DETAILED ACTION
Response to Amendment
This action is in response to the response to the amendment filed on 09/24/2025. Claims 1 and 3-21 have been amended, claim 2 has been canceled, and claim 22 has been newly added. Claims 1 and 3-22 are pending and currently under consideration for patentability.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 3-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1 and 3-22 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1, 3-7, 21, and 22 recite a method; claims 8-14 recite a system; and claims 15-20 recite a computer-readable media. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1 and 3-22 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Independent Claims 1, 8, and 15 recite limitations directed to the abstract idea including providing gift option information, wherein the gift option information includes an initial gift option; determining recipient information that tracks a redemption status of the initial gift option associated with a recipient; wherein the recipient information includes a previous redemption of the initial gift option; processing the recipient information to determine an alternate gift option, wherein the gift option information includes gift options predicted to result in redemption based on the recipient information, wherein the gift options exclude the initial gift option; and receiving a selection of the alternate gift option. These further limitations are not seen as any more than the judicial exception. Claims 1, 8, and 15 recite additional limitations including “training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption; associated with a gifter interface; using a processor; through the gift option machine learning model; wherein the gifter interface is updated to include the alternate redemption option; automatically transmitting a recipient interface including the alternate gift option; and retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information, wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface”. A method of determining alternate gift options based on recipient information in order for receiving selections of alternate gift options is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) because the claims are directed to providing gift option information, determining recipient information, processing recipient information to determine alternate gift options, and receiving selection of alternate gift option. Furthermore, a method of determining alternate gift options based on recipient information in order for receiving selections of alternate gift options is also considered to be fall under another grouping of abstract idea, specifically, Mental Processes; such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to providing data (i.e. gift option information); determining data (i.e. recipient information); determining data (i.e. alternate gift options); and receiving data (i.e. alternate gift option selection) which can all be performed in the human mind. Therefore, under Step 2A, Prong I, claims 1, 8, and 15 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1, 8, and 15 recite additional limitations including “training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption; associated with a gifter interface; using a processor; through the gift option machine learning model; wherein the gifter interface is updated to include the alternate redemption option; automatically transmitting a recipient interface including the alternate gift option; and retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information, wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface.” The additional limitations including “associated with a gifter interface; using a processor; and through the gift option machine learning model” are not found to integrate the judicial exception into a practical application because they are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f), adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g), and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. interface/processor/ML model, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1, 8, and 15 recite additional limitations including “training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption; associated with a gifter interface; using a processor; through the gift option machine learning model; wherein the gifter interface is updated to include the alternate redemption option; automatically transmitting a recipient interface including the alternate gift option; and retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information, wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface.” The additional limitations, reciting - “associated with a gifter interface; using a processor; and through the gift option machine learning model”, do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 8, and 15 also recite additional limitations – “training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption; wherein the gifter interface is updated to include the alternate redemption option; automatically transmitting a recipient interface including the alternate gift option; and retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information.” However, these additional elements or a combination of elements do not result in the claims amounting to significantly more than the judicial exception:
With respect to “training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption; and retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information”; merely training a machine learning model with data (i.e. redemption results from past gift options), using the machine learning model to determine data (i.e. alternate gift options), and retraining the machine learning model based on data (i.e. selections) in order to improve accuracy is seen as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) because this is a computer function that is well-understood, routine, and conventional. For example, it has been well-known since at least 1996 that “Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.” (See Wikipedia: Machine learning: The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.”). Furthermore, the limitation of “retraining the gift option machine learning model using the selection, thereby improving predictive accuracy of the gifter interface for a future gift redemption” is exactly the function of a machine learning model. Feedback or updated data is needed to retrain the learning model because the learning model develops and learns as it goes through the iterations.
With respect to “wherein the gifter interface is updated to include the alternate redemption option; automatically transmitting a recipient interface including the alternate gift option; and wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface”; merely updating an interface to display newer/different results and transmitting the updated interface is seen as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) because this is a computer function that is well-understood, routine, and conventional. For example, it has been well known since 1985 that graphical user interfaces that are updated to display new data (See Wikipeida: User Interface – “In 1985, with the beginning of Microsoft Windows and other graphical user interfaces, IBM created what is called the Systems Application Architecture (SAA) standard which include the Common User Access (CUA) derivative.”) (See also: Richard, Stéphane. "Text User Interface Development Series Part One – T.U.I. Basics". Archived from the original on 16 November 2014. Retrieved 13 June 2014.).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe generic computer-based elements, ¶¶ [0136] [0137], for implementing the “general purpose computers” or “general purpose processor”, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 3-7, 21 and 22, 9-14, and 16-20 further recite independent claims 1, 8, and 15, respectively. Dependent claims 3-7, 9-14, and 16-22 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1, 8, and 15. For example, claims 3-7, 9-14, and 16-22 describe the limitations for a method of determining alternate gift options based on recipient information in order for receiving selections of alternate gift options – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 3-7, 9-14, and 16-22, there are no additional elements introduced. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 11,341,523 to Jacoby in view of U.S. Publication 2013/0204739 to Friedman.
Claims 1, 3-7, 21, and 22; 8-14; and 15-20 are method, system, and computer-readable media claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 1:
Jacoby teaches:
A computer-implemented method, comprising: training a gift option machine learning model to determine gift options, wherein training the gift option machine learning model uses redemption results from past gift options and clustering algorithms to identify correlations between product information and gift redemption (i.e. training a machine learning model based on redemption results such as has the user previously used a reward and using clustering algorithms to identify correlations between item data and payment/redemption data) (Jacoby: Col. 27 Lines 17-29 “In particular embodiments, a machine-learning model may be trained, based on values of signals in a customer transaction history or database, to infer suggested gift offers based on values of those signals. Thus a trained machine-learning model may infer suggested gift offers based on values of the signals received subsequent to the training process so that the suggested gift offers can be presented to a user of a mobile wallet application. The signals may include signals related to identifying users who are likely to send a gift. The signals related to identifying customers may include "has the user previously sent a gift?", "has the user previously used a reward?", "does the user have any friends who use or have used rewards?", and so on.” Furthermore, as cited in Col. 9 Lines 1-23 “Machine learning models are used to create cluster sellers, for example by JTBD, in a more intelligent way than the MCC and revenue-based segmentation. For example, in one implementation, the methods and systems implement a Latent Dirichlet Allocation (LDA) model to itemization data to create itemization topics and itemization names. Reclassification of sellers is then accomplished by executing a Random Forest Classification model that predicts a seller category based on real-time data and historical data. After engineering an intuitive set of contextual features that define a seller's JTBD, the system implements an unsupervised clustering algorithm that takes these features as input and outputs the seller's most probable cluster. These clusters can be correlated to MCC since most clusters have only one or two MCCs overrepresented but can also illuminate similarities across MCCs and differences within MCCs. For example, a cluster with high card not present transactions, mobile payments, payment a few times a day, and sole professionals might have mostly home and repair and professional services MCC. Another cluster might consist of only Quick Service Restaurants (QSRs ), well defined by the payment activity, while some full-service restaurants (FSRs) might be clustered with retail sellers.”);
providing gift option information associated with a gifter interface, wherein the gift option information includes an initial gift option (i.e. display search results of gift offers via sender’s interface, wherein the options for gift offers are the initial gift option) (Jacoby: Col. 20 Lines 28-40 “FIG. SA illustrates an example user interface 500 providing one or more reward offers for selection. In particular embodiments, the user interface 500 may comprise a field 510 representing the virtual payment card. The user interface 500 may comprise one or more "cards" 520 representing one or more reward offers. The cards 520 may be displayed, for example, in the format of a carousel or a stack. Each reward offer card 520 may comprise a logo of a merchant associated with the reward offer, a name of the merchant, and content of the reward offer. As an example and not by way of limitation, the reward offer card 520a may show a logo for the merchant, a name of the merchant, and a specification 40 that the reward offer is redeemable for a 10% discount.”);
determining recipient information using a processor that tracks a redemption status of the initial gift option associated with a recipient; wherein the recipient information includes a previous redemption of the initial gift option (i.e. determining recipient information that tracks if the recipient has redeemed the gift offer, wherein the previous gift offer that was redeemed reads on the initial gift option) (Jacoby: Col. 27 Lines 17-44 “In particular embodiments, a machine-learning model may be trained, based on values of signals in a customer transaction history or database, to infer suggested gift offers based on values of those signals. Thus a trained machine-learning model may infer suggested gift offers based on values of the signals received subsequent to the training process so that the suggested gift offers can be presented to a user of a mobile wallet application. The signals may include signals related to identifying users who are likely to send a gift. The signals related to identifying customers may include "has the user previously sent a gift?", "has the user previously used a reward?", "does the user have any friends who use or have used rewards?", and so on. In particular embodiments, the signals may also include signals related to identifying a gift offer to be suggested to a user X who has been identified as having decided to send a gift to another user Y. The user X may be identified by a machine-learning model based on signals as described above. Alternatively, the identified user X may be a user who has selected a command for creating a gift offer in a user interface of the mobile wallet application. The signals related to identifying a gift offer to be suggested to a user X may include, for example, a "coffee" signal to determine whether the intended recipient Y is a "millennial mom who likes coffee." The coffee signal which may be based on several attributes, such as "subject has gone to a coffee shop in the past 7 days," "subject has at least two children," "subject has sent money to the children," and the like.”);
processing the recipient information through the gift option machine learning model to determine an alternate gift option, wherein the gift option information includes gift options predicted to result in redemption based on the recipient information, and wherein the gifter interface is updated to include the alternate redemption option (i.e. processing recipient and product attributes through machine learning model to determine a plurality of rewards, wherein the gift option are predicted to result in redemption and wherein the interface is updated to suggested values for the gift offers) (Jacoby: Col. 27 Lines 37-54 “The signals related to identifying a gift offer to be suggested to a user X may include, for example, a "coffee" signal to determine whether the intended recipient Y is a "millennial mom who likes coffee." The coffee signal which may be based on several attributes, such as "subject has gone to a coffee shop in the past 7 days," "subject has at least two children," "subject has sent money to the children," and the like. The machine-learning model may use the coffee signal to infer a recommendation that customer X should send customer Y (that qualifying mom) a $1 Off Starbucks Coffee gift. Further, the machine-learning model or other recommendation component may scan a list of friends Z of a given user X to identify signals the friends Z satisfy. The friends Z thus qualify for suggestions, and suggestions to send gift offers to the qualifying friends Z may be generated and presented to user X. For example, such a friend-based suggestion made to a user X may be "Send a gift offer to Brandon?".” Furthermore, as cited in Col. 20 Lines 40-49 “The user may select one or more of the reward offers 520 and interact with the user interface 500 (e.g., drag left or right) to view one or more reward offers 520. The user interface 500 may further comprise a prompt 530 asking the user to pick one or more reward offers and a button 540 allowing the user to indicate that she has finished picking reward offers. In this user interface 500, the user may also temporarily hide a reward offer, which may be withheld from being displayed for a period.” Furthermore, as cited in Col. 28 Lines 39-53 “The sending user may select an amount of value ( e.g., an amount of money or a percentage of savings) to add to the gift offer. For example, the sending user may tap $10 to specify that the gift offer's recipient is to be given $10 off their Boilermaker tab. In particular embodiments, the amount of value may be suggested based on information such as the merchant, maximum use count, use duration, or recipient, if known. If a recipient has not yet been identified, a suggested recipient may be identified by the payment service and used to determine suggested attributes, such as a suggested amount of value. A suggested amount of value may be provided by the payment service. The suggested amount of value may be determined or updated using a machine-learning model based on information associated with the sender or recipient, such as a transaction history.”);
automatically transmitting a recipient interface including the alternate gift option (i.e. interface is updated with different gift offers via carousel, wherein the carousel updates the interface to present different/alternate gift offers) (Jacoby: Fig. 14C and Col. 34 Lines 17-34 “FIG. 14C illustrates an example user interface 1400c providing a gift offer for selection. The user interface 1400c is similar to the user interface 500 of FIG. SA. In particular embodiments, the user interface 1400c may comprise a field 1410 representing the virtual payment card. The user interface 500 may comprise one or more "cards" 1420 representing one or more gift offers, reward offers, or a combination thereof. The cards 1420 may be displayed, for example, in the format of a carousel or a stack. Each gift offer card 1420 may comprise a logo of a merchant associated with the gift offer, a name of the merchant, and content of the gift offer. As an example and not by way of limitation, the reward offer card 1420 may show a logo for the merchant, a name of the merchant, and a specification that the gift offer is redeemable for a 10% discount. The user may select one or more of the gift or reward offers 1420 and interact with the user interface 1400c (e.g., drag left or right) to view one or more reward or gift offers 1420.”);
receiving a selection of the alternate gift option (i.e. receiving selection of gift offer via recipient interface) (Jacoby: Col. 34 Lines 28-38 “As an example and not by way of limitation, the reward offer card 1420 may show a logo for the merchant, a name of the merchant, and a specification that the gift offer is redeemable for a 10% discount. The user may select one or more of the gift or reward offers 1420 and interact with the user interface 1400c (e.g., drag left or right) to view one or more reward or gift offers 1420. The user interface 1400c may further comprise a prompt 1430 asking the user to pick one or more gift or reward offers and a button 1440 allowing the user to indicate that she has finished picking offers.”); and
retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information, wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface (i.e. re-training or updating machine learning model based on selection of gift offer value in order to identify correlations between merchant, customers, and gift redemption information) (Jacoby: Col. 28 Lines 39-56 “The sending user may select an amount of value ( e.g., an amount of money or a percentage of savings) to add to the gift offer. For example, the sending user may tap $10 to specify that the gift offer's recipient is to be given $10 off their Boilermaker tab. In particular embodiments, the amount of value may be suggested based on information such as the merchant, maximum use count, use duration, or recipient, if known. If a recipient has not yet been identified, a suggested recipient may be identified by the payment service and used to determine suggested attributes, such as a suggested amount of value. A suggested amount of value may be provided by the payment service. The suggested amount of value may be determined or updated using a machine-learning model based on information associated with the sender or recipient, such as a transaction history. In particular embodiments, when a recipient user receives a gift offer, a notification may be delivered to the recipient user, e.g., in a card and gift offer management user interface.” Furthermore, as cited in Col. 27 Lines 17-24 “In particular embodiments, a machine-learning model may be trained, based on values of signals in a customer transaction history or database, to infer suggested gift offers based on values of those signals. Thus a trained machine-learning model may infer suggested gift offers based on values of the signals received subsequent to the training process so that the suggested gift offers can be presented to a user of a mobile wallet application.” Furthermore, as cited in Col. 9 Lines 9-67 “After engineering an intuitive set of contextual features that define a seller's JTBD, the system implements an unsupervised clustering algorithm that takes these features as input and outputs the seller's most probable cluster. These clusters can be correlated to MCC since most clusters have only one or two MCCs overrepresented but can also illuminate similarities across MCCs and differences within MCCs. For example, a cluster with high card not present transactions, mobile payments, payment a few times a day, and sole professionals might have mostly home and repair and professional services MCC. Another cluster might consist of only Quick Service Restaurants (QSRs ), well defined by the payment activity, while some full-service restaurants (FSRs) might be clustered with retail sellers…To identify the MCCs and JTBD clusters for a specific merchant, a payment service system 108 (and/or other service) can collect merchant signals for the merchant. Merchant signals for a merchant may include reported data, collected data, training data, and third-party data associated with the merchant. For example, the payment service system 108 can receive reported data from a point-of-sale (POS) device 105 of a merchant and/or an online merchant interface to the payment service system 108 that is accessible by the merchant.”).
Jacoby does not explicitly disclose wherein the gift options exclude the initial gift option.
However, Friedman further discloses wherein the gift options exclude the initial gift option (i.e. gift option do not include duplicate gift options or exclude initial gift option) (Friedman: ¶ [0164] “Double Check Feature. In an exemplary embodiment, the IftGift system utilizes a "Double Check" feature which may prevent gift givers from sending duplicate suggestions. The feature shows gift givers if any of the currently displayed gifts were previously recommended to the recipient. It does not show who sent it. If desired, the giver could then change to another gift item.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Friedman’s gift options exclude the initial gift option to Jacoby’s retraining the gift option machine learning model using the selection, wherein retraining includes using the selection of the alternate gift option to identify updated correlations between the product information, the gift redemption, and the recipient information, wherein retraining results in ongoing customized recipient gift option information displayed through the gifter interface. One of ordinary skill in the art would have been motivated to do so in order to “prevent gift givers from sending duplicate suggestions.” (Friedman: ¶ [0164]).
With respect to Independent Claims 8 and 15:
All limitations as recited have been analyzed and rejected to claim 1. Claim 8 recites “A system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:” (Jacoby: Col. 35 Lines 40-48) perform the steps of method claim 1. Claim 15 recites “A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by a computer system, cause the computer system to:” (Jacoby: Col. 36 Lines 19-55) perform the steps of method claim 1. Claims 8 and 15 do not teach or define any new limitations beyond claim 1. Therefore they are rejected under the same rationale.
With respect to Claim 3:
Jacoby teaches:
The computer-implemented method of claim 1, wherein the gift option information includes a plurality of gift choices, and the gift options include a selection of a portion of the plurality of gift choices (i.e. search results provide plurality of gift choices and receiving selection includes selecting gift offer to be presented to recipient) (Jacoby: Col. 31 Lines 35-44 “At step 1222, the payment service system 108 may generate one or more search results using a machine-learning model based on one or more signals corresponding to the search parameters. At step 1223, the payment service system 108 may send, to a first client device associated with a sender for display, one or more of the search results. At step 1224, the payment service system 108 may receive a selection of one of the search results from the first client device, wherein the selection of the search results provides the approval of the gift offer.”).
With respect to Claims 10 and 17:
All limitations as recited have been analyzed and rejected to claim 3. Claims 10 and 17 do not teach or define any new limitations beyond claim 3. Therefore they are rejected under the same rationale.
With respect to Claim 4:
Jacoby teaches:
The computer-implemented method of claim 1, wherein the gift option information includes a plurality of gift categories, and the gift options include a selection of a gift category of the plurality of gift categories (i.e. gift are sorted according to attributes such as value or merchant category, wherein the selection includes a selection of a merchant category or value in order to view the associated with gift choices) (Jacoby: Col. 28 Lines 1-32 “The attributes of a gift offer may include a merchant at which the gift offer is to be accepted. The merchant may be merchant name or a merchant category or tag, value of the gift offer, which may be, e.g., a specific monetary value or a specific percent to be calculated when the gift offer is used, one or more criteria or conditions that may apply to the gift offer, a time limit, a maximum use count, a use duration or rate limit, and a name or description of the gift offer. In particular embodiments, the sending user may use the gift offer creation user interface to provide attributes such as the merchant at which the offer is to be accepted. If the sending user specifies a specific merchant, the gift offer may be for the specific merchant. The user interface may include a merchant search box in which the sending user can type a query string to search for merchants. The payment service may perform a search to identify merchants that match the query string. Names of merchants that match the query string may be displayed, and the user may select one of the names. In particular embodiments, a user may specify a particular merchant or a merchant category or tag. If the user specifies a tag or category for the merchant, the gift offer may be for merchants associated with the specified tag or category. For example, a tag or category of "shoes" may correspond merchants that sells shoes. A gift offer having the merchant category "shoes" may be used for any merchant in the "shoes" category. A tag or category of "sushi" may correspond restaurants that serve sushi. A gift offer having the merchant category "sushi" may be used for any restaurant in the "sushi" category.” Furthermore, as cited in Col. 28 Lines 39-50 “The sending user may select an amount of value ( e.g., an amount of money or a percentage of savings) to add to the gift offer. For example, the sending user may tap $10 to specify that the gift offer's recipient is to be given $10 off their Boilermaker tab. In particular embodiments, the amount of value may be suggested based on information such as the merchant, maximum use count, use duration, or recipient, if known. If a recipient has not yet been identified, a suggested recipient may be identified by the payment service and used to determine suggested attributes, such as a suggested amount of value. A suggested amount of value may be provided by the payment service.”).
With respect to Claims 11 and 18:
All limitations as recited have been analyzed and rejected to claim 4. Claims 11 and 18 do not teach or define any new limitations beyond claim 4. Therefore they are rejected under the same rationale.
With respect to Claim 6:
Jacoby teaches:
The computer-implemented method of claim 1, further comprising receiving a recognition initiation request including recognition recipient information associated with a recognition recipient, wherein the gift option information is based at least in part on the recognition recipient information (i.e. receiving request for search results of gift offers, wherein request includes value and recipient information and additional recognition information includes transaction information associated with recipient and awards are based on qualifying transaction information) (Jacoby: Col. 31 Lines 29-35 “The method may begin at step 1221, where a payment service system 108 may receive, by the payment service and from the client device, a request for search results based on one or more search parameters, wherein the search parameters include one or more of: an identification of the recipient, the value, or one or more merchant descriptors.” Furthermore, as cited in Col. 18 Lines 32-67 “At step 350, the payment service system 108 may automatically redeem the reward offer to reduce a value associated with the payment. In particular embodiments, the value associated with the payment may be directly reduced by a percentage or amount of money. Alternatively, the value may be indirectly reduced through a rebate. In particular embodiments, the payment service system 108 may send information to the point of sale of the merchant indicating that the reward offer has been redeemed to cause the merchant to charge a reduced price. Alternatively, the payment service system 108 may reduce the value of the payment and later resolve the reduced value with the merchant. In particular embodiments, the payment service system 108 may redeem a reward offer after a delay in time. As an example and not by way of limitation, the payment service system 108 may send to a user a reward offer that would apply to payments to a particular category of merchants (e.g., coffee merchants). The user may have made a qualifying payment to a merchant of the category ( e.g., a local coffee shop). However, the payment service system 108 initially may not recognize the merchant as qualifying and may not redeem the reward offer on the qualifying payment. One or more user interfaces of the application 222 may comprise one or more interactive elements allowing the user to report issues related to redemption of reward offers. The interactive elements may be associated with the reward offers or a displayed purchase history of the user. The payment service system 108 may receive a message or notification from the client device 103 of the user indicating that the reward offer should have but was not applied. The message or notification may be triggered by one or more user inputs in the application 222. In response to the message or notification, the payment service system 108 may retroactively apply a discount associated with the reward offer to the payment, which may result in an increase to the user's account balance.”).
With respect to Claims 13 and 20:
All limitations as recited have been analyzed and rejected to claim 6. Claims 13 and 20 do not teach or define any new limitations beyond claim 6. Therefore they are rejected under the same rationale.
With respect to Claim 7:
Jacoby teaches:
The computer-implemented method of claim 1, further comprising receiving a recognition initiation request including recognition recipient information associated with a recognition recipient, wherein the recognition recipient information is obtained using a real-time interactive interface (i.e. receiving request for search results of gift offers, wherein request includes value and recipient information and additional recognition information includes transaction information associated with recipient and awards are based on qualifying transaction information that occur in real-time) (Jacoby: Col. 31 Lines 29-35 “The method may begin at step 1221, where a payment service system 108 may receive, by the payment service and from the client device, a request for search results based on one or more search parameters, wherein the search parameters include one or more of: an identification of the recipient, the value, or one or more merchant descriptors.” Furthermore, as cited in Col. 18 Lines 32-67 “At step 350, the payment service system 108 may automatically redeem the reward offer to reduce a value associated with the payment. In particular embodiments, the value associated with the payment may be directly reduced by a percentage or amount of money. Alternatively, the value may be indirectly reduced through a rebate. In particular embodiments, the payment service system 108 may send information to the point of sale of the merchant indicating that the reward offer has been redeemed to cause the merchant to charge a reduced price. Alternatively, the payment service system 108 may reduce the value of the payment and later resolve the reduced value with the merchant. In particular embodiments, the payment service system 108 may redeem a reward offer after a delay in time. As an example and not by way of limitation, the payment service system 108 may send to a user a reward offer that would apply to payments to a particular category of merchants (e.g., coffee merchants). The user may have made a qualifying payment to a merchant of the category ( e.g., a local coffee shop). However, the payment service system 108 initially may not recognize the merchant as qualifying and may not redeem the reward offer on the qualifying payment. One or more user interfaces of the application 222 may comprise one or more interactive elements allowing the user to report issues related to redemption of reward offers. The interactive elements may be associated with the reward offers or a displayed purchase history of the user. The payment service system 108 may receive a message or notification from the client device 103 of the user indicating that the reward offer should have but was not applied. The message or notification may be triggered by one or more user inputs in the application 222. In response to the message or notification, the payment service system 108 may retroactively apply a discount associated with the reward offer to the payment, which may result in an increase to the user's account balance.” Furthermore, as cited in Col. 35 Lines 32-39 “As an example and not by way of limitation, one or more computer systems 1500 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 1500 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.”).
With respect to Claim 9:
Jacoby teaches:
The system of claim 8, wherein the gift option information is based at least in part on recognition quantity information (i.e. gift offer is based on award amount) (Jacoby: Col. 28 Lines 39-53 “The sending user may select an amount of value (e.g., an amount of money or a percentage of savings) to add to the gift offer. For example, the sending user may tap $10 to specify that the gift offer's recipient is to be given $10 off their Boilermaker tab. In particular embodiments, the amount of value may be suggested based on information such as the merchant, maximum use count, use duration, or recipient, if known. If a recipient has not yet been identified, a suggested recipient may be identified by the payment service and used to determine suggested attributes, such as a suggested amount of value. A suggested amount of value may be provided by the payment service. The suggested amount of value may be determined or updated using a machine-learning model based on information associated with the sender or recipient, such as a transaction history.”).
With respect to Claim 16:
All limitations as recited have been analyzed and rejected to claim 9. Claim 16 does not teach or define any new limitations beyond claim 9. Therefore it is rejected under the same rationale.
With respect to Claim 14:
Jacoby teaches:
The system of claim 8, wherein the gift option information is based on recognition initiation request and geographic information (i.e. gift option is based on gift creation request and geographic information) (Jacoby: Col. 27 Lines 55-67 “The gift offer creation user interface may include a set of data entry fields for displaying and entering attributes of the gift offer. The payment system may provide initial values for one or more of the attributes, which the user may change. The initial values may be identified by searching one or more databases. The initial values may also be identified by a search that uses a machine-learning model. The search may be based on a search query. The query may include terms received from the user, e.g., to generate autocomplete suggestions, or terms identified from other information, such as information related to the user, e.g., the user's geographic location, transaction history, and previously-sent gift rewards.”).
Claim(s) 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jacoby and Friedman in further view of U.S. Publication 2016/0371625 to Mosley.
With respect to Claim 5:
Jacoby and Friedman do not explicitly disclose the computer-implemented method of claim 1, further comprising receiving a recognition initiation request including a recognition type, wherein the gift option information is based at least in part on the recognition type.
However, Mosley further discloses receiving a recognition initiation request including a recognition type, wherein the gift option information is based at least in part on the recognition type (i.e. user initiates creating a recognition moment which includes quantity and recipient information, wherein user or employer or administrator selects recognition category or type of the plurality of recognition categories or types) (Mosley: ¶ [0042] “A user may be assisted in creating a recognition moment and may be presented with a user interface 300 containing step indicator 304, list of eligible recipients 308, selection status 312 and search function 316, as depicted in FIG. 3. The step indicator 304 may provide the user with the steps that have been taken and are to be taken in creating a recognition moment and may be replicated (with a corresponding highlighted step) as the user progresses (i.e., it may be replicated on user interface 300-700, as shown). The user may search for recipients via the search function 316 by entering the name of the eligible recipient and may select the recipient via the list of eligible recipients 308 that are found.” Furthermore, as cited in ¶ [0057] “As illustrated in FIG. 6, in one exemplary embodiment, the user may identify an award reason or recognition category for the recognition moment. The recognition category may be selected from a list of pre-defined reasons or provided by the user. An administrator of the employee recognition program may submit and maintain the predefined recognition categories. In some exemplary embodiments, the pr