DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 11/17/2025, Applicant, on 2/17/2026, amended claims 1, 8, and 15. Claims 1-20 are pending in this application and have been rejected below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 USC 101 rejections of claims 1-20 regarding abstract ideas are withdrawn in light of Applicant’s amendments and explanations.
New 35 USC 103 rejections of claims 1-20 are applied in light of Applicant’s amendments and explanations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2017/0140285 to Dotan-Cohen et al. (hereafter referred to as Dotan-Cohen) in view of U.S. Patent Application Publication Number 2014/0222570 to Devolites et al. (hereafter referred to as Devolites) and in further view of U.S. Patent Application Publication Number 2021/0158423 to Ngo et al. (hereafter referred to as Ngo).
As per claim 1, Dotan-Cohen teaches:
A system for location-based and event-based machine learning, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: (Paragraph Number [0136] teaches with reference to FIG. 6, computing device 600 includes a bus 610 that directly or indirectly couples the following devices: memory 612, one or more processors 614, one or more presentation components 616, one or more input/output (I/O) ports 618, one or more I/O (I/O) components 620, and an illustrative power supply 622. Bus 610 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Paragraph Number [0137] teaches computing device 600 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device 600 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data).
generate a predicted event level, associated with an entity, based on event information associated with the entity (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
determine that the predicted event level satisfies a threshold (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
receive a set of location indications associated with a set of users (Paragraph Number [0119] teaches examples of activity-related features may include, without limitation, location-related features, such as location of the user device(s) during the user activity, prior to and/or after the user activity, venue-related information associated with the location, or other location-related information).
provide the set of location indications to a machine learning model, in response to determining that the predicted event level satisfies the threshold (Paragraph Number [0069] teaches activity patterns may be determined using pattern inferences logic 230. Pattern inferences logic may include rules, associations, conditions, prediction and/or classification models, or pattern inference algorithms. The pattern inferences logic 230 can take many different forms depending on the particular activity pattern or the mechanism used to identify an activity pattern, or identify feature similarity among observed activity events to determine the pattern. For example, some embodiments of pattern inferences logic 230 may employ machine-learning mechanisms to determine feature similarity, or other statistical measures to determine the activity events belonging to a set of “example user actions” that support the determined activity pattern, as further described below. The user activity information may be received from user activity monitor 280 and information about identified similar features may be received from features similarity identifier 264. In some embodiments, the user pattern(s) determined by activity pattern determiner 266 may be stored as inferred user routines 248 in user profile 240).
in order to receive an indication, from the machine learning model, of a subset of users in the set of users (Paragraph Number [0026] teaches crowdsourced user activity history may also be utilized in conjunction with a user's own activity history. For example, for a given user, a set of other users similar to the given user may be identified, based on having features or characteristics in common with the given user. This might include other users located in proximity to the given user, the given user's social media friends, work colleagues (which may be determined from an analysis of contextual information associated with the given user), other users with similar user activity patterns, or the like. Information about user activity history from the other users may be relied upon for inferring patterns of user activity for the given user. This may be particularly useful in situations where little user activity history exists for the given user, such as where the user is a new user. In some embodiments, user activity information from similar users may be imputed to the new user until enough user history is available for the new user to determine statistically reliable user pattern predictions, which may be determined based on the number of observations included in the user activity history information or the statistical confidence of the determined user activity patterns, as further described herein. In some cases, where the user activity history comes from other users, the resulting inferred activity patterns for the given user may be assigned a lower confidence).
receive feedback information associated with one or more actions performed in response to the at least one communication wherein the one or more actions include whether one or more users, of the subset of users, at least one of viewed the communication, frequented an entity associated with the communication, or provided a rating associated with the communication (Paragraph Number [0091] teaches example content personalization engine 271 is responsible for generating and providing aspects of personalized user experiences, such as personalized content or tailored delivery of content to a user. The content may be provided to the user as a personalized notification (such as described in connection with presentation component 220), may be provided to an application or service of the user (such as a calendar or scheduling application), or may be provided as part of an API where it may be consumed by yet another application or service. In one embodiment, the personalized content includes suggesting that the user perform a relevant activity at the right time before the user performs the activity manually. For example, where an activity pattern indicates the user visits his bank's website near the beginning of the month and enters financial information into an Excel file, the user may be provided with a recommendation asking the user, “Would you like to visit your bank website?” Upon responding affirmatively, a browser instance may be provided that has automatically navigated to the user's bank's website. Further, the particular Excel file may be opened automatically when the user acknowledges the suggestion or when the user navigates to the bank's website. Still further, the user may be provided this content (here, the notification suggestion) at a convenient time, such as when the user is at his home, on an evening, near the beginning of the month, etc. (Examiner asserts that this section teaches at least the first alternative of having viewed the communication)).
that is compared to the threshold to select or refrain from selecting a new subset of users (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
transmit at least one communication to one or more user devices associated with the subset of users (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
Both Dotan-Cohen and Devolites are directed to tracking information of users in regard to characteristics and location. Dotan-Cohen discloses determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location. Devolites improves upon Dotan-Cohen by sending specific communications to users based on calculated data. One of ordinary skill in the art would be motivated to further include sending specific communications to users based on calculated data, to efficiently send communications to users such as targeted advertising based on user characteristics.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location in Dotan-Cohen to further utilize sending specific communications to users based on calculated data as disclosed in Devolites, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach the re-training of the machine learning model based on feedback which is taught by the following citations from Ngo:
re-train the machine learning model based on the feedback information wherein the re-trained machine learning model is used to generate a new predicted event level (Paragraph Number [0094] teaches the machine learning model may have been previously trained on inputs such as historical user purchases, prices, speed a seat has sold out, quantity of social media posts about a performer in a pre-determined time frame, etc., to determine a statistical likelihood that an input (e.g., a request) has likelihood to occur that exceeds a pre-determined threshold based on a previous output. Thus, for each query the user answers, the answer may be applied to the machine learning model to update the recommendations. If the user answers the queries and purchases the ticket, the machine learning model may learn that the query, the answer, and the associated seat are part of the set of examples making up a specific output, and the machine learning model may continue to improve on making accurate and personalized recommendations).
Both the combination of Dotan-Cohen and Devolites and Ngo are directed to tracking information of users in regard to characteristics and location. The combination of Dotan-Cohen and Devolites discloses determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location using machine learning. Ngo improves upon the combination of Dotan-Cohen and Devolites by teaching the re-training of the machine learning model based on feedback. One of ordinary skill in the art would be motivated to further include the re-training of the machine learning model based on feedback, to efficiently build a better machine learning model that is specifically and iteratively trained on relevant feedback data.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location in the combination of Dotan-Cohen and Devolites to further utilize the re-training of the machine learning model based on feedback as disclosed in Ngo, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 1.
In addition, Dotan-Cohen teaches:
wherein the event information is associated with a first time period (Paragraph Number [0067] teaches in embodiments where activity features have a value, similarity may be determined among different activity features having the same value or approximately the same value, based on the particular feature. (For example, a time stamp of a first activity happening at 9:01 AM on Friday and a time stamp of a second activity happening at 9:07 AM on Friday may be determined to have similar or in-common time stamp features.) Paragraph Number [0083] teaches for each pattern-based predictor 267, user activity filtering may be carried out to determine a set of historical user actions that are relevant to that particular pattern-based predictor 267, which may include, for example, periodic-feature based predictors, behavior-feature based predictors (which may include behavior sequences or sequences of previous user actions), unique or uncommon behavior features (such as when a user performs an activity at an unusual time when compared to similar historical user activities) or other types of feature-based predictors).
and the predicted event level is associated with a second time period subsequent to the first time period (Paragraph Number [0067] teaches in embodiments where activity features have a value, similarity may be determined among different activity features having the same value or approximately the same value, based on the particular feature. (For example, a time stamp of a first activity happening at 9:01 AM on Friday and a time stamp of a second activity happening at 9:07 AM on Friday may be determined to have similar or in-common time stamp features.) Paragraph Number [0083] teaches for each pattern-based predictor 267, user activity filtering may be carried out to determine a set of historical user actions that are relevant to that particular pattern-based predictor 267, which may include, for example, periodic-feature based predictors, behavior-feature based predictors (which may include behavior sequences or sequences of previous user actions), unique or uncommon behavior features (such as when a user performs an activity at an unusual time when compared to similar historical user activities) or other types of feature-based predictors).
As per claim 3, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 1.
In addition, Dotan-Cohen teaches:
receive additional event information associated with the set of users (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern).
wherein the additional event information is provided to the machine learning model in order to receive the indication of the subset of users (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern).
As per claim 4, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 1.
In addition, Dotan-Cohen teaches:
provide the event information, associated with the entity, to an additional machine learning model in order to receive the predicted event level from the additional machine learning model (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern).
As per claim 5, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claims 1 and 4.
In addition, Dotan-Cohen teaches:
wherein the additional machine learning model is unique to the entity (Paragraph Number [0051] teaches the user activity-related features may be interpreted to determine a user activity has occurred. For example, in some embodiments, user activity detector 282 employs user activity event logic, which may include rules, conditions, associations, classification models, or other criteria, to identify user activity. For example, in one embodiment, user activity event logic may include comparing user activity criteria with the user data in order to determine that an activity event has occurred. The activity event logic can take many different forms depending on the mechanism used to identify an activity event. For example, the user activity event logic could be training data used to train a neural network that is used to evaluate user data to determine when an activity event has occurred. The activity event logic may comprise fuzzy logic, neural network, finite state machine, support vector machine, logistic regression, clustering, or machine-learning techniques, similar statistical classification processes, or combinations of these to identify activity events from user data. For example, activity event logic may specify types of user device interaction(s) information that are associated with an activity event, such as navigating to a website, composing an email, or launching an app. In some embodiments, a series or sequence of user device interactions may be mapped to an activity event, such that the activity event may be detected upon determining that the user data indicates the series or sequence of user interactions has been carried out by the user).
As per claim 6, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 1.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
transmit, to an administrator device, an indication of the subset of users. (Paragraph Number [0097] teaches flow diagram 900 may receive or determine an alert time relative to an exemplary time period for an exemplary subscriber. For example, depending on the exemplary offer from a marketer or other entity seeking to reach or promote the targeted subscriber users, an alert time may be selected, to achieve a desired advance offset of the subscribers expected arrival and/or departure time, according to an exemplary embodiment. For example, flow diagram 900 may determine an alert time or times for each subscriber, including an exemplary offset from the associated start/stop time, according to an exemplary embodiment. From 908, flow diagram 900 may proceed with 910 or 912, according to various alternative embodiments. Paragraph Number [0129] teaches a marketer device may be used to create an offer to be distributed to subscribers at a desired alert time, in proximity to a particular location, based on predictive data indicating a particular subscriber is expected to be in a location in the future (after the alert time, but potentially offset by a given time period from the desired expected time to arrive at the location), according to an exemplary embodiment. (See also Paragraph Number [0147])).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 7, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 1.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
transmit, to an administrator device, an indication of an amount associated with transmitting the at least one communication (Paragraph Number [0097] teaches flow diagram 900 may receive or determine an alert time relative to an exemplary time period for an exemplary subscriber. For example, depending on the exemplary offer from a marketer or other entity seeking to reach or promote the targeted subscriber users, an alert time may be selected, to achieve a desired advance offset of the subscribers expected arrival and/or departure time, according to an exemplary embodiment. For example, flow diagram 900 may determine an alert time or times for each subscriber, including an exemplary offset from the associated start/stop time, according to an exemplary embodiment. From 908, flow diagram 900 may proceed with 910 or 912, according to various alternative embodiments. Paragraph Number [0129] teaches a marketer device may be used to create an offer to be distributed to subscribers at a desired alert time, in proximity to a particular location, based on predictive data indicating a particular subscriber is expected to be in a location in the future (after the alert time, but potentially offset by a given time period from the desired expected time to arrive at the location), according to an exemplary embodiment. (See also Paragraph Number [0147]) (Paragraph Number [0040] of Applicant's Specification provides for the following clarification on amount: e.g., calculated based on how many users actually read the communication and/or how many users frequented the entity after receiving the communication, among other examples)).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 8, claim 8 recites a method that is substantially similar to the method performed by the system found in claim 1 and is rejected for the same reasons put forth in regard to claim 1
As per claim 9, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
In addition, Dotan-Cohen teaches:
receiving, from an administrator device, an indication of the threshold (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
As per claim 10, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
receiving, from an administrator device, at least a portion of the at least one communication (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 11, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
receiving, from an administrator device, an indication of a condition, wherein the set of users are selected using the condition (Paragraph Number [0097] teaches flow diagram 900 may receive or determine an alert time relative to an exemplary time period for an exemplary subscriber. For example, depending on the exemplary offer from a marketer or other entity seeking to reach or promote the targeted subscriber users, an alert time may be selected, to achieve a desired advance offset of the subscribers expected arrival and/or departure time, according to an exemplary embodiment. For example, flow diagram 900 may determine an alert time or times for each subscriber, including an exemplary offset from the associated start/stop time, according to an exemplary embodiment. From 908, flow diagram 900 may proceed with 910 or 912, according to various alternative embodiments. Paragraph Number [0129] teaches a marketer device may be used to create an offer to be distributed to subscribers at a desired alert time, in proximity to a particular location, based on predictive data indicating a particular subscriber is expected to be in a location in the future (after the alert time, but potentially offset by a given time period from the desired expected time to arrive at the location), according to an exemplary embodiment. (See also Paragraph Number [0147])).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 12, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
In addition, Dotan-Cohen teaches:
generating, by the machine learning system, an additional predicted event level, associated with an additional entity, based on additional event information associated with the additional entity (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
determining, by the machine learning system, that the additional predicted event level fails to satisfy an additional threshold (Paragraph Number [0025] teaches a corresponding confidence weight or confidence score may be determined regarding the user activity patterns. The confidence score may be based on the strength of the pattern, which may be determined by the number of observations used to determine a pattern, how frequently the user activity is consistent with the pattern, the age or freshness of the activity observations, the number of features in common with the activity observations that make up the pattern, or similar measurements. In some instances, the confidence score may be considered when providing a personalized user experience or other improved user experience. Further, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide such experiences or other services. For example, in one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be considered. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold may still be monitored since additional observations of user activities may increase the confidence for a particular pattern).
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
refraining from selecting an additional set of users in response to determining that the additional predicted event level fails to satisfy the additional threshold. (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 13, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
In addition, Dotan-Cohen teaches:
determining, by the machine learning system, that a weighted distance associated with the entity and the set of users is greater than an additional weighted distance associated with an additional entity and the set of users (Paragraph Number [0070] teaches activity pattern determiner 266 provides a pattern of user activity and an associated confidence score regarding the strength of the user pattern, which may reflect the likelihood that future user activity will follow the pattern. More specifically, in some embodiments, a corresponding confidence weight or confidence score may be determined regarding a determined user activity pattern. The confidence score may be based on the strength of the pattern, which may be determined based on the number of observations (of a particular user activity event) used to determine a pattern, how frequently the user's actions are consistent with the activity pattern, the age or freshness of the activity observations, the number of similar features, types of features, and/or degree of similarity of the features in common with the activity observations that make up the pattern, or similar measurements. (examiner is interpreting the distance limitation in light of Applicant's Specification Paragraph Number [0033] where it appears that the distance is mathematical difference not geographic distance)).
wherein the set of users is selected based on the weighted distance being greater than the additional weighted distance (Paragraph Number [0070] teaches activity pattern determiner 266 provides a pattern of user activity and an associated confidence score regarding the strength of the user pattern, which may reflect the likelihood that future user activity will follow the pattern. More specifically, in some embodiments, a corresponding confidence weight or confidence score may be determined regarding a determined user activity pattern. The confidence score may be based on the strength of the pattern, which may be determined based on the number of observations (of a particular user activity event) used to determine a pattern, how frequently the user's actions are consistent with the activity pattern, the age or freshness of the activity observations, the number of similar features, types of features, and/or degree of similarity of the features in common with the activity observations that make up the pattern, or similar measurements. (examiner is interpreting the distance limitation in light of Applicant's Specification Paragraph Number [0033] where it appears that the distance is mathematical difference not geographic distance)).
As per claim 14, the combination of Dotan-Cohen, Devolites, and Ngo teaches each of the limitations of claim 8.
In addition, Dotan-Cohen teaches:
receiving a set of location indications associated with the set of users, wherein the set of users is selected based on the set of location indications (Paragraph Number [0060] teaches examples of activity-related features include, without limitation, location-related features, such as location of the user device(s) during the user activity, venue-related information associated with the location, or other location-related information; Paragraph Number [0126] teaches once an activity pattern is determined, it can be used to determine that a probable future activity event will occur at a future time that is a threshold time from a present time (e.g., at or within three hours, twelve hours, one day, five days, two weeks, etc., from the current time) by analyzing the exercise pattern and/or current or recent information about user activity. The probable future activity event can be associated with a location, time, condition, and/or situation (e.g., the future activity likely occurs following a certain type of event, such as after the user performs another activity), and other contextual data that can be used to facilitate an improved user experience. Further, as described previously, in some embodiments, the user activity pattern, or an inferred user intent or predictions of future activity determined therefrom, may be made available to one or more applications and services that consume this information and provide an improved user experience, such as activity pattern consumers 270, 271, 370, 372, or 373, described in connection to FIGS. 2 and 3).
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2017/0140285 to Dotan-Cohen et al. (hereafter referred to as Dotan-Cohen), in view of U.S. Patent Application Publication Number 2014/0222570 to Devolites et al. (hereafter referred to as Devolites), in further view of U.S. Patent Application Publication Number 2021/0158423 to Ngo et al. (hereafter referred to as Ngo) and in even further view of U.S. Patent Application Publication Number 2013/0295963 to Sen (hereafter referred to as Sen).
As per claim 15, Dotan-Cohen teaches:
A non-transitory computer-readable medium storing a set of instructions for configuring event-based machine learning, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to (Paragraph Number [0136] teaches computing device 600 includes a bus 610 that directly or indirectly couples the following devices: memory 612, one or more processors 614, one or more presentation components 616, one or more input/output (I/O) ports 618, one or more I/O (I/O) components 620, and an illustrative power supply 622. Bus 610 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Paragraph Number [0137] teaches computing device 600 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device 600 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data).
transmit, to a remote system, a registration message (Paragraph Number [0047] teaches user activity monitor 280, or its subcomponents, may determine a device name or identification (device ID) for each device associated with a user. This information about the identified user devices associated with a user may be stored in a user profile associated with the user, such as in user accounts and devices 244 of user profile 240. In an embodiment, the user devices may be polled, interrogated, or otherwise analyzed to determine information about the devices. This information may be used for determining a label or identification of the device (e.g., a device ID) so that user interaction with the device may be recognized from user data by user activity monitor 280. In some embodiments, users may declare or register a device, such as by logging into an account via the device, installing an application on the device, connecting to an online service that interrogates the device, or otherwise providing information about the device to an application or service. In some embodiments, devices that sign into an account associated with the user, such as a Microsoft® account or Net Passport, email account, social network, or the like, are identified and determined to be associated with the user).
that authorizes a remote system to access event information (Paragraph Number [0043] teaches user activity monitor 280 is generally responsible for monitoring user data for information that may be used for determining user activity information, which may include identifying and/or tracking features (sometimes referred to herein as “variables”) or other information regarding specific user actions and related contextual information. Embodiments of user activity monitor 280 may determine, from the monitored user data, user activity associated with a particular user. As described previously, the user activity information determined by user activity monitor 280 may include user activity information from multiple user devices associated with the user and/or from cloud-based services associated with the user (such as email, calendars, social-media, or similar information sources), and which may include contextual information associated with the identified user activity. User activity monitor 280 may determine current or near-real-time user activity information and may also determine historical user activity information, in some embodiments, which may be determined based on gathering observations of user activity over time, accessing user logs of past activity (such as browsing history, for example). Further, in some embodiments, user activity monitor 280 may determine user activity (which may include historical activity) from other similar users (i.e., crowdsourcing), as described previously).
transmit, to the remote system, an indication of a threshold; (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
in response to a predicted event level satisfying the threshold (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
generated by a machine learning model of the remote system based on event information (Paragraph Number [0051] teaches the user activity-related features may be interpreted to determine a user activity has occurred. For example, in some embodiments, user activity detector 282 employs user activity event logic, which may include rules, conditions, associations, classification models, or other criteria, to identify user activity. For example, in one embodiment, user activity event logic may include comparing user activity criteria with the user data in order to determine that an activity event has occurred. The activity event logic can take many different forms depending on the mechanism used to identify an activity event. For example, the user activity event logic could be training data used to train a neural network that is used to evaluate user data to determine when an activity event has occurred. The activity event logic may comprise fuzzy logic, neural network, finite state machine, support vector machine, logistic regression, clustering, or machine-learning techniques, similar statistical classification processes, or combinations of these to identify activity events from user data).
in response to a predicted event level … satisfying the threshold (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
receive, from the remote system, information associated with one or more actions performed in response to the at least one communication, wherein the one or more actions include whether one or more users, of the set of users, at least one of viewed the communication, frequented an entity associated with the communication, or provided a rating associated with the communication (Paragraph Number [0091] teaches example content personalization engine 271 is responsible for generating and providing aspects of personalized user experiences, such as personalized content or tailored delivery of content to a user. The content may be provided to the user as a personalized notification (such as described in connection with presentation component 220), may be provided to an application or service of the user (such as a calendar or scheduling application), or may be provided as part of an API where it may be consumed by yet another application or service. In one embodiment, the personalized content includes suggesting that the user perform a relevant activity at the right time before the user performs the activity manually. For example, where an activity pattern indicates the user visits his bank's website near the beginning of the month and enters financial information into an Excel file, the user may be provided with a recommendation asking the user, “Would you like to visit your bank website?” Upon responding affirmatively, a browser instance may be provided that has automatically navigated to the user's bank's website. Further, the particular Excel file may be opened automatically when the user acknowledges the suggestion or when the user navigates to the bank's website. Still further, the user may be provided this content (here, the notification suggestion) at a convenient time, such as when the user is at his home, on an evening, near the beginning of the month, etc. (Examiner asserts that this section teaches at least the first alternative of having viewed the communication)).
to be compared to the threshold (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
transmit, to the remote system, a data structure encoding at least one communication (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach the re-training of the machine learning model based on feedback which is taught by the following citations from Ngo:
wherein the information is used to re-train the machine learning model to generate a new predicted event level (Paragraph Number [0094] teaches the machine learning model may have been previously trained on inputs such as historical user purchases, prices, speed a seat has sold out, quantity of social media posts about a performer in a pre-determined time frame, etc., to determine a statistical likelihood that an input (e.g., a request) has likelihood to occur that exceeds a pre-determined threshold based on a previous output. Thus, for each query the user answers, the answer may be applied to the machine learning model to update the recommendations. If the user answers the queries and purchases the ticket, the machine learning model may learn that the query, the answer, and the associated seat are part of the set of examples making up a specific output, and the machine learning model may continue to improve on making accurate and personalized recommendations).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach receiving a conformation of a communication being sent to a user which is taught by the following citations from Sen:
receive, from the remote system, a confirmation that the at least one communication was sent to a set of users (Paragraph Number [0055] teaches a trigger generated at 303 to request confirmation of the current activity. Although not shown in FIG. 3, triggers may be generated throughout the method 300 to request confirmation of actions performed or cognitive state. For example, a trigger may be generated to determine the next future activity of the individual, such as whether the individual desires to stop for coffee on the way to work. The response to this trigger may modify the choice set or confirm the choice set or generate other triggers. For example, if the individual indicates they want to stop for coffee, the next choice set may identify coffee shops within range and/or generate attribute-related triggers, such as whether the individual desires better quality or lower price coffee. Also, triggers may be generated to determine or confirm the cognitive state. Paragraph Number [0081] teaches screen shot 910 shown in FIG. 9B shows that a transportation mode may be entered by an individual and screen shot 911 in FIG. 9C shows that a route may be selected by an individual. This information may be predicted by the system 100 and triggers may be generated to request confirmation of the predictions. For example, the system 100 may predict that the user is taking the train to work and the screen may display a confirmation request and the user can confirm "only train" or indicate a different mode).
Both the combination of Dotan-Cohen, Devolites, and Ngo and Sen are directed to tracking information of users in regard to characteristics and location. The combination of Dotan-Cohen, Devolites, and Ngo discloses determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location. Sen improves upon the combination of Dotan-Cohen, Devolites, and Ngo by disclosing receiving a conformation of a communication being sent to a user. One of ordinary skill in the art would be motivated to further include receiving a conformation of a communication being sent to a user, to efficiently determining the actual reach of an advertiser sending communications to users.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location in the combination of Dotan-Cohen, Devolites, and Ngo to further utilize receiving a conformation of a communication being sent to a user as disclosed in Sen, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 16, the combination of Dotan-Cohen, Devolites, Ngo, and Sen teaches each of the limitations of claim 15.
In addition, Dotan-Cohen teaches:
wherein the registration message includes a set of credentials associated with the event information (Paragraph Number [0047] teaches some embodiments of user activity monitor 280, or its subcomponents, may determine a device name or identification (device ID) for each device associated with a user. This information about the identified user devices associated with a user may be stored in a user profile associated with the user, such as in user accounts and devices 244 of user profile 240. In an embodiment, the user devices may be polled, interrogated, or otherwise analyzed to determine information about the devices. This information may be used for determining a label or identification of the device (e.g., a device ID) so that user interaction with the device may be recognized from user data by user activity monitor 280. In some embodiments, users may declare or register a device, such as by logging into an account via the device, installing an application on the device, connecting to an online service that interrogates the device, or otherwise providing information about the device to an application or service. In some embodiments, devices that sign into an account associated with the user, such as a Microsoft® account or Net Passport, email account, social network, or the like, are identified and determined to be associated with the user).
As per claim 17, the combination of Dotan-Cohen, Devolites, Ngo, and Sen teaches each of the limitations of claim 15.
In addition, Dotan-Cohen teaches:
wherein the indication of the threshold includes a selection of a value for the threshold from a plurality of candidate values. (Paragraph Number [0071] teaches the confidence score may be considered when providing a determined activity pattern to an activity pattern consumer 270. For example, in some embodiments, a minimum confidence score may be needed before using the activity pattern to provide an improved user experience or other service by an activity pattern consumer 270. In one embodiment, a threshold of 0.6 (or just over fifty percent) is utilized such that only activity patterns having a 0.6 (or greater) likelihood of predicting user activity may be provided. Nevertheless, where confidence scores and thresholds are used, determined patterns of user activity with confidence scores less than the threshold still may be monitored and updated based on additional activity observations, since the additional observations may increase the confidence for a particular pattern. Paragraph Number [0078] teaches having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), activity pattern determiner 266 may identify that a plurality of user activities corresponds to a user activity pattern for the user. As a further example, activity pattern determiner 266 may determine that a user activity pattern is likely to be followed by a user where one or more of the confidence scores for one or more tracked variables satisfy a threshold value).
As per claim 18, the combination of Dotan-Cohen, Devolites, Ngo, and Sen teaches each of the limitations of claim 15.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
wherein the confirmation indicates a quantity of users in the set of users. (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 19, the combination of Dotan-Cohen, Devolites, Ngo, and Sen teaches each of the limitations of claim 15.
Dotan-Cohen teaches determining user information and characteristics based on specific events that are taking place proximate to the user in either time or location but does not explicitly teach sending specific communications to users based on calculated data which is taught by the following citations from Devolites:
receive, from the remote system, an indication of an amount associated with transmission of the at least one communication. (Paragraph Number [0074] teaches in 116, flow diagram 100 may include optionally using the predictive data and/or other data to support applications such as, e.g., but not limited to, a marketing campaign, a political campaign, a traffic management application, etc., according to an exemplary embodiment. In an exemplary embodiment, an exemplary application may include producing a marketing campaign, according to an exemplary embodiment. According to an exemplary embodiment, a campaign may include, e.g., but not limited to, a communication, an offer, an alert, a notification, an advertisement, a promotion, a marketing material, and/or a coupon, etc. According to an exemplary embodiment, the offering may be pushed to, e.g., but not limited to, a list of subscribers such as, e.g., but not limited to, a list of subscribers meeting a targeted audience, as well as expected to be near or within a threshold distance from a targeted location, at a future time based on the predictive data, which is based on analysis of historical event data, and/or other data, etc. Advantageously, a network service provider (NSP) and/or a communications service provider (CSP) may use its own geolocated event data, and may contact its customers by pushing an advertisement, etc. to the customer without revealing the service provider's customers' information, according to an exemplary embodiment. From 116, flow diagram 100 may proceed with 118).
One of ordinary skill in the art would be motivated to combine these references as described in regard to claim 1.
As per claim 20, the combination of Dotan-Cohen, Devolites, Ngo, and Sen teaches each of the limitations of claim 15.
In addition, Dotan-Cohen teaches:
transmit, to the remote system, an indication of a geographic area (Paragraph Number [0060] teaches examples of activity-related features include, without limitation, location-related features, such as location of the user device(s) during the user activity, venue-related information associated with the location, or other location-related information; Paragraph Number [0126] teaches once an activity pattern is determined, it can be used to determine that a probable future activity event will occur at a future time that is a threshold time from a present time (e.g., at or within three hours, twelve hours, one day, five days, two weeks, etc., from the current time) by analyzing the exercise pattern and/or current or recent information about user activity. The probable future activity event can be associated with a location, time, condition, and/or situation (e.g., the future activity likely occurs following a certain type of event, such as after the user performs another activity), and other contextual data that can be used to facilitate an improved user experience. Further, as described previously, in some embodiments, the user activity pattern, or an inferred user intent or predictions of future activity determined therefrom, may be made available to one or more applications and services that consume this information and provide an improved user experience, such as activity pattern consumers 270, 271, 370, 372, or 373, described in connection to FIGS. 2 and 3).
wherein the confirmation is received based on the set of users being associated with the geographic area (Paragraph Number [0060] teaches examples of activity-related features include, without limitation, location-related features, such as location of the user device(s) during the user activity, venue-related information associated with the location, or other location-related information; Paragraph Number [0126] teaches once an activity pattern is determined, it can be used to determine that a probable future activity event will occur at a future time that is a threshold time from a present time (e.g., at or within three hours, twelve hours, one day, five days, two weeks, etc., from the current time) by analyzing the exercise pattern and/or current or recent information about user activity. The probable future activity event can be associated with a location, time, condition, and/or situation (e.g., the future activity likely occurs following a certain type of event, such as after the user performs another activity), and other contextual data that can be used to facilitate an improved user experience. Further, as described previously, in some embodiments, the user activity pattern, or an inferred user intent or predictions of future activity determined therefrom, may be made available to one or more applications and services that consume this information and provide an improved user experience, such as activity pattern consumers 270, 271, 370, 372, or 373, described in connection to FIGS. 2 and 3).
Response to Arguments
Applicant’s arguments filed 2/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the previously cited reference does not teach the newly amended portions including the new limitations recited by the independent claims. (See Applicant’s Remarks, 2/17/2026, pgs. 15-17). Examiner respectfully disagrees. Examiner notes that new citations from the previously cited Dotan-Cohen reference and the newly cited Ngo reference have been applied to the newly presented claim limitations as indicated in the above in the new 103 rejections. As such, Applicant’s arguments directed towards the previous rejection are moot. In response to Applicant’s arguments, Examiner directs Applicant to review the new citations and explanations provided in the new 103 rejections presented above.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H DIVELBISS whose telephone number is (571)270-0166. The examiner can normally be reached on 7:30 am - 6:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M. H. D./
Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624