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 .
Notice to Applicant
The following is a Final Office Action. In response to Examiner’s Non-Final Rejection of 11/28/25, Applicant, on 2/17/26, amended claims. Claims 1-3, 6-10, 13-17, and 20 are pending in the instant application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
The 101 rejections are withdrawn. The claims, when viewed in combination, are not directed to an abstract idea and are viewed as a practical application, similar to Diamond v. Diehr (see MPEP 2106.05e), when viewing the combination of training a machine learning model, executing the machine learning model, executing a communication scheme executing a plug-in identifying particular terminology in the communications, modifying settings associated with volume of audio and pitch of audio provided via a self-service kiosk, and continuously updating the machine learning model.
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 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.
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 1-3, 6-10, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (US 2020/0293923) in view of Kim (US 2017/0293929) and Mittelstaedt (US 2016/0350953).
Concerning claim 1, Zhu discloses:
A computing platform (Zhu – see par 58 - FIG. (FIG. 4 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller)), comprising:
at least one processor (Zhu – see par 58 - The program code may be comprised of instructions 424 executable by one or more processors 402. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines);
a communication interface communicatively coupled to the at least one processor (Zhu – see par 20 - network 110 is communicatively coupled with at least one enterprise (e.g., enterprise 120 and enterprise 130), and activity-based communication management system 140. see par 68, FIG. 1, 5 - ] Activity-based management system 140 adjusts 505 a communication setting. In some embodiments, system 140 adjusts a communication setting based on the category determined 504); and
a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform (Zhu – see par 62 - The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., instructions 424) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein.) to:
receive historical user data from a plurality of data sources (Zhu – see par 19 - data such as activity history may be stored at enterprises 120 and 130 and/or stored at activity-based communication management system 140; See par 26 - Activity-based communication management system 140 may have access to local databases such as behavior database 200 and demographics database 205. In some embodiments, activity-based communication management system 140 includes additional, fewer, or different components for various functions. For example, behavior database 200 and/or demographics database 205 may be stored remotely and accessible through network 110. Although not depicted, behavior database 200 and demographics database 205 may be part of a larger profile database. data such as enterprise and market data may also be used to determine user segmentation.);
train, using the historical user data, a machine learning model to identify patterns or sequences in data to recommend a category of user (Applicant’s [0038] as published states In some examples, the machine learning model may be trained (e.g., using data received from one or more internal data sources, external data sources, from the user, and the like) to identify patterns or sequences in data to generate or output a recommended category of the user and/or communication scheme customizations. For instance, the machine learning model may receive, as inputs, user specific data for a user and, upon execution of the machine learning model, may output a category with which the user should be associated (e.g., based on employment, hobbies, interests, transaction data, or the like; [0089] as published states “In analyzing the data, the machine learning model may detect an anomaly in the pattern or sequence of data that may indicate a triggering event, such as a change in job, new interest of hobby, change in life status, or the like.”).
Zhu discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 26, FIG. 2 – machine learning model selector 220, machine learning model trainer 260; data such as enterprise and market data may also be used to determine user segmentation; see par 48 - Machine learning model trainer 260 may retrain machine learning models used by activity parameter generator 230. To perform the retraining, machine learning model trainer 260 may update a training set using empirical behavioral data and demographic data. For example, a training set is updated to account for the recent activity experience (e.g., behavioral data updated to have empirical value of $55) (disclosing pattern/sequence of data as it considers more recent data as well). In some embodiments, activity-based communication management system 140 receives an indication that an activity has occurred. For example, activity-based communication management system 140 receives transaction data indicating that the user spent $55 instead of the $12.40 predicted using value model 233. This may indicate that the user was incorrectly categorized into a group with low value and instead should be categorized into a group with high value, which may be accomplished by retraining value model 233;
See also Kim – see par 174 - A “persona” is a synthetic personality and rules associated thereto based on specific demographic, preference, and behavioral data, all within the context of time. More specifically, an individual's behavior and preferences, especially related to purchasing habits, are related to a specific time period in the individual's life. For example, an individual who is single and at the beginning stages of his or her career will exhibit a particular purchasing behavior that is different from the behavior of an individual who is established in his or her career and is newly married (same as example in Applicant’s specification [0089] as published of “life status” or “change in job”); See par 175 - a persona in accordance with the narrowcasting system of the present invention is characterized by a defined event, a trigger of the event, duration of the event, and the user's location in the timeline of events. In particular, the personas generated by the narrowcasting engine 250 relate to the “directional” (i.e., future trend of purchases) of the user in his or her purchasing behavior rather than the “data point” (i.e., item of purchase) of the user's purchases; See par 177 - Similarly, “life events” are cells segmented based on demographic, preference, and behavioral data that indicate a particular stage of life that the user is in. For example, a “wedding” life event is defined by rules that look for users who are single or divorced…Another example may be a “baby” life event that is defined by rules … Accordingly, the offers segmented by the narrowcasting engine 250 according to the life events may include travel offers for their honeymoon or offers for family-friendly vehicles, such as minivans. Therefore, the narrowcasted communications according to the present invention proactively communicate offers that are relevant and timely. ) and one or more customization recommendations for one or more systems (Zhu – see par 44 - Communication setting modifier 250 adjusts communication settings based on segmentation determined by activity parameter categorizer 240; see par 45 -In another example, communication setting modifier 250 receives user segmentation indicating that a user is in a group characterized by high recency and determines generate communication settings inviting the user to exclusive events hosted by the enterprise. In some embodiments, communication setting modifier 250 may determine communication settings using a mapping table that maps a user group to at least one communication setting (e.g., communicate daily) or communication setting instruction (e.g., decrease communication frequency by 50%));
receive user specific data, wherein the user specific includes at least social media data received from a social media platform external to the enterprise organization (par 23 – as published - user specific data may include transaction data, payroll or employment data, and the like,
Zhu –see par 23, 30 – user account/profile data from social media account; see par 46 - communication setting modifier 250 receives updates to existing user groups from activity parameter categorizer 240 and modifies communication settings based on the updated groups. For example, communication setting modifier 250 receives an update to the user groups indicating that a user has changed from being characterized by low value to high value. Communication setting modifier 250 may then update communication settings from notifying the user of discounts to notifying the user of more expensive activity opportunities. see par 47 - a dataset including a user's historical frequency of performing an activity, contributions made to perform each activity (e.g., money spent), age, and location of residence is labeled with an expected recency, frequency, and/or value.
see also Mittelstaedt Abstract – social graph; see par 253 – website may be associated with social network system 2402 or on an external server);
execute the machine learning model using the user specific data as inputs to output a recommended category of a first user (Zhu – see par 46 - communication setting modifier 250 receives updates to existing user groups from activity parameter categorizer 240 and modifies communication settings based on the updated groups. For example, communication setting modifier 250 receives an update to the user groups indicating that a user has changed from being characterized by low value to high value; see par 50 - machine learning model trainer 260 uses feedback to emphasize particular information from databases 200 and 205 that is likely to lead to correct or incorrect categorization that produces positive or negative feedback, respectively. For example, system 140 applies weights to particular attributes in the information from databases 200 and 205 (e.g., applying a greater weight to user age than applied to user's historical value contributed) and determines, after receiving positive feedback, that the weights have resulted in proper categorization.);
retrieve, based on the recommended category of the first user, a first communication scheme to execute for the first user, wherein the first communication scheme is associated with the recommended category ([0025] - In some examples, the communication scheme may include predetermined customizations established for users in that category. For instance, for users who are musicians, communication to the user may be in context of “gigs” rather than salary. In some examples, because musicians may receive payments on a less regular schedule than a salaried worker, communications to the user may be times to align with payments received and/or particular services or products may be recommended to the musician (e.g., “have you considered the tax implications of your current pay scheme?”) [0040] - For instance, the communication scheme may include a default set of customizations for users in the identified category (e.g., frequency of communication, channel of communication, type of communication, terminology used within communications, and the like).
Zhu - see par 45 -In another example, communication setting modifier 250 receives user segmentation indicating that a user is in a group characterized by high recency and determines generate communication settings inviting the user to exclusive events hosted by the enterprise. In some embodiments, communication setting modifier 250 may determine communication settings using a mapping table that maps a user group to at least one communication setting (e.g., communicate daily) or communication setting instruction (e.g., decrease communication frequency by 50%); par 46 -Communication setting modifier 250 may then update communication settings from notifying the user of discounts to notifying the user of more expensive activity opportunities (disclosing “type of communication”));
execute the first communication scheme for the first user, wherein executing the first communication scheme for the first user includes executing a … identifying particular terminology to use in communication with the user (Zhu – see par 31, FIG. 2 – data encoder 210 generates feature vectors; different enterprises use different terminology/labels; data encoder determines similarity between label terms; see par 32 - In some embodiments, data encoder 210 attributes weights within training sets or feature vectors input to a machine learning model. Weights may be determined by machine learning model trainer 260 and are discussed in further detail in the description of machine learning model trainer 260. see par 46 - communication setting modifier 250 receives updates to existing user groups from activity parameter categorizer 240 and modifies communication settings based on the updated groups; see FIG. 5, par 68 – “adjust a communication setting” (505); see par 47 - In some embodiments, machine learning model trainer 260 uses manually labeled data from user-enterprise activities and user profiles. For example, a dataset including a user's historical frequency of performing an activity, contributions made to perform each activity (e.g., money spent), age, and location of residence is labeled with an expected recency, frequency, and/or value. Each model may be retrained at a different rate to ensure that each model reflects the latest training set available for it.)
Zhu and Kim do not disclose a “plug-in.”
Mittelstaedt discloses:
execute the first communication scheme for the first user, wherein executing the first communication scheme for the first user includes executing a “plug-in” identifying particular terminology to use in communication with the user (Mittelstaedt see par 75, 152 – plug-ins; user input detector can recognize user input provided in conjunction with the communication application 214 indicating a desire to compose an electronic message with a content enhancement; content enhancement application 210 to select and apply a content enhancement to a digital content item; see par 85 - enhancement selection information can relate to a user's interest in receiving a content enhancement generally or the user's interest in receiving a content enhancement of a particular type, style, format, or feature. Enhancement selection can include demographic information (e.g. age of user); see par 104 - the enhancement manager 232 can select a content enhancement based on demographic information (e.g., age, gender, etc.), purchases, user searches, web-page visits, residence, or other user characteristics. For example, the enhancement manager 232 can provide a content enhancement that includes an advertisement).
Zhu, Kim, and Mittelstaedt disclose:
receive subsequent user specific data for the first user, wherein the subsequent user specific data for the first user includes social media data including further interactions between the first user and the social media platform (Zhu – see par 23, 30 – user account/profile data from social media account ; see par 43 - Activity parameter categorizer 240 may segment users into groups based on the difference of received activity parameters. For example, a user may be categorized into an enterprise champion based on the change of recency and frequency activity parameters over time; see par 46-47 - a dataset including a user's historical frequency of performing an activity, contributions made to perform each activity (e.g., money spent), age, and location of residence is labeled with an expected recency, frequency, and/or value.
see also Mittelstaedt Abstract – social graph; see par 253 – website may be associated with social network system 2402 or on an external server; see par 85 – enhancement selection information can relate to a user’s interest in receiving a content enhancement… which can include features from … characteristics of a user, information from a social graph; a user’s actions on a social network (e.g. “liking” a particular product or event).);
execute the machine learning model using the subsequent user specific data as inputs to generate a first user specific customization to the first communication scheme (Zhu – see par 46 - communication setting modifier 250 receives updates to existing user groups from activity parameter categorizer 240 and modifies communication settings based on the updated groups. For example, communication setting modifier 250 receives an update to the user groups indicating that a user has changed from being characterized by low value to high value. Communication setting modifier 250 may then update communication settings from notifying the user of discounts to notifying the user of more expensive activity opportunities; see par 67-68, FIG. 5 - Activity-based management system 140 determines 504 a category to which the behavioral data and demographic data belong based on at least one activity parameter received as an output from a machine learning model. Activity-based management system 140 adjusts 505 a communication setting. In some embodiments, system 140 adjusts a communication setting based on the category determined 504)
As best understood for how the claims work, it does not appear Zhu discloses the limitations.
Kim discloses the previous limitation:
execute the machine learning model using the subsequent user specific data as inputs to generate a first user specific customization to the first communication scheme (Kim – see FIG. 2 showing narrowcasting 250 including “Communication Management” (CM); see par 191, FIG. 24 - he active learning AL processes the collected data as described above and applies rules and algorithms that will determine what offers to present to a particular user (i.e., determine the right product for the right person at the right time). Based on results from the AL process, the communication management CM will send customer service emails (based on reminder and suggestion preference data, for example) and/or marketing/advertising communication (based on inferred data, for example). The communication of marketing/advertising messages occurs utilizing one or more mediums: Internet (e.g., website portal), newsletter insertion (e.g., HR newsletters) and emails (email newsletters). The response is fed back into the system in real-time to collect and refine the data to be even more accurate and relevant.).
Kim discloses the remaining limitations:
modify the first communication scheme to include the first user specific customization (Kim – see par 111 - the member segmentation module 220 processes the member users' data based on the demographic and preference data using eligibility rules and existing marketing models to create a market segmented member database 320. As explained further below, this segmentation is dynamically adjusted as the users' preference information changes over a period of time. The initial segmentation is made based on rules and models applied to the information provided by the networks. See FIG. 2, par 143 - The active learning AL includes three aspects: (1) targeting management 254, (2) fatigue management 255, and (3) content optimization 256. The targeting management 254 is directed to control and tracking of response rates to various algorithms for inferring a user's interest in a particular merchant); and
execute the modified first communication scheme, wherein executing the modified first communication scheme includes modifying settings ... provided via a self-service kiosk (see Applicant’s See also [0018] as published “In some examples, the communication scheme customized to the particular user may include user specific settings directed to frequency of communication, type of communication channel or preferred communication channel to use for communications between the enterprise organization and user, type of lingo or terminology to present to the user in communications (e.g., modifying chat terminology to use, modifying terminology presented via a user interface in one or more channels or communication;”
Kim discloses the limitations based on broadest reasonable interpretation in light of the specification - see par 143 - Based on the customer type, a user will be marketed to accordingly. Additional factors may be incorporated into the targeting management 254, such as behavioral and demographic data. The fatigue management 255 is directed to monitoring of response rates of individual users and alters the frequency of communication to that user; see par 191 - The communication of marketing/advertising messages occurs utilizing one or more mediums: Internet (e.g., website portal), newsletter insertion (e.g., HR newsletters) and emails (email newsletters). The response is fed back into the system in real-time to collect and refine the data to be even more accurate and relevant; par 194 - As the user answers the intelligent questions on the left side of the screen, the offers displayed on the right side of the screen dynamically changes, as shown in FIG. 29. Further, as the user interacts with some of the offers (e.g., hovers over a particular offer), the information of the user's interest is also gathered to build the user's preference profile. FIG. 28 is an exemplary flow of the preference building process during registration. Once the user's preference data is gathered during the registration process through the preference game, for example, the segmented content and preference based content are collected and dynamically arranged on the user's website as personalized content. (e.g., FIG. 29).
Mittelstaedt discloses:
execute the modified first communication scheme, wherein executing the modified first communication scheme includes modifying settings “associated with volume of audio provided and pitch of audio” provided via a self-service kiosk (Mittelstaedt see par 83 – content manager 228, FIG. 2 - can also modify previously captured digital content items; can adjust audio qualities (e.g. pitch, tone, volume); see par 85 - enhancement selection information can relate to a user's interest in receiving a content enhancement generally or the user's interest in receiving a content enhancement of a particular type, style, format, or feature. Enhancement selection can include demographic information (e.g. age of user); see par 104 - the enhancement manager 232 can select a content enhancement based on demographic information (e.g., age, gender, etc.), purchases, user searches, web-page visits, residence, or other user characteristics. For example, the enhancement manager 232 can provide a content enhancement that includes an advertisement).
Zhu, Kim, and Mittelstaedt disclose:
receive, via a dynamic feedback loop, information related to the recommended category and the first user specific customization (Zhu – see par 47 - Machine learning model trainer 260 trains machine learning models used by activity parameter generator 230 (e.g., recency model 231, frequency model 232, and value model 233). The machine learning models may be initially trained on data representative of previous activities between users and enterprises (e.g., historical recency data). In some embodiments, machine learning model trainer 260 uses manually labeled data from user-enterprise activities and user profiles. For example, a dataset including a user's historical frequency of performing an activity, contributions made to perform each activity (e.g., money spent), age, and location of residence is labeled with an expected recency, frequency, and/or value. Each model may be retrained at a different rate to ensure that each model reflects the latest training set available for it.
See also Kim – see par 141 - One process used to gather preference data is called “intelligent questioning.” Intelligent questioning, described in further detail below, uses algorithms from the active learning AL to infer a user's preferences and gives the user an opportunity to confirm those inferences. For example, the active learning AL may infer that a particular user is likely to be interested in purchasing at a particular merchant. The narrowcasting engine 250 will confirm this inferred preference by dynamically presenting the user with a preference question (e.g., a reminder) about that merchant and detecting the user's reaction; see par 143 - The active learning AL includes three aspects: (1) targeting management 254, (2) fatigue management 255, and (3) content optimization 256.);
continuously update the machine learning model based on the information received via the dynamic feedback loop (Zhu – See par 48 - Machine learning model trainer 260 may retrain machine learning models used by activity parameter generator 230. To perform the retraining, machine learning model trainer 260 may update a training set using empirical behavioral data and demographic data;
see also Kim – see par 137 - In this way, the narrowcasting system 38 dynamically gathers, analyzes, and adjusts the effectiveness of each narrowcasted message. As shown in FIG. 11, the narrowcasting system of the present invention uses the active learning process in a continuous feedback loop to build/launch, gather, analyze, and refine each narrowcasted message such that the next message is more accurate and effective in eliciting responses from the users; see par 139 - To this end, using artificial intelligence, for example, the narrowcasting engine 250 (FIG. 2) includes an active data gathering ADG and active learning AL that performs the active data gathering process and the active learning process to obtain future buying data of each member; see par 191 - The active learning AL processes the collected data as described above and applies rules and algorithms that will determine what offers to present to a particular user (i.e., determine the right product for the right person at the right time). Based on results from the AL process, the communication management CM will send customer service emails (based on reminder and suggestion preference data, for example) and/or marketing/advertising communication; The response is fed back into the system in real-time to collect and refine the data to be even more accurate and relevant.);
detect a triggering event (Kim – see par 174 - A “persona” is a synthetic personality and rules associated thereto based on specific demographic, preference, and behavioral data, all within the context of time. More specifically, an individual's behavior and preferences, especially related to purchasing habits, are related to a specific time period in the individual's life. For example, an individual who is single and at the beginning stages of his or her career will exhibit a particular purchasing behavior that is different from the behavior of an individual who is established in his or her career and is newly married. By detecting particular trigger events (based on preference and/or behavioral data—e.g., purchase of an engagement ring, purchasing a home, purchasing a minivan, etc.) and observing the following purchase preferences and behaviors, a more accurate profile, and eventually future purchasing data, can be obtained), wherein the triggering event includes a change in payroll payments and a change in social media interactions between the first user and the social media platform (Kim – see par 163 - demographic data includes, but is not limited to, home and work locations, gender, income level, job title, and marital status. The data may be obtained from employee data files. see par 178 - In each case as described above, a defined event (e.g., new job, wedding) is detected based on a trigger of the event (e.g., business apparel, engagement ring). Using data gathered from other individuals, a duration of these events can be approximated (e.g., 1-5 years for the workaholic, 6-12 months for the wedding). The individual's position within this time frame can be determined based on the preference and behavior data (e.g., luxury car may indicate the latter stages of the workaholic while purchase of a wedding gown may indicate the wedding date is near);
see also Mittelstaedt see par 85 – enhancement selection information can relate to a user’s interest in receiving a content enhancement… which can include features from … characteristics of a user, information from a social graph; a user’s actions on a social network (e.g. “liking” a particular product or event); see par 89 - accessing enhancement selection information from other applications, the information identifier 230 can also obtain enhancement selection information from the social graph 224. For example, the information identifier 230 can utilize the social graph 224 to obtain information regarding friends, family, associations, purchases, likes, interests, interactions, posts, events, messages, and other data);
responsive to detecting the triggering event, generate at least one recommended alternate category (Zhu – see par 9, 68 - For example, the system determines, based on the received predicted frequency value, that the behavioral and demographic data of the user can be categorized into a high frequency user segment (e.g., users who engage relatively more often with an enterprise). The system may adjust communication settings based on the determined category. For example, the system adjusts the content of the communications transmitted to a user categorized into a high frequency user segment (e.g., includes more notifications of newly available items or services at the enterprise).
see also Kim – see par 178 - By using these established personas, future purchasing data can be determined and appropriate offers (e.g., vacation or honeymoon packages) may be presented to the appropriate individuals at the most relevant times in their life stage.
see also Mittelstaedt – see par 88 - the information identifier 230 can obtain enhancement selection information from … a social media application; the information identifier 230 can collect information indicating that an individual may have an interest in a particular content enhancement related to sports based on … a social media post relating to a sporting event);
generate a notification including a request for user input including one of: confirmation that the recommended category still applies for the first user or selection of an alternate category from the generated at least one recommended alternate category (Kim – see par 111 - segmentation is dynamically adjusted as the users' preference information changes over a period of time; see par 141 – Preference data is preferably gathered continually so as to maintain trust. One process used to gather preference data is called “intelligent questioning”; see par 157 - the narrowcasting engine 250 uses the initial data about the user stored in the user preference data store 240a and the user demographic data store 240d to generate questions to refine/supplement information about each user who enrolls with the narrowcasting system. This “intelligent questioning” process allows the narrowcasting engine 250 to validate, modify, and/or refine the member's data.);
responsive to receiving the user input confirming that the recommended category still applies for the first user (Kim –see par 141 - Untargeted marketing emails diminish trust, in that the user may stop believing that giving preference information will result in a more relevant and customized experience. For this reason, preference data is preferably gathered continually so as to maintain trust. One process used to gather preference data is called “intelligent questioning”; see par 157 - the narrowcasting engine 250 uses the initial data about the user stored in the user preference data store 240a and the user demographic data store 240d to generate questions to refine/supplement information about each user who enrolls with the narrowcasting system. This “intelligent questioning” process allows the narrowcasting engine 250 to validate, modify, and/or refine the member's data. The information collected during the registration/activation process is added to the user preference data store 240a and the user demographic data store 240d, and member segmentation cells and associated rules stored in the rules/personas data store 240c are refined. The narrowcasting system of the present invention uses the registration process to verify and/or supplement the information already stored in the user preference data store 240a and the user demographic data store 240d):
further modify the modified first communication scheme based on the triggering event (Kim - see par 141 - Intelligent questioning, described in further detail below, uses algorithms from the active learning AL to infer a user's preferences and gives the user an opportunity to confirm those inferences. For example, the active learning AL may infer that a particular user is likely to be interested in purchasing at a particular merchant. The narrowcasting engine 250 will confirm this inferred preference by dynamically presenting the user with a preference question (e.g., a reminder) about that merchant and detecting the user's reaction; see par 143- The targeting management 254 is directed to control and tracking of response rates to various algorithms for inferring a user's interest in a particular merchant. Important factors in generating high response may include recency of data and customer type); and
execute the further modified first communication scheme (Kim – see par 144- the investment banker (type A) is extremely busy and has little time available to read marketing messages. Accordingly, the fatigue management 255 is used to limit the quantity of emails to this user to only the most relevant offerings resulting in infrequent, but highly responded to emails. The bank teller (type B), by contrast, will receive more frequent and consistent emails, as they tend to enjoy reading the messages and enjoy a variety of offers; In this manner, the active learning AL “learns” to optimize response rates for individual users. see par 147 - Accordingly, the active data gathering process according to the present invention is performed on a continual basis, constantly updating the users' activities to modify the explicit and inferred data to obtain an accurate profile. As more information about the user is gathered, the information presented to the user, including preference questions and offers, is refined to be more relevant to the user);
responsive to receiving user input including selection of an alternate category, generate and execute a second communication scheme (Applicant’s [0017] as published states “e category may be based on a user's employment area, hobbies, interests, or the like. A communication scheme associated with the category may be retrieved and executed for the user.”
Mittelstaedt discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 80 - The content manager 228 can assist in selecting a digital content item based on a number of possible factors, including but not limited to, user input, content enhancement features, or contextual information (e.g., time, date, or location). For example, in one or more embodiments, the content manager 228 can present a plurality of digital content items and detect user input indicating selection of a particular digital content item; see par 86 - For example, the information identifier 230 can obtain enhancement selection information from characteristics or features of a digital content item, a content enhancement use history, the social graph 224, the Internet, the server storage manager 226 (e.g., the local user profile data 252), an application running on the client device 202, user input, contextual information, metadata, a location device or service, or other source. See par 90 -the information identifier 230 can analyze content enhancements previously received, selected, sent, viewed, ignored, or deleted to obtain enhancement selection information. see par 88 – information identifier can obtain enhancement selection information from web browsing, social media, shopping application… and collect information indicating an interest in a particular content enhancement related to sports… purchase of tickets for a sporting event; See par 96 - enhancement manager 232 can suggest, present, select, create, generate, modify, remove, and/or apply one or more content enhancements with respect to one or more digital content items
see also Kim – see par 150 - For example, based on personas and other segmentations, it is inferred that a certain user, such as a member of the persona or segment, will have interest in a particular merchant. To confirm this interest, the active data gathering module ADG may dynamically present the user with a preference question (e.g., a reminder) or with an offer (e.g., a prominent link on the website portal or email) and monitor if the user responds. In this mariner, the system uses behavioral data to infer a user's future buying preferences and uses the website to confirm that interest, generate more preference data, and refine the algorithm;.).
Zhu, Kim, and Mittelstaedt are analogous art as they are directed to segmenting people and changing communications to the people (see Zhu Abstract; Kim Abstract, par 111, 143; Mittelstaedt Abstract, par 64, 85). 1) Zhu discloses receiving updates and changing a user characterization from “low value” to “high value” (See par 46). Zhu discloses receiving updates to existing user groups (see par 46), account/profile data from social media (See par 23, 30), and looking at recent money spent (see par 47). Kim improves upon Zhu by disclosing dynamically adjusting segmentation as users’ information changes over time (See par 111), as well as tracking response rates for particular merchants and tracking fatigue from communications, refining data regarding mediums to use for marketing/advertising messages and preference based content (See par 143, 191, 194), and looking at demographic/behavioral data over time including different stages of a career for having appropriate offers (e.g. vacation) (See par 174, 178) and income level/job title (See par 163). One of ordinary skill in the art would be motivated to further include updated data, track responses and fatigue, and refining along with segments, and updating income level/job title and verifying user preferences, to efficiently improve upon the adjust communications setting based on user segmentation in Zhu (See Abstract). 2) Zhu discloses having different terminology/labels as part of feature vectors, which are then used to in machine learning model and communication setting (See FIG. 2, par 31-32, 46, 68) and account/profile data from social media (See par 23, 30). Kim discloses marketing to a user based on customer type, behavioral, and demographics and changing content based on preferences (See par 143, 191, 194). Mittelstaedt improves upon Zhu and Kim by disclosing a plug-in for applying a content enhancement to a digital item, such as advertisements based on demographics or other user characteristics (see par 75, 104) and adjusting audio qualities (pitch, volume) as part of content enhancement (See par 83, 85, 104) and further using user’s actions on a social network and/or a social graph to obtain purchases, likes, interests, interactions, posts (See par 85, 89) to then enhance content based on social media posts (See par 88). One of ordinary skill in the art would be motivated to further include a plug-in for applying a content enhancement to a digital item and adjusting audio qualities (pitch, volume), and changing content based on social media information as part of content enhancement to efficiently improve upon the adjusting of communications setting based on user segmentation and consideration of terms and features vectors with social media profiles in Zhu and the marketing and changing content in Kim.
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 adjusting communication settings based on communication settings in Zhu to further include dynamically adjusting segmentation as users’ information changes over time (See par 111, 174, 178) as well as tracking response rates for particular merchants and tracking fatigue from communications, and refining data regarding mediums to use for marketing/advertising messages (See par 143, 150, 163, 191, 193-194) as disclosed in Kim, and plug-ins and changing of volume and pitch as part of the content enhancement and changing content based on social media actions as disclosed in Mittelstaedt, 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 and there is a reasonable expectation of success.
Concerning independent claim 8, Zhu and Kim disclose:
A method comprising:
receiving, by a computing platform, the computing platform having at least one processor and memory, and from a plurality of data sources, historical user data; (Zhu – see par 19 - data such as activity history may be stored at enterprises 120 and 130 and/or stored at activity-based communication management system 140; See par 26 - Activity-based communication management system 140 may have access to local databases such as behavior database 200 and demographics database 205; see par 58 - FIG. (FIG. 4 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller)).
It would be obvious to combine Zhu and Kim for the same reasons as claim 1.
Concerning independent claim 15, Zhu and Kim disclose:
One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to; (see par 58 - FIG. (FIG. 4 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller)).
It would be obvious to combine Zhu and Kim for the same reasons as claim 1.
Concerning claims 2, 9, and 16, Zhu discloses:
The computing platform of claim 1, wherein executing the first communication scheme for the first user includes:
generating an instruction causing a computing system to modify at least one setting associated with communication between the computing system and the first user (Zhu – see par 58 - In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.); and
transmitting the generated instruction to the computing system, wherein transmitting the instruction to the computing system causes the computing system to execute the instruction and modify the at least one setting associated with communication between the computing system and the first user (see par 59 - The machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions 424 (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions 124 to perform any one or more of the methodologies discussed herein; see abstract, par 68 - Activity-based management system 140 adjusts 505 a communication setting).
Concerning claims 3, 10, and 17, Zhu discloses:
The computing platform of claim 1, wherein executing the modified first communication scheme further includes:
generating an instruction causing a computing system to modify at least one setting associated with one of: a preferred channel of communication or a frequency of communication with the first user (Zhu – see par 45 - In some embodiments, communication setting modifier 250 may determine communication settings using a mapping table that maps a user group to at least one communication setting (e.g., communicate daily) or communication setting instruction (e.g., decrease communication frequency by 50%)); see par 58 - The program code may be comprised of instructions 424 executable by one or more processors 402. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.); and
transmitting the generated instruction to the computing system, wherein transmitting the instruction to the computing system causes the computing system to execute the instruction and modify a setting associated with one of: the preferred channel of communication or the frequency of communication with the first user (see par 59 - the term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions 124 to perform any one or more of the methodologies discussed herein).
Concerning claims 6, 13, and 20, Zhu discloses user profile includes job title and other information relevant to demographic information (see par 23).
Kim discloses:
The computing platform of claim 1, wherein the recommended category is based on one of: user employment area, user hobby area, or user interest area (Kim – see par 116 - Member segmentation cells 410 are generally categorized into three types of cells: (1) custom cells 420, (2) advanced cells 430, and (3) basic cells 440. Custom cells 420 refer to member segmentation categories with rules customized in accordance with the directions from the affinity group or network for select members. For example, a credit card company may want to categorize certain segments of its members as “luxury travel” members. As another example, an employer may want to categorize certain segments of its employees as “officers.”)
It would be obvious to combine Zhu and Kim for the same reasons as claim 1. In addition, Zhu discloses user profile includes job title and other information relevant to demographic information (see par 23). Kim improves upon the job title information by also providing different interests in profile (“luxury travel”) or even segmenting by employee categories (“officers”).
Concerning claims 7, and 14, Zhu discloses:
The computing platform of claim 1, further including instructions that, when executed, cause the computing platform to:
Further update the machine learning model based on the received subsequent user specific data for the first user (Zhu – see par 48 - Machine learning model trainer 260 may retrain machine learning models used by activity parameter generator 230. To perform the retraining, machine learning model trainer 260 may update a training set using empirical behavioral data and demographic data. For example, a training set is updated to account for the recent activity experience (e.g., behavioral data updated to have empirical value of $55).
Response to Arguments
Applicant's arguments filed 2/17/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
The 103 arguments are moot over then revised rejections, as necessitated by the amendments.
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.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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