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 .
DETAILED ACTION
1. This office action is responsive to the restriction election filed on 12/30/2025. Claims 2-9 and 22-33 are elected without traverse. Claims 10-21 are canceled. Claims 22-33 are new. Claims 2-9 and 22-33 are pending examination.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 04/04/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
3. Claims 7, and 27 are objected to because of the following informalities: Claims 7, and 27 recite “on an likelihood”. Examiner requests the phrase to be amended to “on a likelihood”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-9 and 22-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 22 is/are drawn to method (i.e., a process), claim(s) 2 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 30 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 2, 22, and 30 is/are drawn to one of the statutory categories of invention.
Claims 2-9 and 22-33 are directed to providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. Specifically, claim(s) 2, 22, and 30 recite(s) determine an offer to extend to a user based on an interest of the user; first learning model trained for predicting offers, provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; detect that the user has processed a transaction associated with the offer using the account of the user; provide an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction wherein the indication is shared with the one or more other users via the data feed; determine a user activity associated with at least one of the indication or the offer; determine a first benefit for the user based on the user activity and a second model trained for predicting benefits; and provide the first benefit to the account of the user, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
determine an offer to extend to a user based on an interest of the user; first learning model trained for predicting offers, provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users;; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
detect that the user has processed a transaction associated with the offer using the account of the user; provide an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction wherein the indication is shared with the one or more other users via the data feed; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
determine a user activity associated with at least one of the indication or the offer; determine a first benefit for the user based on the user activity and a second model trained for predicting benefits; and provide the first benefit to the account of the user; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium perform(s) the steps or functions of determine an offer to extend to a user based on an interest of the user; first learning model trained for predicting offers, provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; detect that the user has processed a transaction associated with the offer using the account of the user; provide an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction wherein the indication is shared with the one or more other users via the data feed; determine a user activity associated with at least one of the indication or the offer; determine a first benefit for the user based on the user activity and a second model trained for predicting benefits; and provide the first benefit to the account of the user. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. As discussed above, taking the claim elements separately, the system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium perform(s) the steps or functions of determine an offer to extend to a user based on an interest of the user; first learning model trained for predicting offers, provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; detect that the user has processed a transaction associated with the offer using the account of the user; provide an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction wherein the indication is shared with the one or more other users via the data feed; determine a user activity associated with at least one of the indication or the offer; determine a first benefit for the user based on the user activity and a second model trained for predicting benefits; and provide the first benefit to the account of the user. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 3-9 and 23-29, and 31-33 further describe the abstract idea of providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. Claim(s) 3-9 and 23-29, and 31-33 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. As discussed above, taking the claim elements separately, the system, non-transitory memory, hardware processors, system, machine, non-transitory machine-readable medium perform(s) the steps or functions of generate a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer; and wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction, track user data for the user over a period of time from at least one of visited webpages by the user or software applications used by the user, wherein the offer is further determined based on the user data, wherein the user data comprises at least one of a past purchase, a web browsing history, a digital shopping list, one or more account subscriptions, a purchase benefit preference, or a second benefit previously provided to the user, wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions, receive a request for an incentive offer from the user based on the first benefit provided to the user; and generate the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account, wherein the first and second ML models comprises one of a multi-layer ML model, a decision tree model, or a clustering model, and wherein at least one of the multi-layer model comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes . These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of providing offer and transaction history in a data feed and determining and providing benefit to a first user based on a second user activity associated with the offer. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
A. Claim(s) 2, 3, 5, 6, 22, 23, 25, 26, 30, 31, and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bryant et al., (U.S. Patent Application Publication No. 20170330229) in view of Peterson et al., (U.S. Patent Application Publication No. 20130024260) in view of Gregovic, (U.S. Patent Application Publication No. 20210192496).
As to Claim 2, Bryant teaches a system comprising:a non-transitory memory; and (0058: memory),one or more hardware processors (0058: processor) coupled to the non-transitory memory and configured to execute instructions to cause the system to: (0058: As illustrated in more detail in FIG. 2, the wireless ordering device 4 generally includes a processor 20 and memory 22 with processor control instructions and/or other microcontroller or other application specific electronic control circuitry. The ordering device also includes one or more wireless transceiver circuit(s) 24. Such circuit(s) may serve as the communications mechanism for purposes of ordering information or products and services from one or more vendors as well as the reader mechanism for determining vendor, product or service information from a mass media publication 4. Such transceiver circuit(s) 24 may be incorporated into the wireless ordering device 4 or attachable or insertable as expansion modules, cards or components of such a device, for example, by coupling with or into a wireless phone, PDA or laptop. Preferably, such transceiver circuit(s) 24 is/are incorporated to permit the wireless ordering device 4 to serve with the communications mechanism and reader mechanism as a compact unit for hand-held operation.),detect that the user has processed a transaction associated with the offer using the account of the user; (0079: user subsequently purchases the item… from the shared message).provide an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction (0067: Sharing messages concerning an advertisement, past or prospective purchase is one way to accumulate rewards, 0079: another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet),determine a user activity associated with at least one of the indication or the offer; (0079: user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet). (Examiners note: activity can be the purchase or the re-post of the message that includes the ),determine a first benefit for the user based on the user activity (0079: calculating a reward the system may analyze the reactions of other users, This may be based on the audience reached by the message, rewards if another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet) that includes the advertisement and the past purchase), (Examiner note: benefit can be the rewards given for the first user posting the item on the message board and a second user purchases the item), and provide the first benefit to the account of the user; (0078: Once a consumer transaction (e.g., purchase) or an act of sharing that qualifies for rewards occurs, the system may calculate the amount of a reward based on a set of programmed rules. 0080: Once a reward has been calculated, the system may then credit the user. Crediting a user reward may include attributing the reward to a user and/or also transferring a reward value to the user account).
Bryant does not teach determine an offer to extend to a user based on an interest of the user, provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users;
wherein the indication is shared with the one or more other users via the data feed.
However Peterson teaches determine an offer to extend to a user based on an interest of the user (Fig. 1 (determining qualification for participation in incentive program, and 0029: the offer may be made in connection with certain preliminary or qualifying conditions being met. For example, the offer may be made to individual that have pre-qualified in some way, such as by participating in a rewards club or other loyalty program of the provider), (Examiner note: interest can be user interest in signing up in the rewards program), provide the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall). Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).),wherein the indication is shared with the one or more other users via the data feed (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include wherein the indication is shared with the one or more other users via the data feed of Peterson. Motivation to do so comes from the knowledge well known in the art that wherein the indication is shared with the one or more other users via the data feed would help increase the likelihood that the other users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable.
Bryant does not teach a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits.
However Gregovic teaches a first machine learning (ML) model trained for predicting offers; (0019: first sub-model of the first machine learning model classified the transaction as capable of providing a reward),a second ML model trained for predicting benefits; and (0019: the second sub-model of the machine learning model may be trained to predict reward/ benefits/bonus).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits of Gregovic. Motivation to do so comes from the knowledge well known in the art that a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits would help provide a more accurate offers that the users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable and more accurate.
As to Claim 3, Bryant, Peterson, and Gregovic teach the system of claim 2.
Peterson further teaches wherein executing the instructions further causes the system to: generate a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer; and (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall), (Examiner note: generating a post is generated once the user or purchaser post information about the purchase on social media), Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).), provide the post in the data feed; (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generate a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer. Motivation to do so comes from the knowledge well known in the art that generate a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer would help provide a more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 5, Bryant, Peterson, and Gregovic teach the system of claim 2.
Bryant further teaches wherein executing the instructions further causes the system to:track user data for the user over a period of time from at least one of visited webpages by the user or software applications used by the user, wherein the offer is further determined based on the user data; (0043: Users may be rewarded by the amount of eyeballs that a post attracts (tracked by the system based on how many times the username appears through online impressions) and/or by a fixed reward scheme based on upfront rewards simply by posting).
As to Claim 6, Bryant, Peterson, and Gregovic teach the system of claim 5.
Bryant further teaches wherein the user data comprises at least one of a past purchase, a web browsing history, a digital shopping list, one or more account subscriptions, a purchase benefit preference, or a second benefit previously provided to the user; (0043: tracked by the system based on how many times the username appears through online impressions), (Examiner note: online impression can be browsing history or purchases).
As to Claim 22, Bryant teaches a method comprising: (0058: As illustrated in more detail in FIG. 2, the wireless ordering device 4 generally includes a processor 20 and memory 22 with processor control instructions and/or other microcontroller or other application specific electronic control circuitry. The ordering device also includes one or more wireless transceiver circuit(s) 24. Such circuit(s) may serve as the communications mechanism for purposes of ordering information or products and services from one or more vendors as well as the reader mechanism for determining vendor, product or service information from a mass media publication 4. Such transceiver circuit(s) 24 may be incorporated into the wireless ordering device 4 or attachable or insertable as expansion modules, cards or components of such a device, for example, by coupling with or into a wireless phone, PDA or laptop. Preferably, such transceiver circuit(s) 24 is/are incorporated to permit the wireless ordering device 4 to serve with the communications mechanism and reader mechanism as a compact unit for hand-held operation.),detecting that the user has processed a transaction associated with the offer using the account of the user; (0079: user subsequently purchases the item… from the shared message),providing an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction (0067: Sharing messages concerning an advertisement, past or prospective purchase is one way to accumulate rewards, 0079: another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet), determining a user activity associated with at least one of the indication or the offer; (0079: user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet). (Examiners note: activity can be the purchase or the re-post of the message that includes the ),determining a first benefit for the user based on the user activity (0079: calculating a reward the system may analyze the reactions of other users, This may be based on the audience reached by the message, rewards if another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet) that includes the advertisement and the past purchase), (Examiner note: benefit can be the rewards given for the first user posting the item on the message board and a second user purchases the item), and providing the first benefit to the account of the user; (0078: Once a consumer transaction (e.g., purchase) or an act of sharing that qualifies for rewards occurs, the system may calculate the amount of a reward based on a set of programmed rules. 0080: Once a reward has been calculated, the system may then credit the user. Crediting a user reward may include attributing the reward to a user and/or also transferring a reward value to the user account).
Bryant does not teach determining an offer to extend to a user based on an interest of the user, providing the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users, wherein the indication is shared with the one or more other users via the data feed.
However Peterson teaches determining an offer to extend to a user based on an interest of the user (Fig. 1 (determining qualification for participation in incentive program, and 0029: the offer may be made in connection with certain preliminary or qualifying conditions being met. For example, the offer may be made to individual that have pre-qualified in some way, such as by participating in a rewards club or other loyalty program of the provider), (Examiner note: interest can be user interest in signing up in the rewards program),providing the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall). Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).),wherein the indication is shared with the one or more other users via the data feed; (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include wherein the indication is shared with the one or more other users via the data feed of Peterson. Motivation to do so comes from the knowledge well known in the art that wherein the indication is shared with the one or more other users via the data feed would help increase the likelihood that the other users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable.
Bryant does not teach a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits.
However Gregovic teaches a first machine learning (ML) model trained for predicting offers; (0019: first sub-model of the first machine learning model classified the transaction as capable of providing a reward),a second ML model trained for predicting benefits; and (0019: the second sub-model of the machine learning model may be trained to predict reward/ benefits/bonus).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits of Gregovic. Motivation to do so comes from the knowledge well known in the art that a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits would help provide a more accurate offers that the users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable and more accurate.
As to Claim 23, Bryant, Peterson, and Gregovic teach the method of claim 22.
Peterson further teaches generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer; and (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall), (Examiner note: generating a post is generated once the user or purchaser post information about the purchase on social media), Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).), providing the post in the data feed; (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer. Motivation to do so comes from the knowledge well known in the art that generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer would help provide a more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 25, Bryant, Peterson, and Gregovic teach the method of claim 22.
Bryant further teaches tracking user data for the user over a period of time from at least one of visited webpages by the user or software applications used by the user, wherein the offer is further determined based on the user data; (0043: Users may be rewarded by the amount of eyeballs that a post attracts (tracked by the system based on how many times the username appears through online impressions) and/or by a fixed reward scheme based on upfront rewards simply by posting).
As to Claim 26, Bryant, Peterson, and Gregovic teach the method of claim 25.
Bryant further teaches wherein the user data comprises at least one of a past purchase, a web browsing history, a digital shopping list, one or more account subscriptions, a purchase benefit preference, or a second benefit previously provided to the user; (0043: tracked by the system based on how many times the username appears through online impressions), (Examiner note: online impression can be browsing history or purchases).
As to Claim 30, Bryant teaches a non-transitory machine-readable medium having stored thereon machine- readable instructions executable to cause a machine to perform operations comprising: (0058: As illustrated in more detail in FIG. 2, the wireless ordering device 4 generally includes a processor 20 and memory 22 with processor control instructions and/or other microcontroller or other application specific electronic control circuitry. The ordering device also includes one or more wireless transceiver circuit(s) 24. Such circuit(s) may serve as the communications mechanism for purposes of ordering information or products and services from one or more vendors as well as the reader mechanism for determining vendor, product or service information from a mass media publication 4. Such transceiver circuit(s) 24 may be incorporated into the wireless ordering device 4 or attachable or insertable as expansion modules, cards or components of such a device, for example, by coupling with or into a wireless phone, PDA or laptop. Preferably, such transceiver circuit(s) 24 is/are incorporated to permit the wireless ordering device 4 to serve with the communications mechanism and reader mechanism as a compact unit for hand-held operation.),detecting that the user has processed a transaction associated with the offer using the account of the user; (0079: user subsequently purchases the item… from the shared message),providing an indication that the user has processed the transaction in the data feed in an association with the offer and the transaction (0067: Sharing messages concerning an advertisement, past or prospective purchase is one way to accumulate rewards, 0079: another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet), determining a user activity associated with at least one of the indication or the offer; (0079: user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet). (Examiners note: activity can be the purchase or the re-post of the message that includes the ), determining a first benefit for the user based on the user activity (0079: calculating a reward the system may analyze the reactions of other users, This may be based on the audience reached by the message, rewards if another user subsequently purchases the item or re-shares the message (e.g., a re-post or re-tweet) that includes the advertisement and the past purchase), (Examiner note: benefit can be the rewards given for the first user posting the item on the message board and a second user purchases the item), andproviding the first benefit to the account of the user; (0078: Once a consumer transaction (e.g., purchase) or an act of sharing that qualifies for rewards occurs, the system may calculate the amount of a reward based on a set of programmed rules. 0080: Once a reward has been calculated, the system may then credit the user. Crediting a user reward may include attributing the reward to a user and/or also transferring a reward value to the user account).
Bryant does not teach determining an offer to extend to a user based on an interest of the user;
providing the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users;
wherein the indication is shared with the one or more other users via the data feed.
However Peterson teaches determining an offer to extend to a user based on an interest of the user (Fig. 1 (determining qualification for participation in incentive program, and 0029: the offer may be made in connection with certain preliminary or qualifying conditions being met. For example, the offer may be made to individual that have pre-qualified in some way, such as by participating in a rewards club or other loyalty program of the provider), (Examiner note: interest can be user interest in signing up in the rewards program), providing the offer in a data feed for an account of the user, wherein the data feed enables the offer and a transaction history of the account to be shared with one or more other users; (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall). Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).),wherein the indication is shared with the one or more other users via the data feed (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include wherein the indication is shared with the one or more other users via the data feed of Peterson. Motivation to do so comes from the knowledge well known in the art that wherein the indication is shared with the one or more other users via the data feed would help increase the likelihood that the other users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable.
Bryant does not teach a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits.
However Gregovic teaches a first machine learning (ML) model trained for predicting offers; (0019: first sub-model of the first machine learning model classified the transaction as capable of providing a reward),a second ML model trained for predicting benefits; and (0019: the second sub-model of the machine learning model may be trained to predict reward/ benefits/bonus).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bryant to include a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits of Gregovic. Motivation to do so comes from the knowledge well known in the art that a first machine learning (ML) model trained for predicting offers; a second ML model trained for predicting benefits would help provide a more accurate offers that the users would click and convert or review and engage with such indication and that would promote an increase in the sales and would therefore make the method/system more profitable and more accurate.
As to Claim 31, Bryant, Peterson, and Gregovic teach the non-transitory machine-readable medium of claim 30.
Peterson further teaches wherein the operations further comprise: generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer; and (0046: enabling the purchaser to post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall), (Examiner note: generating a post is generated once the user or purchaser post information about the purchase on social media), Another example is shown in FIG. 9, where a dialogue box 280 is shown in association with enabling the purchaser being able to post information about their purchase and the associated incentive through a different social media website (i.e., Twitter).), providing the post in the data feed; (0046: post information about their purchase and the incentive on a social media web page (e.g., a Facebook Wall).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer. Motivation to do so comes from the knowledge well known in the art that generating a post for the data feed based on at least one of the offer or the first benefit, wherein the post comprises a second benefit available to the user and additional users for at least one of an item or a merchant associated with the offer would help provide a more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 33, Bryant, Peterson, and Gregovic teach the non-transitory machine-readable medium of claim 30.
Bryant further teaches tracking user data for the user over a period of time from at least one of visited webpages by the user or software applications used by the user, wherein the offer is further determined based on the user data; (0043: Users may be rewarded by the amount of eyeballs that a post attracts (tracked by the system based on how many times the username appears through online impressions) and/or by a fixed reward scheme based on upfront rewards simply by posting).
B. Claim(s) 4, 24, and 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bryant et al., (U.S. Patent Application Publication No. 20170330229) in view of Peterson et al., (U.S. Patent Application Publication No. 20130024260) in view of Gregovic, (U.S. Patent Application Publication No. 20210192496) in view of Sharp et al., (U.S. Patent Application Publication No. 20160012465).
As to Claim 4, Bryant, Peterson, and Gregovic teach the system of claim 2.
Bryant, Peterson, and Gregovic do not teach wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction.
However Sharp teaches wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction; (1084: redemption information 64 or portions of redemption information (e.g., a redemption code and/or PIN) may be entered into existing “promo code” or “coupon code” fields; wherein in some embodiments, the total balance for the checkout page may be reduced by the appropriate amount (including, but not limited to 100% discounts, or “free”). Entered redemption codes may comprise a representation of any one or more of: an amount of vendor store credit, an amount of system credit which is accepted by the vendor as valid payment, and/or a prepaid voucher for an item, product, good, or service being displayed on the checkout page. When a redemption code is provided, and applied, the checkout page may subsequently remove the item cost from the total cart value and 1086-1092).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction. Motivation to do so comes from the knowledge well known in the art that wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction would help provide a code with more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 24, Bryant, Peterson, and Gregovic teach the method of claim 22.
Bryant, Peterson, and Gregovic do not teach wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction.
However Sharp teaches wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction; (1084: redemption information 64 or portions of redemption information (e.g., a redemption code and/or PIN) may be entered into existing “promo code” or “coupon code” fields; wherein in some embodiments, the total balance for the checkout page may be reduced by the appropriate amount (including, but not limited to 100% discounts, or “free”). Entered redemption codes may comprise a representation of any one or more of: an amount of vendor store credit, an amount of system credit which is accepted by the vendor as valid payment, and/or a prepaid voucher for an item, product, good, or service being displayed on the checkout page. When a redemption code is provided, and applied, the checkout page may subsequently remove the item cost from the total cart value and 1086-1092).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction. Motivation to do so comes from the knowledge well known in the art that wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction would help provide a code with more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 32, Bryant, Peterson, and Gregovic teach the non-transitory machine-readable medium of claim 30.
Bryant, Peterson, and Gregovic do not teach wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction.
However Sharp teaches wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction; (1084: redemption information 64 or portions of redemption information (e.g., a redemption code and/or PIN) may be entered into existing “promo code” or “coupon code” fields; wherein in some embodiments, the total balance for the checkout page may be reduced by the appropriate amount (including, but not limited to 100% discounts, or “free”). Entered redemption codes may comprise a representation of any one or more of: an amount of vendor store credit, an amount of system credit which is accepted by the vendor as valid payment, and/or a prepaid voucher for an item, product, good, or service being displayed on the checkout page. When a redemption code is provided, and applied, the checkout page may subsequently remove the item cost from the total cart value and 1086-1092).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction. Motivation to do so comes from the knowledge well known in the art that wherein the offer in the data feed includes a code usable when processing transactions associated with the offer for a discount, and wherein the user activity comprises using the code for at least one additional transaction would help provide a code with more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
C. Claim(s) 7, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bryant et al., (U.S. Patent Application Publication No. 20170330229) in view of Peterson et al., (U.S. Patent Application Publication No. 20130024260) in view of Gregovic, (U.S. Patent Application Publication No. 20210192496) in view of Henchy, (U.S. Patent No. 11024190).
As to Claim 7, Bryant, Peterson, and Gregovic teach the system of claim 2.
Bryant, Peterson, and Gregovic do not teach wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions.
However Henchy teaches wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions; (18: provide different online course formats, which can offer different benefits to the user. For example, live online courses encourage user engagement and interaction, as the users may ask questions and have discussions with the instructor or with other users).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions. Motivation to do so comes from the knowledge well known in the art that wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions would help provide more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 27, Bryant, Peterson, and Gregovic teach the method of claim 22.
Bryant, Peterson, and Gregovic do not teach wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions.
However Henchy teaches wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions; (18: provide different online course formats, which can offer different benefits to the user. For example, live online courses encourage user engagement and interaction, as the users may ask questions and have discussions with the instructor or with other users).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions. Motivation to do so comes from the knowledge well known in the art that wherein the user activity is one of a plurality of different interactions that can be performed with the offer by the one or more other users, and wherein each of the plurality of different interactions provides a separate benefit to the user based on an likelihood that a purchase is made from a corresponding one of the plurality of different interactions would help provide more benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
D. Claim(s) 8, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bryant et al., (U.S. Patent Application Publication No. 20170330229) in view of Peterson et al., (U.S. Patent Application Publication No. 20130024260) in view of Gregovic, (U.S. Patent Application Publication No. 20210192496) in view of Van et al., (U.S. Patent Application Publication No. 20200097938).
As to Claim 8, Bryant, Peterson, and Gregovic teach the system of claim 2.
Bryant, Peterson, and Gregovic do not teach receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account.
However Van teaches receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account; (0025: receive discounts (e.g., based on available benefits), 0024: discounts on goods and/or services offered by those other entities… based on benefits).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account. Motivation to do so comes from the knowledge well known in the art that receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account would help provide more incentives and benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 28, Bryant, Peterson, and Gregovic teach the method of claim 22.
Bryant, Peterson, and Gregovic do not teach wherein executing the instructions further causes the system to: receive a request for an incentive offer from the user based on the first benefit provided to the user; and generate the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account.
However Van teaches wherein executing the instructions further causes the system to: receive a request for an incentive offer from the user based on the first benefit provided to the user; and generate the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account; (0025: receive discounts (e.g., based on available benefits), 0024: discounts on goods and/or services offered by those other entities… based on benefits).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receive a request for an incentive offer from the user based on the first benefit provided to the user; and generate the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account. Motivation to do so comes from the knowledge well known in the art that receive a request for an incentive offer from the user based on the first benefit provided to the user; and generate the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account would help provide more incentives and benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
E. Claim(s) 9, and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bryant et al., (U.S. Patent Application Publication No. 20170330229) in view of Peterson et al., (U.S. Patent Application Publication No. 20130024260) in view of Gregovic, (U.S. Patent Application Publication No. 20210192496) in view of Sullivan et al., (Foreign Application No. CN114026644A).
As to Claim 9, Bryant, Peterson, and Gregovic teach the system of claim 2.
Bryant, Peterson, and Gregovic do not teach receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account.
However Sullivan teaches wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes; (description paragraph: the first machine learning model and/or the second machine learning model may include a type of neural network, such as dense layer neural network, residual neural network, convolutional neural network module, recursive neural network and so on. The neural network model may be configured to include an input layer, an output layer, and a set of hidden layers. The set of hidden layers may further include a set of normalized layers, a set of dense layers, a set of convolution layers, a set of pool layers, a set of active layers, a set of drain layers, and the like. in the training stage, the neural network model can be configured to receive the set of the contact matrix, from the set of sequencing reading section of sample with known variation (e.g., variation with known clinical significance), corresponding to the chromosome structure variation or the analogue sequencing reading section of the wild-type chromosome as input, in the form of a batch of data, as the input vector of the input layer, and generating output. The neural network model can be trained based on the input iteratively, and the trained neural network model is generated by comparing the output with the variation and the variation with significance. in the verification stage and/or execution stage, then performing the trained neural network model to generate an estimated output, the estimated output closely predicting the variation of the sample and/or the contact matrix and/or a significant variation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes. Motivation to do so comes from the knowledge well known in the art that wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes would help provide more accurate incentives and benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 29, Bryant, Peterson, and Gregovic teach the method of claim 22.
Bryant, Peterson, and Gregovic do not teach receiving a request for an incentive offer from the user based on the first benefit provided to the user; and generating the incentive offer for the user based on at least one of the first benefit or a second benefit conferrable to the user from a past account activity of the account.
However Sullivan teaches wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes; (description paragraph: the first machine learning model and/or the second machine learning model may include a type of neural network, such as dense layer neural network, residual neural network, convolutional neural network module, recursive neural network and so on. The neural network model may be configured to include an input layer, an output layer, and a set of hidden layers. The set of hidden layers may further include a set of normalized layers, a set of dense layers, a set of convolution layers, a set of pool layers, a set of active layers, a set of drain layers, and the like. in the training stage, the neural network model can be configured to receive the set of the contact matrix, from the set of sequencing reading section of sample with known variation (e.g., variation with known clinical significance), corresponding to the chromosome structure variation or the analogue sequencing reading section of the wild-type chromosome as input, in the form of a batch of data, as the input vector of the input layer, and generating output. The neural network model can be trained based on the input iteratively, and the trained neural network model is generated by comparing the output with the variation and the variation with significance. in the verification stage and/or execution stage, then performing the trained neural network model to generate an estimated output, the estimated output closely predicting the variation of the sample and/or the contact matrix and/or a significant variation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes. Motivation to do so comes from the knowledge well known in the art that wherein the first and second ML models comprises one of a multi-layer ML model, a neural network, a ML decision tree model, or a ML clustering model, and wherein at least one of the multi-layer ML model or the neural network comprises an input layer, at least one hidden layer, and the output layer each having one or more trained nodes would help provide more accurate incentives and benefits that the users would use in future purchases and that would promote an increase in the sales and would therefore make the method/system more profitable.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Predicting Customer Churn with Amazon Machine Learning” describes “Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This post describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn prediction. ML models rarely give perfect predictions though, so my post is also about how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML. I use an example of churn that is familiar to all of us–leaving a mobile phone operator. Seems like I can always find fault with my provider du jour! And if my provider knows that I’m thinking of leaving, it can offer timely incentives–I can always use a phone upgrade or perhaps have a new feature activated–and I might just stick around. Incentives are often much more cost effective than losing and reacquiring a customer.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20210090145 teaches similar invention which describes the user profile configuration circuit 216 is structured to enable the user to configure a user profile associated with the user's merchant offer account. For instance, the circuit 216 may enable the user to provide user-specific information regarding preferred products or services, preferred merchants, location information, user demographics, and the like. The circuit 216 may also enable the user to add the user's contacts (e.g., friends, family, etc.) to the user profile, including any gift-related dates associated with the user's contacts (e.g., anniversaries, birthdays, holidays, etc.), as well as product or service preferences for the contacts (e.g., hobbies, interests, preferred products, owned products, etc.). The circuit 216 may also enable the user to connect the user's merchant offer account to one or more social media profiles held by the user (e.g., Twitter, Facebook, Instagram, etc.), including to enable the merchant offer client application 136 (i.e., the offer computing system 102) to access the user's social media posts, feed, and contacts (e.g., friends, followers, etc.). The user may also enable the merchant offer client application 136 to post to the user's social media account(s) on behalf of the user (e.g., when a merchant offer is accepted or a product is purchased).
Conclusion
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B