the intent prediction processing unit 120 may include a graphics processing unit (GPU) or a vision processing unit or video processing unit, either of which may be referred to as a VPU, configured to perform certain aspects of a process for personalized intent prediction. In some embodiments, other program instructions, when executed by the microprocessor, may perform a process for training a neural network or machine learning model to perform such predictions. Selected elements of an example intent prediction processing unit 120 are illustrated in FIG. 6 and described in more detail below. In other embodiments, system 100 may include more, fewer, or different elements than those illustrated in FIG. 1).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans and the notification and options elements of Chauhan to include the travel intent prediction elements of Venetianer in the analogous art of personalized intent predictions for the same reasons as stated for claim 1.
Regarding Claim 21, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
Maugans further discloses …wherein the interface comprises: a document associated with the function comprising at least one portion of the document including pre-filled data associated with the subject (Id., ¶ 109, Upon analysis, the method 1200 may further include a stage 1210 determining the in-market (e.g., interest/propensity) status of the consumer in one or more of the consumer device. For example, a data field associated with the Unique ID may be set to ‘true,’ ‘in-market,’ or ‘interested’(discloses pre-filled data fields associated with the subject));
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …and notifying the subject to provide input for remaining portions of the document.
However, Chauhan discloses …and notifying the subject to provide input for remaining portions of the document (Chauhan, ¶ 53, FIGS. 7A and 7B show examples of user interfaces 700, 710. Interface 700 (FIG. 7A) may include a notification advising the user that an issue has been encountered. Interface 710 (FIG. 7B) may advise the user that the issue has been resolved and also may identify the shortest path through which the user may complete the transaction. Interface 710 may include, for example, an option to establish a connection with the recommended channel, make an in-person appointment, and/or locate the nearest ATM (discloses a notification to provide input to complete a transaction on an interface document), as applicable. The interfaces 700, 710 may also include options to provide more information about the content of the notification, as illustrated in FIG. 7A).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the notification and subject input elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 22, this claim recites limitations substantially similar to those in claim 21, and is rejected for the same reasons as stated above.
Regarding Claim 23, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
While suggested in at least Fig. 2 and related text, Maugans does not explicitly disclose …wherein the one or more available services at the physical destination vary among a plurality of physical destinations associated with the entity
However, Chauhan discloses …wherein the one or more available services at the physical destination vary among a plurality of physical destinations associated with the entity (Chauhan, ¶ 47, If the issue processor 550 determines that one or more paths which may lead to resolution of the issue are available from a path database 540, the issue processor 550 may transmit to the autonomous helper agent 560 the identity and classification of the issue, along with any known path(s) for resolution. This transmitted information may include such system parameters as application context, operation context, the current variables/inputs given by the user, the type of issue, and/or the severity of the issue. The autonomous helper agent 560 also may establish a connection with an applicable channel, such as a virtual agent 592, or alternatively transmit a notification to the user computing device with an invitation to establish a connection with a virtual agent 592, make in-person appointment 594, (discloses routing a customer to various channel based on a service required) or pursue other available channels such as online assistance), (Id., ¶ 51, Time-to-reach 620 for a given channel may be influenced by factors such as queue size and slot availability for the channel and, if applicable, the proximity of the customer's current location to the channel. Proximity to the channel may be particularly relevant for channels such as an in-person appointment 680 or an ATM 690. (discloses physical destinations offering varying services) A customer's channel preference 630 and language preference 640 may be determined from user input and/or through machine learning based on the user's previous interactions with the computing platform).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans to include the service variation elements of Chauhan in the analogous art of autonomous helper agents for the same reasons as stated for claim 1.
Regarding Claim 24, this claim recites limitations substantially similar to those in claim 22, and is rejected for the same reasons as stated above.
Claims 6, 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Maugans in view of Chauhan and Venetianer, and in further view of Liebman et al., U.S. Publication No. 2022/0035965 [hereinafter Liebman].
Regarding Claim 6, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 1…
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans, Chauhan and Venetianer does not explicitly disclose …wherein the map application data comprises current location data associated with the subject, and the method further comprises: determining a plurality of travel options for traveling to the physical destination, and notifying the subject of the plurality of travel options.
However, Liebman discloses …wherein the map application data comprises current location data associated with the subject, and the method further comprises: determining a plurality of travel options for traveling to the physical destination, and notifying the subject of the plurality of travel options (Liebman, ¶ 100, the operator type model (204) includes, for each of the one or more operator types (252), operator preference information (254) regarding a plurality of travel types. (discloses travel options) As an illustrative example, the operator preference information (254) indicates a preference for one or more categories (256) corresponding to at least one of cruise (228), sport (230), comfort (232), acceleration (234), speed (236), or economy (“ECO”) (260). Each of the one or more categories (256) can correspond a type of vehicle performance that is preferred, or predicted to be preferred, by a particular operator (132) or operator type based on each particular type of travel of the vehicle (102). As an example, the operator type model (204) may determine that an “aggressive” operator type prefers that the vehicle (102) operate according to the acceleration (234) category (e.g., adjusting throttle response, transmission shift points, etc. for improved power and performance), when the determined travel type (220) corresponds to straight travel (242) and that a “conservative” operator type prefers that the vehicle (102) operates according to the ECO (260) category (e.g., adjusting throttle response, transmission shift points, etc. for improved fuel efficiency) when the travel type (220) corresponds to straight travel (242). As another example, the operator type model (204) may determine that an “aggressive” operator type prefers that the vehicle (102) operates according to the sport (230) category when the determined travel type (220) corresponds to turning (226) and that a “conservative” operator type prefers that the vehicle (102) operate according to the comfort (232) category when the travel type (220) corresponds to turning (226)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans, the notification and options elements of Chauhan and the travel intent prediction elements of Venetianer to include the travel options elements of Liebman in the analogous art of calibration of online-real-world systems using simulations.
The motivation for doing so would have been to improve user experience based on preference data in order “to determine an operating characteristic (134) further based on the preference data (222))” (Liebman, ¶ 99), wherein such improvements would benefit Venetianer’s method which seeks to improve user experience by “employ[ing] Artificial Intelligence (AI) and Machine Learning (ML) techniques that enable performance improvements over existing intent prediction methods. For example, in some embodiments, a neural network or machine learning model may be trained to more accurately predict, based on personal statistical models” (Venetianer, ¶ 26), wherein such improvements would further benefit Chauhan’s method which seeks to improve “the accuracy of a customer feedback system, in terms of relevance to a matter at hand, … by automatically selecting topics that are pertinent to input received from the customer. In another example, the effectiveness of a customer feedback system is improved by automatically identifying appropriate times for soliciting customer feedback... In yet another example, the versatility of a customer feedback system is improved by automatically cataloging features that are not presently available to address a particular customer need, so as to help identify a need for new product features.” (Chauhan, ¶ 16), and wherein such improvements would further benefit Maugans’ method which seeks provide an improved ability “to determine if a user visiting the webpage is ‘in-market’ for products and/or services that the webpage publisher provides. This is advantageous because if the user is ‘in-market’, the webpage publisher may execute an appropriate marketing or sales campaign to increase the likelihood that the user converts to a customer. (See the '193 disclosure.)” [Liebman, ¶ 99; Venetianer, ¶ 26; Chauhan, ¶ 16; Maugans, ¶47].
Regarding Claim 8, the combination of Maugans, Chauhan and Venetianer discloses …The method of claim 6…
While suggested in at least Fig. 2 and related text of Maugans, the combination of Maugans and Chauhan does not explicitly disclose … further comprising: determining, for the plurality of travel options, estimated carbon consumption for travelling to the physical destination; and notifying the subject of the estimated carbon consumption associated with the plurality of travel options.
However, Liebman discloses … further comprising: determining, for the plurality of travel options, estimated carbon consumption for travelling to the physical destination; and notifying the subject of the estimated carbon consumption associated with the plurality of travel options (Liebman, ¶ 77, In some implementations, the engine (104) includes a gasoline engine, a diesel engine, or is adjustable to switch between diesel operation and gasoline operation, as illustrative, non-limiting examples. In some implementations, the engine (104) is configured to operate using carbon dioxide-free fuels (e.g., carbon-neutral fuels, such as synthetic hydrocarbons generated using renewable energy), renewable fuels (e.g., fossil-free fuels, such as biofuels), or one or more other environmentally friendly fuels), (Id., ¶ 86, In some implementations, the valve control model (116) is trained to optimize or balance one or more characteristics of the engine (104), such as power output, torque production, fuel efficiency, emissions, responsiveness, and engine longevity, as illustrative, non-limiting examples. In some implementations, the valve control model (116) is generated and installed by a manufacturer of the vehicle (102) based on experimental or test data generated using one or more test vehicles, the vehicle (102) itself, or a combination thereof. The valve control model (116) may indicate default values that enhance operation of the vehicle (102), as compared to conventional non-adjustable cam-operated valves, by tuning performance of the engine (104) based on the state of the engine (104) and the control inputs (130) responsive to the operator (132) of the vehicle (102). The operator (132) can be within the vehicle (102), such as within a cabin or cockpit of the vehicle (102), or remote from the vehicle (102), such as in implementations in which the one or more operator controls (128) include a remote controller for the vehicle (102) (e.g., for remote control of the vehicle (102) via wireless signaling)), (Id., ¶ 101, the one or more processors (120) are further configured to determine, using the fleet operation model (206), fleet operation data (224) corresponding to fleet control data (218) that is received at the vehicle (102) and to determine the operating characteristic (134) further based on the fleet operation data (224). In some examples, the fleet control data (218) corresponds to an instruction from a governmental or regulatory entity (212). To illustrate, a municipality may issue fleet control data (218) instructing vehicles to operate in a lowered-emission mode in response to air pollution levels exceeding a threshold amount. In other example, the fleet control data (218) corresponds to an instruction from a manufacturer or corporate owner (214) of the vehicle (102). To illustrate, an owner of a fleet of vehicles including the vehicle (102) (e.g., the vehicle (102) may be a commercial aircraft owned by an airline or a delivery truck owned by a business) may issue the fleet control data (218) instructing vehicles in the fleet to operate in an increased fuel-efficiency mode in response to an increase in fuel prices), (Id., ¶ 82, the one or more processors (120) may include, or are coupled to, a vehicle operation data interface (122) (“VOD I/F”) that is configured to receive vehicle operation data (136). For example, the vehicle operation data interface (122) may receive control inputs (130) from the one or more operator controls (128) and sensor data (124) from one or more vehicle operation sensors (118). In an illustrative implementation, the vehicle operation data interface (122) may be an electrical signal bus, an optical signal bus, and/or a wireless interface).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the location and machine learning elements of Maugans and the notification and options elements of Chauhan to include the carbon-neutral travel options elements of Liebman in the analogous art of calibration of online-real-world systems using simulations for the same reasons as stated for claim 6.
Regarding Claim 17, this claims recites limitations substantially similar to those in claim 8, and is rejected for the same reasons as stated above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharp, U.S. Publication No. 2016/0012465, discloses a system and method for distributing, receiving, and using funds or credits and apparatus thereof.
Phillips et al., U.S. Patent No. 10,810,528, discloses identifying and utilizing the availability of enterprise resources.
Hernandez et al., U.S. Publication No. 2021/0073819, discloses systems for detecting application, database, and system anomalies.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624
/PATRICIA H MUNSON/ Supervisory Patent Examiner, Art Unit 3624
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