DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
This Office Action addresses the response filed on 09/29/2025.
Claims 1-19 were amended.
Claim Objections
Claim 9 is objected to because of the following informalities: Claim 9 recites “The system of claim 1, stored one or more notification”. Examiner interprets the language as “The system of claim 1, further comprising stored one or more notification”. the suggestion is based on similar language recited by method claim 16. Appropriate correction is required.
Broadest Reasonable Interpretation of the Claims
Claims 1-19 were amended to recite optional limitations (claim language “optionally”). Optional limitations do not further limit claim scope. See In re Johnston, 435 F.3d 1381, 1384 (Fed. Cir. 2006) (“As a matter of linguistic precision, optional elements do not narrow the claim because they can always be omitted”).
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
According to MPEP 2106 II, It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility. Further, MPEP 2103 I C establishes that the subject matter of a properly construed claim is defined by the terms that limit the scope of the claim when given their broadest reasonable interpretation. It is this subject matter that must be examined. Regarding the independent claims, claims 1 and 13 recite “determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need”; “generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need”; “receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises” , language directed to contingent limitations. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) (precedential) for an analysis of contingent claim limitations in the context of both method claims and system claims. See also MPEP 2111.04. Claims 1 and 13 also recite “associating one or more approval and rejection workflows with the emergency cash loan wherein the one or more approval and rejection workflows optionally use one or more of: (a) one or more rules which are optionally based on…”, which is optional language not required by the Broadest Reasonable Interpretation of the claims.
With respect to the Eligibility Step 1 of the Alice/Mayo two-part test of the subject matter eligibility analysis (see MPEP 2106), in the instant case, claims 1-12 are directed to a system, and claims 13-19 are directed to a method. Therefore, these claims fall within the four statutory categories of invention.
Following step 2A, prong one of the analysis, the language of the independent claims reciting an abstract idea are marked in bold below:
a. determining need for a specific amount of an emergency cash loan for a specific loan time duration for a student program participant associated with one or more program participants while facing one or more crises;b. associating one or more approval and rejection workflows with the emergency cash loan wherein the one or more approval and rejection workflows optionally use one or more of: (a) one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions and stored as the configuration data related to the one or more approval and rejection workflows data, (b) one or more constraints stored as the configuration data related to the one or more approval and rejection workflows data, (c) one or more filters stored as the configuration data related to the one or more approval and rejection workflows data, (d) one or more insights stored as the configuration data related to the one or more approval and rejection workflows data;c. determining total of available earned points associated with the student program participant who is further associated with one or more program participants, earned by performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same one or more program participants, wherein the available earned points are based on sum of multiple of amount of each emergency cash loan and associated specific loan time duration;d. determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need;e. generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need;f. receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises;g. transferring the emergency cash loan to the student program participant associated with the one or more program participants;h. receiving one or more completion details related to the accepted one or more generation suggestions by the student program participant associated with the one or more program participants.
Therefore, the portions highlighted in bold above recite intermediary settlement and electronic recordkeeping, which is an abstract idea grouped within the certain methods of organizing human activity and mathematical concepts grouping of abstract ideas in prong one of step 2A. The claims are grouped within certain methods of organizing human activity because the steps recited describe the fundamental economic practice of loan processing. In addition, the claims are also grouped within mathematical concepts because the steps recited describe point determination, which represents a mathematical calculation. In situations like this where a series of steps recite judicial exceptions, examiners should combine all recited judicial exceptions and treat the claim as containing a single judicial exception for purposes of further eligibility analysis. See MPEP 2106.04 and 2106.05(II). Thus, the language identified in the certain methods of organizing human activity and mathematical concepts groupings were considered as a single abstract idea. Accordingly, the claims recite an abstract idea.
With respect to step 2A, prong two of the analysis, this judicial exception is not integrated into a practical application. Specifically, with respect to using processor and memory to perform the recited steps/functions, this additional element perform the steps or functions such as: “determining need…”, “associating… workflows…”, “determining… points…”, “transferring… loan…”, “receiving… details…”1. These additional elements are recited at a high-level of generality such that it represents no more than mere instructions to apply the exception using a generic computer component, which only serves to use computers as a tool to perform the abstract idea. Therefore, these elements do 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. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, following the analysis of step 2A, prong two, the claims are still directed to an abstract idea.
With respect to step 2B of the analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional computer elements, such as processor and memory perform the steps/functions of “determining need…”, “associating… workflows…”, “determining… points…”, “transferring… loan…”, “receiving… details…”, and amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept beyond the abstract idea of intermediary settlement and electronic recordkeeping. As discussed above, taking the claim elements separately, these additional elements perform the steps or functions that 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 intermediary settlement and electronic recordkeeping. Therefore, the independent claims are not eligible.
Examiner notes that, for elements recited in the dependent claims which were previously analyzed as additional elements of the independent claims above (i.e. processor and memory), the assessment of these elements under step 2A and step 2B for the dependent claims is inherited from the analysis of the independent claims and omitted for brevity, unless noted by Examiner below. Dependent claims 2-12 and 14-19 further recite the following additional language, in which elements which merely further define the identified abstract idea are marked in bold below:
i) wherein the stored configuration data related to the stored one or more program participants data includes one or more related constraints, one or more related filters, one or more related insights, and one or more related rules, the operations further comprising at least one of: classifying one or more program participants into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more program participants into one or more domains optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more program participants to one or more domains of one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining eligibility for one or more prospect program participants and one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
j) wherein the stored configuration data related to the stored one or more point earning activities data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: classifying one or more point earning activities into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more point earning activities into one or more domains optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more point earning activities to one or more domains of one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the one or more related insights; determining eligibility for one or more prospect point earning activities and one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; generating one or more proposals for one or more point earning activities with or without considering one or more point earnings for one or more student program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; assigning one or more point earning activities with or without planned one or more completion times with or without using one or more stored rules which are optionally based on one or more models developed by one or more self-learning algorithms optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining completion status of assigned one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; calculating earned points by using determined completion status of assigned one or more point earning activities optionally based on one or more of:(a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
k) wherein the stored configuration data related to the stored one or more point consumption activities data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: classifying one or more point consumption activities into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more point consumption activities into one or more domains with or without using one or more stored rules which are optionally based on one or more models developed by one or more self-learning algorithms optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more point consumption activities to one or more domains of one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining eligibility for one or more prospect point consumption activities and one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; generating one or more proposals for one or more point consumption activities for one or more student program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; assigning one or more point consumption activities with and without one or more schedules for assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining completion status of assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; calculating consumed earned points by using determined completion status of assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
l) wherein the stored configuration data related to the stored one or more point transfers data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: determining eligibility for one or more point transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point transfers optionally based on one or more of:(a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
m) wherein the stored configuration data related to the stored one or more point conversions data contains one or more constraints, one or more filters, one or more insights, and one or more rules, the operations further comprising at least one of: determining eligibility for one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; dynamically changing one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; evaluating one or more suggestions related to one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
n) further comprising stored one or more program transfers data, and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: determining eligibility for one or more program transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more program transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
o) further comprising stored one or more financial and subscription data, and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: managing one or more membership by one or more program participant to a program, optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; managing one or more subscription by one or more program participant to a program optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; managing one or more donations to a program to provide its operational costs optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
p) stored one or more notification data and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: issuing one or more notifications to one or more program participants and associated one or more student program participants related to providing and receiving help during one or more crises, optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights.
q) enabling data interaction with one or more users through one or more of: (a) one or more graphical user interfaces, assign (b) one or more application programming interfaces; access control for the one or more users; authenticating and enabling optionally enabling extract transfer load for the enabled data interaction through the one or more application programming interfaces; optionally enabling extract load transfer for the enabled data interaction through the one or more application programming interfaces; determining one or more point earning activities excluding the one or more activities providing one or more emergency cash loans to one or more student program participants associated with one or more program participants for approval; generating and sending the determined one or more point earning activities as one or more proposals for selection to the one or more student program participants associated with the one or more program participants; receiving selection of one or more point earning activities from the one or more student program participants associated with the one or more program participants for earning one or more earned points through the received selected one or more point earning activities; assigning the received selected one or more point earning activities to the one or more student program participants associated with the one or more program participants; enabling the one or more student program participants associated with the one or more program participants to further reassign the assigned received selected one or more point earning activities to one or more of the: (a) one or more relatives associated with the one or more student program participants associated with the one or more program participants, (b) one or more friends of the one or more student program participants associated with the one or more program participants, (c) one or more volunteers not associated with the one or more program participants; receiving one or more completion progress details related to the assigned, reassigned, or both the one or more point earning activities related to the one or more student program participants associated with the one or more program participants:; calculating one or more earned points for partially or fully completed the assigned, reassigned, or both one or more point earning activities for the one or more student program participants associated with the one or more program participants:; determining one or more recipients for the one or more earned points based on one or more user selections from the one or more student program participants associated with the one or more program participants and /or by using the stored data:; assigning the one or more earned points to the one or more determined recipients while the assigned one or more earned points are to be consumed by the one or more determined recipients when faced by one or more crises through help activities performed by other one or more student program participants associated with the one or more program participants, performed by someone on behalf of the one or more student program participants or performed by the both where entitlement to the point consuming activities allowed to match the one or more earned points.
r) determining one or more point consuming activities to provide help to the one or more determined recipients when the one or more determined recipients face one or more crises, wherein the help is provided through one or more point earning activities performed by other one or more student program participants associated with the one or more program participants, someone on behalf of the other one or more student program participants associated with the one or more program participants, or both, where entitlement to the help received through the point consuming activities is allowed to match the one or more earned points associated with the one or more determined recipients; sending the determined one or more point consuming activities for selection to the one or more determined recipients; receiving one or more selections of one or more point consuming activities which are related to the one or more crises being faced by the one or more determined recipients; determining one or more earned points required for the received one or more selections of the one or more point consuming activities which are related to the one or more crises being faced by the one or more determined recipients; executing or scheduling the one or more selections of one or more point consuming activities when there are sufficient one or more earned points available; generating one or more suggestions for one or more additional point earning activities to earn insufficient one or more earned points for the one or more selections of one or more point consuming activities when there are not sufficient one or more earned points available for the one or more selections of the one or more point consuming activities to provide help to the one or more determined recipients facing one or more crises; sending the generated one or more suggestions for selection of the one or more additional point earning activities to the one or more determined recipients facing one or more crises; receiving one or more selections for the one or more additional point earning activities and one or more planned times for conducting the additional point earning activities from the one or more determined recipients facing one or more crises; generating one or more reminder notification generation schedules; assigning and /or scheduling the generated one or more point consumption activities to the one or more determined recipients facing one or more crises.
s) wherein the point earning activities performed by the one or more student program participants associated with the one or more program participants, performed by one or more persons on behalf of the one or more student program participants, or performed by both and, for the purpose of providing help to other one or more student program participants, optionally include at least one of: one or more temporary accommodation providing activities; one or more textbook donation activities; one or more food donation activities; one or more sick care activities excluding one or more activities that require involvement of regulated medical professional; one or more non-prescribed medication donation activities; one or more clothes donation activities; one or more transit pass donation activities; one or more course tuition fee donation activities; one or more computing equipment lending activities; one or more emergency travel cost activities; one or more communication cost donation activities.
With respect to claim 2, the claim recites item i) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claim recites item i) above, which represents the additional elements/functions of classifying data. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 3, the claim recites item j) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claim recites item j) above, which represents the additional elements/functions of classifying data. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 4, the claim recites item k) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claim recites item k) above, which represents the additional elements/functions of classifying data. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 5, the claim recites item l) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claim recites item l) above, which represents the additional elements/functions of eligibility determination. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 6, the claim recites item m) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claim recites item m) above, which represents the additional elements/functions of eligibility determination. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claims 7 and 14, the claims recite item n) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claims recite item n) above, which represents the additional elements/functions of eligibility determination. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claims 7 and 14, the claims recite item n) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claims recite item n) above, which represents the additional elements/functions of eligibility determination. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claims 8 and 15, the claims recite item o) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claims recite item o) above, which represents the additional elements/functions of membership and subscription management. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claims 9 and 16, the claims recite item p) above, language directed to non-functional descriptive material by describing what the data is. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claims recite item p) above, which represents the additional elements/functions of issuing notifications. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claims 10 and 17, the claims recite item q) above, which represents the additional elements/functions of data interaction through interfaces. This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
Examiner notes that claims 11 and 18 recite “determining one or more point consuming activities to provide help to the one or more determined recipients when the one or more determined recipients face one or more crises”; “executing or scheduling the one or more selections of one or more point consuming activities when there are sufficient one or more earned points available;”; “generating one or more suggestions for one or more additional point earning activities to earn insufficient one or more earned points for the one or more selections of one or more point consuming activities when there are not sufficient one or more earned points available for the one or more selections of the one or more point consuming activities to provide help to the one or more determined recipients facing one or more crises;” , language directed to contingent limitations. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) (precedential) for an analysis of contingent claim limitations in the context of both method claims and system claims. See also MPEP 2111.04. With respect to the eligibility analysis of claims 11 and 18, This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claims 12 and 19, This language further elaborates the abstract idea of intermediary settlement and electronic recordkeeping identified in the analysis of independent claims 1 and 13. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
Therefore, while the additional language i)-s) of dependent claims 2-12 and 14-19 slightly modify the analysis provided with respect to independent claims 1 and 13, these additional elements/functions are insufficient to render the dependent claims eligible, as detailed above. Therefore, these dependent claims are also ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 13 were amended to recite “determining need for a specific amount of an emergency cash loan for a specific loan time duration for a student program participant associated with one or more program participants while facing one or more crises;”. The specification as filed recites, inter alia:
“[0019] Figure 4 is a diagram conceptually illustrating an exemplary one or more calculations of one or more earned points for one or more point earning activities according to the present invention. The act of creating data related to Emergency Cash Loan Providing Activity 401 results in generation of one or more earned points by giving one or more emergency cash loans to one or more Student Program Participants with urgent need of cash. ”
Therefore, the specification as filed does not recite the algorithm for determining need for a specific amount of cash loan for a specific loan time duration. While the specification as filed recites "giving one or more emergency cash loans to one or more Student Program Participants", the details of determining specifics of the amount and duration of the loan are not sufficiently disclosed in the specification as filed. Therefore, the specification as filed does not provide sufficient written description for the claimed language (see MPEP 2161.01). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. Dependent claims 2-11 and 14-19 are also rejected since they depend on claims 1 and 13, respectively.
Claims 1 and 13 were amended to recite “wherein the one or more approval and rejection workflows optionally use one or more of: (a) one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions and stored as the configuration data related to the one or more approval and rejection workflows data,”. The specification as filed recites, inter alia:
“[0007] … The data model Workflow 217 stores data related to approval and rejection one or more workflows. The data model Workflow Configuration 218 may store data such as one or more data schema, constraints, filters, insights, rules, default values, validations, types, format information, presentation templates and other configuration data which is used to create, read, delete and update data related to the data model Workflow 217...
[0018] The Workflow Module 310 contains computer instructions to read, write, update and delete data related to workflow by mainly using the data model Workflow 217 and the data model Workflow Configuration 218...[0022] The one or more stored rules used by one or more modules may be based on one or more models developed by one or more self-learning algorithms with or without human interventions”
Therefore, the specification as filed does not recite rules for approval and rejection workflows based on one or more models developed by one or more self-learning algorithms. Specifically, the algorithms for performing these steps/functions are not part of the disclosure as filed. Therefore, the specification as filed does not provide sufficient written description for the claimed language (see MPEP 2161.01). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. Dependent claims 2-11 and 14-19 are also rejected since they depend on claims 1 and 13, respectively.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites “the system” in line 29. There is insufficient antecedent basis for this language in the claim. Dependent claims 14-19 are also rejected since they depend on claim 13.
Claim 13 recites “the at least one processor” in line 45. There is insufficient antecedent basis for this language in the claim. Dependent claims 14-19 are also rejected since they depend on claim 13.
Claim 13 recites “the instructions” in line 45. There is insufficient antecedent basis for this language in the claim. Dependent claims 14-19 are also rejected since they depend on claim 13.
Claim Rejections - 35 USC § 103
Claims 1-9 and 13-16 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Chang et al. (US 2023/0289909 A1), hereinafter Chang, in view of Trivedi et al. (US 2015/0348166 A1), hereinafter Trivedi, and in view of Bloy et al. (US 2020/0118156 A1), hereinafter Bloy.
With respect to claims 1 and 13, Chang teaches a system for facilitating help among student program participants associated with at least one program participant while in crises by utilizing data related to help activities, wherein the help is not promotion of one or more commercial products, one or more commercial services, or both, wherein earned points are used to quantify extent of received help in crises and extent of provided help in crises; wherein entitlement to extent of received help in crises match sum of extent of past help provided and extent of planned future help, wherein the type of help provided is based on types of crises faced by student program participants, wherein different types of earned points are convertible, wherein the system is not a campaign planning system, the system comprising: at least one processor coupled to at least one memory storing instructions; computer-readable stored one or more program participants data and related stored configuration data with one or more access control; computer-readable stored one or more point earning activities data and related stored configuration data with one or more access control; computer-readable stored one or more point consumption activities data and related stored configuration data with one or more access control; computer-readable stored one or more point conversions data and related stored configuration data with one or more access control; computer-readable stored one or more point transfers data and related stored configuration data with one or more access control; computer-readable stored one or more approval and rejection workflows data and related stored configuration data with one or more access control; computer-readable stored one or more subscriptions data and related stored configuration data with one or more access control; the at least one processor configured to execute the instructions to perform operations to provide help to student program participants (see Fig. 1, data elements, paragraphs [0027]-[0030], computer, paragraphs [0033]-[0035]); and a computer-implemented method for facilitating help among student program participants associated with at least one program participant while in crises by utilizing data related to help activities, wherein the help is not promotion of one or more commercial products, one or more commercial services, or both, wherein earned points are used to quantify extent of received help in crises and extent of provided help in crises; wherein entitlement to extent of received help in crises match sum of extent of past help provided and extent of planned future help, wherein the type of help provided is based on types of crises faced by student program participants, wherein different types of earned points are convertible, wherein the system is not a campaign planning system, and the computer-implemented method comprising: computer-readable stored one or more program participants data and related stored configuration data with one or more access control; computer-readable stored one or more point earning activities data and related stored configuration data with one or more access control; computer-readable stored one or more point consumption activities data and related stored configuration data with one or more access control; computer-readable stored one or more point conversions data and related stored configuration data with one or more access control; computer-readable stored one or more point transfers data and related stored configuration data with one or more access control; computer-readable stored one or more approval and rejection workflows data and related stored configuration data with one or more access control; computer-readable stored one or more subscriptions data and related stored configuration data with one or more access control; the at least one processor configured to execute the instructions to perform operations to provide help to student program participants (A method for monitoring the academic progress of a user) comprising:
determining total of available earned points associated with the student program participant who is further associated with one or more program participants, earned by performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same one or more program participants, wherein the available earned points are based on sum of multiple of amount of each emergency cash loan and associated specific loan time duration (see paragraph [0068]: “Upon completion of the ENTER EDUCATION stage 402, there are a number of pathways to achieve rewards, wherein the user may complete some or all of the pathways. Rewards may be financial such that they may be redeemed against educational programme and material costs, tuition costs or loans, costs outside of the educational programme, such as user rent or travel, or be put towards maintenance and infrastructure work carried out in the education facility. The rewards may take the form of a points system such that points accumulate towards a goal wherein acknowledgment is issued in the form of a recognizable award that is desirable to future employers. Alternatively or additionally, the rewards system may rank each user. In this way, the user, educator, other users and third parties, such as employers, customers, clients and homeowners, may view a user's ranking. Therein, a user is provided with motivation to achieve a high ranking.”; paragraph [0070]: “A reward wallet is created for the user and associated with the educator on the central resource. The user's progress towards, as well as achievement of, rewards is recorded on the central resource. In this way, an overview of all users academic and reward progress is recorded to the central resource for monitoring and comparison against other users.”; paragraph [0072]: “There follows a REWARDS stage 411, wherein it is recorded on the central resource that the user has earned a reward. The provision of the reward is automated. In this way, the user is automatically provided with a reward, or the educator or a third party are notified that the user has earned a reward such that further manual action may be taken by the educator or a third party to provide the user with said reward.”; paragraph [0074]: “Following this stage, if appropriate, there is initiated a PARTICIPATION stage 406 wherein the user is prompted to participate in educator reward programmes and projects. These may be non-compulsory, supplementary educational opportunities that the educator is providing or such tasks may be compulsory but with a choice as to which reward programme or project is selected by the user. In this way, the user is made aware of supplementary opportunities to their educational programme such that educational benefit is gained, or the user can make a better selection given the improved monitoring of their educational progress towards a personal objective. In this example, the educator rewards project is participating in repair work about the grounds of the educational facility or in the community.”; paragraph [0075]: “Next, the user may progress to the REWARD stage 411, wherein the user earns a reward for participating in the educator reward programme or project.”);
Chang does not explicitly disclose a system and method comprising: determining need for a specific amount of an emergency cash loan for a specific loan time duration for a student program participant associated with one or more program participants while facing one or more crises; associating one or more approval and rejection workflows with the emergency cash loan wherein the one or more approval and rejection workflows optionally use one or more of: (a) one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions and stored as the configuration data related to the one or more approval and rejection workflows data, (b) one or more constraints stored as the configuration data related to the one or more approval and rejection workflows data, (c) one or more filters stored as the configuration data related to the one or more approval and rejection workflows data, (d) one or more insights stored as the configuration data related to the one or more approval and rejection workflows data; determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need; generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need; receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises; receiving one or more completion details related to the accepted one or more generation suggestions by the student program participant associated with the one or more program participants. transferring the emergency cash loan to the student program participant associated with the one or more program participants.
However, Trivedi discloses a system and method (System and method for providing enhanced financial services based on social signals) comprising:
determining need for a specific amount of an emergency cash loan for a specific loan time duration for a student program participant associated with one or more program participants while facing one or more crises (see paragraph [0030]: “In various embodiments, an account holder's social signals may include a status update indicating that the account holder's car has broken down and/or is in the shop. The social signal may include a status update indicating the account holder is looking for a new car. Products module 104 may generate one or more financial products based on this information. The financial products may include a car loan, or an offer for a line of credit that includes an initial 10,000 rewards points that can be redeemed with a local car rental agency.”; Fig. 3, 303, paragraph [0053]: “At block 303, social signals may be received from the social media account....");
associating one or more approval and rejection workflows with the emergency cash loan wherein the one or more approval and rejection workflows optionally use one or more of: (a) one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions and stored as the configuration data related to the one or more approval and rejection workflows data, (b) one or more constraints stored as the configuration data related to the one or more approval and rejection workflows data, (c) one or more filters stored as the configuration data related to the one or more approval and rejection workflows data, (d) one or more insights stored as the configuration data related to the one or more approval and rejection workflows data (see paragraph [0054]: “At block 304, one or more product offerings may be generated based on the social signals. The financial product may be a credit limit increase program. The product may be targeted to the account holder based on the social signals from the social media account associated with the account holder. In one example, the account holder may currently have a credit account. The social signals system may generate a credit limit increase offer for the account holder, with better products & terms than the current credit account. The offer may be based on received social signals. The social signal may be event data indicating the account holder just got his first job out of college. The social signal may include profile data with the name of the new employer. The social signal may include geo-location data and/or profile data with the location of the new employer and/or the new location where the account holder is going to live. The credit limit increase offer may be based on these signals. If the social signals indicate that the account holder just obtained his MBA and got a job with a large consulting firm (based on status updates and changes to the account holder's social media profile), the credit limit increase offer generated by the social signals system may include a higher line of credit (e.g., an increase from a $10,000 credit limit to a $20,000 credit limit, with no annual fee). If the social signals indicate that the account holder obtained a bachelor's degree and got an entry-level marketing job, the credit limit increase offer may be smaller (e.g., an increase from a $10,000 credit limit to a $12,500 credit limit, with a smaller annual fee).”; paragraph [0056]: “In various embodiments, social signals system may generate a risk score for the account holder based on the received social signals. The risk score may be used by the financial institution and/or social signals system to generate financial product offers for the account holder in lieu of a bad credit score. The risk score may be based on profile data (e.g., the account holder's current job, how long they have been employed there). The risk score may be based on event data. For example, if the social signals include a new status update indicating that the account holder just obtained a relatively secure job as a tenured professor at a university, the social signals system may lower the risk score of the account holder. A lower risk score may indicate that the account holder is eligible for financial products with better terms (such as pre-approval for certain lines of credit). Social signals system may generate a financial product offer based on the better risk score, even if the account holder has a relatively low credit score. Method 300 may proceed to block 305.”);
transferring the emergency cash loan to the student program participant associated with the one or more program participants (see Fig. 3, block 305, paragraph [0057]: “At block 305, the product offer may be provided to the account holder. The offer may be provided to a device associated with the account holder. The account holder may be able to view the offer and its terms using an application on the device. For example, the application may be part of a mobile banking application. The account holder may be able to accept the offer and/or respond to the offer using an interface provided by the application.”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the enhanced financial products as disclosed by Trivedi in the system and method of Chang, the motivation being to identify relevant events in a person's life early can help entities market relevant products and offer the best terms to an individual based on the life event. (see Trivedi, paragraph [0004]).
The combination of Chang and Trivedi does not explicitly disclose a system and method comprising: determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need; generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need; receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises; receiving one or more completion details related to the accepted one or more generation suggestions by the student program participant associated with the one or more program participants.
However, Bloy discloses a system and method (Automated solution for loyalty rewards points) comprising:
determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need; generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need; receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises; receiving one or more completion details related to the accepted one or more generation suggestions by the student program participant associated with the one or more program participants (see paragraph [0016]: “In the present solution, users can identify particular rewards for which they are interested or are working towards. In some instances, the identification may be made specifically for a reward (e.g., a particular trip associated with a predefined point value for redemption), while in others, the particular reward may be automatically identified or determined based on prior user actions or interactions (e.g., identifying potential travel to be taken based on prior user travel, a reward for a free flight or hotel night may be identified). Using an offer management system, a determination can be made, upon reviewing the identified reward and its associated point value, whether the user should be eligible for an offer to borrow future points now for the identified reward. The determination can be based on a number of considerations.”; paragraph [0017]: “First, a number of points still needed to reach the reward level may be determined. The number of points may be considered as a relative amount towards the overall number of points needed (e.g., only 10% of points required are needed), or an absolute number of additional points (e.g., 1000 points are required to reach the goal). For example, if the user has 90% of the points needed for a trip, it may be more likely that an offer is to be made. However, if that point amount is a relatively significant amount, the offer may not be made. The specific offer requirements can be provided in a predefined and/or dynamic set of rules used to evaluate how to proceed.”; paragraph [0018]: “In addition to the points required, a user-specific analysis can be performed to determine, at least in part, whether the offer should be generated. For example, a user's transaction history can be used to determine a likely or expected amount of points that are to be awarded to the user during a particular period of time. In some instances, points may be assigned after each billing cycle, for example, based on the amount spent and any particular additional point-earning actions or transactions. In some instances, a current billing cycle or period may be considered by the offer management system as well to determine whether the user has already earned at least a part of the portion needed in recent, but not yet billed, transactions or interactions. Using the user's transaction and/or point-earning history as a basis, a determination can be made as to how many billing cycles or point-assigning periods that would be needed before the user can repay any borrowed points from an offer. If a user typically earns 7000 points per billing cycle (e.g., per month), then an offer of 1000 points to reach a particular reward may be acceptable based on the rules. However, if a user is new, and has little transaction history, providing a large offer of points may not be acceptable. In those instances, if an offer is provided, the user may accept the offer, redeem the reward, and then abandon use of card or account associated with the rewards, leaving the amount borrowed outstanding and any benefits provided by the offer remaining unpaid for.”; paragraph [0019]: “In addition to identifying and offering future points, the present solution can also provide tools and mechanisms for managing financial statement generation after the point borrowing mechanism is introduced. In particular, some financial account management systems may not be equipped to manage and monitor point deficits occurring after an offer to borrow points is accepted. In those instances, a new field may be added to or associated with the financial account of the user. In some instances, that new field may be a custom-defined data field used to manage and/or identify any deficits in points occurring after the borrowing process. The field may be associated with each user account, or may only be defined for particular users who obtain or otherwise incur a deficit in points due to an accepted borrowing offer. In normal instances, a statement generation process can access an account management system and a loyalty account management system to obtain information about a current account balance and a loyalty point balance, respectively. In a revised system based on the current solution, the statement generation process, which may be executed by the financial institution, one of its systems, or a third-party system, as a batch process, can be interrupted to perform a determination whether a deficit exists for a particular user account prior to generating that account's statement. If a deficit does exist, then the information associated with the deficit can be inserted into the corresponding statement for the user. The account balance information and the loyalty point balance information can then be obtained as normal, and an updated statement can be generated and provided to the user reflecting any point deficit or debt.”; paragraph [0034]: “The goal analyzer 116 can perform operations associated with identifying one or more reward goals 154 identified by or for the customer. The goal analyzer 116 can access the loyalty account database 146 using an accounts interface 118, which can perform operations and authorizations required to access a loyalty account associated with a particular account identifier 148 and corresponding to a particular customer account 132. The identified reward goals 154 can be identified from the loyalty account profile 152, along with any point value costs associated with those rewards..."; Fig. 2A, paragraph [0046]: “As illustrated in FIG. 2A, at 1a and 1b, the offer management system 206 determines whether or not a point lending offer is to be generated. The point lending offer analysis may be performed by the offer management system 206 at particular intervals or periods, in response to customer action (e.g., an indication of a customer reviewing a rewards catalog, in response to a purchase or transaction performed by the customer related to a good or service included in or related to a reward goal, in response to a loyalty program login, etc.). The offer management system 206 may consider one or more offer rules associated with a points loan offer, where at least some of the offer rules are specific to the customer and some are generally applied to all customers. At 1a, the offer management system 206 can identify information stored in a financial system 202 associated with a particular customer, including an account balance (e.g., $10K), and whether a current points deficit exists, as well as one or more analytical or historical sets of information associated with the customer. Additionally, at 1b, the offer management system 206 can access the loyalty management system 204 to determine an existing point balance (e.g., 5800 points) and whether any existing reward goals have been identified for the customer 208 and their associated loyalty account. In the initial illustration, a reward X has been identified as a reward goal, with a required point value of 6000 points. In other words, 200 points more than the customer current has available.”; paragraph [0056]: “Turning to method 300, at 305 a triggering event initiating a point balance loan offer analysis for a first customer account can be identified. The point balance loan offer analysis can be an analysis of the customer account and related loyalty account to determine whether to offer the customer a loan of loyalty points for a loyalty program in order to achieve or reach a point amount corresponding to the at least one identified reward goal. In some instances, the triggering event may be a periodic account analysis, either specific to the first customer account or a batch process performed across some or all of the plurality of accounts. In some instances, the triggering event may be a manual request from the customer (e.g., via a loyalty program application). In other instances, one or more account events may trigger the analysis, such as a large transaction being processed, a determination that the customer reviews the rewards catalog, or any other suitable event. In some instances, the trigger may be initiated by an offer management system.”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the offer management system as disclosed by Bloy in the system and method of Chang and Trivedi, the motivation being to determine whether the user should be eligible for an offer to borrow future points now for an identified reward (see Bloy, paragraph [0016]: “In the present solution, users can identify particular rewards for which they are interested or are working towards. In some instances, the identification may be made specifically for a reward…”)
With respect to the BRI of the claims, Examiner notes that claims 1 and 13 recite “determining insufficient earned points based on the determined total of available earned points and the determined need for a specific amount of emergency cash loan for a specific loan time duration, when the total of available earned points are not sufficient to meet the determined need;”; “generating one or more suggestions for performing one or more emergency cash loan providing activities toward other one or more student program participants associated with the same associated program participants to compensate the determined insufficient earned point, when the total of available earned points is not sufficient to meet the determined need;”; “receiving acceptance for the generated one or more suggestions from the student program participant, when the total of available earned points is not sufficient to meet the need associated with the one or more crises”, language directed to contingent limitations. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) (precedential) for an analysis of contingent claim limitations in the context of both method claims and system claims. See also MPEP 2111.04.
With respect to claim 2, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system as described above with respect to claim 1. Furthermore, Bloy discloses a system wherein the stored configuration data related to the stored one or more program participants data includes one or more related constraints, one or more related filters, one or more related insights, and one or more related rules, the operations further comprising at least one of: classifying one or more program participants into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more program participants into one or more domains optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more program participants to one or more domains of one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining eligibility for one or more prospect program participants and one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 3, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system as described above with respect to claim 1. Furthermore, Bloy discloses a system wherein the stored configuration data related to the stored one or more point earning activities data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: classifying one or more point earning activities into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more point earning activities into one or more domains optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more point earning activities to one or more domains of one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the one or more related insights; determining eligibility for one or more prospect point earning activities and one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; generating one or more proposals for one or more point earning activities with or without considering one or more point earnings for one or more student program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; assigning one or more point earning activities with or without planned one or more completion times with or without using one or more stored rules which are optionally based on one or more models developed by one or more self-learning algorithms optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining completion status of assigned one or more point earning activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; calculating earned points by using determined completion status of assigned one or more point earning activities optionally based on one or more of:(a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 4, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system as described above with respect to claim 1. Furthermore, Bloy discloses a system wherein the stored configuration data related to the stored one or more point consumption activities data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: classifying one or more point consumption activities into one or more categories optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; classifying one or more point consumption activities into one or more domains with or without using one or more stored rules which are optionally based on one or more models developed by one or more self-learning algorithms optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; associating one or more categories of one or more point consumption activities to one or more domains of one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; identifying one or more prospect point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining eligibility for one or more prospect point consumption activities and one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; generating one or more proposals for one or more point consumption activities for one or more student program participants optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; assigning one or more point consumption activities with and without one or more schedules for assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; determining completion status of assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; calculating consumed earned points by using determined completion status of assigned one or more point consumption activities optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 5, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system as described above with respect to claim 1. Furthermore, Bloy discloses a system wherein the stored configuration data related to the stored one or more point transfers data includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising at least one of: determining eligibility for one or more point transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point transfers optionally based on one or more of:(a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 6, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system as described above with respect to claim 1. Furthermore, Bloy discloses a system wherein the stored configuration data related to the stored one or more point conversions data contains one or more constraints, one or more filters, one or more insights, and one or more rules, the operations further comprising at least one of: determining eligibility for one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; ranking one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; dynamically changing one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights; evaluating one or more suggestions related to one or more point conversions optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claims 7 and 14, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system and method as described above with respect to claims 1 and 13. Furthermore, Bloy discloses a system and method further comprising stored one or more program transfers data, and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: determining eligibility for one or more program transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]);
ranking one or more program transfers optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see paragraphs [0034], [0038] and [0059]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claims 8 and 15, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system and method as described above with respect to claims 1 and 13. Furthermore, Trivedi discloses a system and method further comprising stored one or more financial and subscription data, and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: managing one or more membership by one or more program participant to a program, optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see Profile information, paragraph [0025]);
managing one or more subscription by one or more program participant to a program optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see subscriber data, paragraph [0021]);
managing one or more donations to a program to provide its operational costs optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see likes, paragraph [0024]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claims 9 and 16, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system and method as described above with respect to claims 1 and 13. Furthermore, Bloy discloses a system and method stored one or more notification data and related stored configuration which includes related one or more constraints, related one or more filters, related one or more insights, and related one or more rules, the operations further comprising: issuing one or more notifications to one or more program participants and associated one or more student program participants related to providing and receiving help during one or more crises, optionally based on one or more of: (a) the related one or more rules which are optionally based on one or more models developed by one or more self-learning algorithms with or without human interventions, (b) the related one or more constraints, (c) the related one or more filters, (d) the related one or more insights (see notofications, paragraphs [0049] and [0050]). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
Claims 10-12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 2023/0289909 A1), in view of Trivedi (US 2015/0348166 A1), in view of Bloy (US 2020/0118156 A1), in view of Ding (US 2022/0148011 A1)
With respect to claims 10 and 17, the combination of Chang, Trivedi and Bloy teaches all the subject matter of the system and method as described above with respect to claims 1 and 13. Furthermore, Bloy discloses a system and method enabling data interaction with one or more users through one or more of: (a) one or more graphical user interfaces, assign (b) one or more application programming interfaces; access control for the one or more users (see interfaces, paragraph [0042]);
authenticating and enabling optionally enabling extract transfer load for the enabled data interaction through the one or more application programming interfaces; optionally enabling extract load transfer for the enabled data interaction through the one or more application programming interfaces (see APIs, paragraph [0023]);
determining one or more point earning activities excluding the one or more activities providing one or more emergency cash loans to one or more student program participants associated with one or more program participants for approval: (see instructions to increase the point deficit, paragraph [0049]);
generating and sending the determined one or more point earning activities as one or more proposals for selection to the one or more student program participants associated with the one or more program participants (see instructions to increase the point deficit, paragraph [0049]);
receiving selection of one or more point earning activities from the one or more student program participants associated with the one or more program participants for earning one or more earned points through the received selected one or more point earning activities (see acceptance, paragraph [0047]);
assigning the received selected one or more point earning activities to the one or more student program participants associated with the one or more program participants (see paragraph [0048]);
receiving one or more completion progress details related to the assigned, reassigned, or both the one or more point earning activities related to the one or more student program participants associated with the one or more program participants: (see Fig. 2D, statement, paragraph [0053]);
calculating one or more earned points for partially or fully completed the assigned, reassigned, or both one or more point earning activities for the one or more student program participants associated with the one or more program participants: (see Fig. 2D, statement, paragraph [0053]);
determining one or more recipients for the one or more earned points based on one or more user selections from the one or more student program participants associated with the one or more program participants and /or by using the stored data: (see point assignment or allocation process, paragraph [0020]);
assigning the one or more earned points to the one or more determined recipients while the assigned one or more earned points are to be consumed by the one or more determined recipients when faced by one or more crises through help activities performed by other one or more student program participants associated with the one or more program participants, performed by someone on behalf of the one or more student program participants or performed by the both where entitlement to the point consuming activities allowed to match the one or more earned points (see point assignment or allocation process, paragraph [0020]).
The combination of Chang, Trivedi and Bloy does not explicitly disclose enabling the one or more student program participants associated with the one or more program participants to further reassign the assigned received selected one or more point earning activities to one or more of the: (a) one or more relatives associated with the one or more student program participants associated with the one or more program participants, (b) one or more friends of the one or more student program participants associated with the one or more program participants, (c) one or more volunteers not associated with the one or more program participants.
However, Ding discloses a system and method (Method and system for evaluating, rewarding and facilitating philanthropic works) comprising:
enabling the one or more student program participants associated with the one or more program participants to further reassign the assigned received selected one or more point earning activities to one or more of the: (a) one or more relatives associated with the one or more student program participants associated with the one or more program participants, (b) one or more friends of the one or more student program participants associated with the one or more program participants, (c) one or more volunteers not associated with the one or more program participants (see Fig. 8, paragraph [0043]: “FIG. 8 depicts an interface displaying a following dashboard of a user which comprises rewarding accounts summary information and philanthropic work related activities of the user's benefactors, beneficiaries, and other users followed by the user, according to an exemplary embodiment. The interface 800 may be displayed on a user's device after a user logging in, or after a user clicking the People and Organizations button 805 on a navigation bar, scrolling down and selecting Your Following Dashboard. This example following dashboard interface 800 displays the user's benefactors, their rewarding accounts balances and summary information related to the user in section 810, the user's beneficiaries, their rewarding accounts balances and summary information related to the user in section 820, other users that the user is following and their rewarding accounts balances and summary information in section 830, and the philanthropic work related activities of the user's benefactors and beneficiaries, or of those that the user is following, in section 840. The section 810 may include each benefactor's username 812, accounts balances 814, and total amount of rewarding points that the user has contributed to the benefactor 816. The section 820 may include each beneficiary's username 822, accounts balances 824, and total amount of rewarding points that the beneficiary has directly distributed to the user 826, and total amount of rewarding points that has been distributed to the user through the beneficiary 828. The section 830 may include username of each of those that the user is following 832, their accounts balances 835 and accounts summary information based on all their direct beneficiaries 836 and based on all their indirect beneficiaries 837. The activities section 840 may show usernames of the users who conduct the activities, brief description of the activities and the links to the related philanthropic works such as the one depicted in interface 500. The usernames in the interface 800 may be the users' profile links that lead to their profiles as depicted in interface 700. Specifically in this example interface 800, the summary information 816 shows that the user has transferred 5.40 monetary points and 0 time points to its direct benefactor with username shown in 812, the summary information 826 shows that 0 monetary points and 4.75 time points have been transferred to the user by its beneficiary with username shown in 822, and the summary information 828 shows that 0.26 monetary points and 0.06 time points have been indirectly distributed to the user through its beneficiary with username shown in 822. The summary information 835 shows that the user, with username shown in 832, whom the logged in user is following, has transferred 9.02 monetary points and 14.70 time points to its 3 direct benefactors, the summary information 836 shows that 14.42 monetary points and 3.62 time points have been transferred to the user with username shown in 832 by its 3 direct beneficiaries, and the summary information 837 shows that 1.11 monetary points and 1.13 time points have been distributed to the user with username shown in 832 by its 6 second degree beneficiaries. The interface 800 is meant to be illustrative only, as a variety of other information, formats or arrangement are possible. For example, other embodiments may exhibit the benefactor or beneficiary users in graphs, and may include other summary information based on rewarding accounts, or based on relationships of higher degree levels.”);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the ethical accounts and points as disclosed by Ding in the system and method of Chang, Trivedi and Bloy, the motivation being to encourage sustainable philanthropy over time (see Ding, Abstract).
With respect to claims 11 and 18, the combination of Chang, Trivedi, Bloy and Ding teaches all the subject matter of the system and method as described above with respect to claims 10 and 17. Further, Ding discloses a system and method (Method and system for evaluating, rewarding and facilitating philanthropic works) comprising
determining one or more point consuming activities to provide help to the one or more determined recipients when the one or more determined recipients face one or more crises, wherein the help is provided through one or more point earning activities performed by other one or more student program participants associated with the one or more program participants, someone on behalf of the other one or more student program participants associated with the one or more program participants, or both, where entitlement to the help received through the point consuming activities is allowed to match the one or more earned points associated with the one or more determined recipients (see Fig. 4, paragraph [0039]: “FIG. 4 is a flow chart that illustrates a method for determining a reference amount of rewarding points to a benefactor of a philanthropic work and enabling a beneficiary user to verify and reward the benefactor and potential indirect benefactors, according to an exemplary embodiment. The method 400 may be implemented by a networked system 100 described in FIG. 1.);
sending the determined one or more point consuming activities for selection to the one or more determined recipients (see paragraph [0024]: “In one embodiment, a user may submit electronically a finished philanthropic work to the system using a user device. The submission may comprise (1) descriptive information of the philanthropic work such as work title, type, role, amount of money or time contributed by the benefactor(/s), location, time, description, and philanthropic cause(/s) related to the work, etc., (2) evidence of the philanthropic work such as documents, photos, videos, audios, links to them, etc., and (3) publish preference of the philanthropic work indicating whether the user intends to post the work publicly or privately. In some situations, the philanthropic work may be submitted by the benefactor. In other situations, the philanthropic work may be submitted by others. A philanthropic work may be displayed by the system publicly or privately based on the publish preference in the submission and the type of the work. For example, an anonymous money donation may be displayed only to the donor itself as a private record. A philanthropic work done with contribution from volunteers may be displayed publicly so that potential beneficiary users can view and reward the participating volunteers. In one embodiment, money donation made by a user to another user via the system can generate public or private philanthropic work submission automatically by the system, depending on anonymity of the donation. In various embodiments, a published philanthropic work can be viewed, searched, commented, and shared with others by any users. Beneficiary users can acknowledge (i.e., reward) benefactors of a published philanthropic work.”; paragraph [0031]: “In one embodiment, a user may submit electronically a philanthropic work request, such as a volunteer need to recruit volunteers, to the system using a user device. The submission may include the requested work's type, description, work role, starting and ending time, the minimum time duration an applicant needs to work for, the estimated number of applicants needed for the said durations, work location, related philanthropic causes, skills needed, instructions, whether the request is repeating and repeating rules, etc. The requester is allowed to review its submission and decide whether to edit or cancel the request before publishing it.);
receiving one or more selections of one or more point consuming activities which are related to the one or more crises being faced by the one or more determined recipients (see paragraph [0031]: "...Based on the submitted philanthropic work request, the system may generate request time slots automatically and enable a user to search for, apply and un-apply to before being confirmed by the requester, or be matched with specific time slots of a published request. A user is also enabled to view, search, comment, and share with others published philanthropic work requests...");
determining one or more earned points required for the received one or more selections of the one or more point consuming activities which are related to the one or more crises being faced by the one or more determined recipients (see paragraph [0025]: “Based on a published philanthropic work, a beneficiary user may reward benefactors of the philanthropic work by paying out rewards from the user's rewarding accounts.);
executing or scheduling the one or more selections of one or more point consuming activities when there are sufficient one or more earned points available (see paragraph [0032]: “In various embodiments, a philanthropic work requester is enabled to manage the request for coordination and communications such as scheduling, confirming applications, sending messages or cancelling time slots via system provided management dashboard that is configured to simplify the flow of work, offer efficient batch operations and avoid omissions or repetitions.);
generating one or more suggestions for one or more additional point earning activities to earn insufficient one or more earned points for the one or more selections of one or more point consuming activities when there are not sufficient one or more earned points available for the one or more selections of the one or more point consuming activities to provide help to the one or more determined recipients facing one or more crises (see Fig. 13, paragraph [0049]: “FIG. 13 depicts an interface that enables a user to search and (un/)apply to philanthropic work request slots according to an exemplary embodiment. The interface 1300 may be displayed on a user's device after a user clicking the Volunteering button 1310 on a navigation bar and selecting Search and Apply for Local Volunteer Opportunities. This example interface 1300 displays a request search form with a Search button 1329 and a list of philanthropic work request time slots 1330 which is the result of the search. The request search form may include search criteria such as minimum stay duration 1320, starting time 1321, ending time 1322, requester username 1323, request title 1324, description of the request 1325, location of the request 1326, philanthropic cause related to the request 1327, and the requested work role 1328 which is an auto-complete widget that displays dynamic search options as the user types. The result list of request time slots 1330 may include requester 1331 which is the username of the requester linking to the requester's user profile, starting time 1332, ending time 1333, location 1334, request title 1335 which is also a link leading to the detail page of the philanthropic work request post, number of available spots for applicants 1336, and an apply column displaying an Apply button 1337 which allows the user to apply for the request time slot, or displaying an Unapply button 1338 which allows the user to un-apply for an applied request time slot before the requester confirms the application, or displaying a “Confirmed” mark 1339 if an application is confirmed by the requester. The interface 1300 is meant to be illustrative only, as a variety of other search criteria, limits, result information, formats or widgets are possible. For example, in other embodiments, full or legal name of the requester may be included as search criteria, or a user may be allowed to search and apply for philanthropic work requests that are not local. In some embodiments, a map may be used to show the locations of the resulted requests as well as the user, based on the user's profile address or geo-location information collected from the user's device, and distances may be further calculated for the user's reference.”);
sending the generated one or more suggestions for selection of the one or more additional point earning activities to the one or more determined recipients facing one or more crises (see Fig. 14, paragraph [0050]: “FIG. 14 depicts an interface that enables a user to search, view and add to calendar its volunteer applications to philanthropic work request slots according to an exemplary embodiment. The interface 1400 may be displayed on a user's device after a user clicking the Volunteering button 1410 on a navigation bar, scrolling down and selecting Your Volunteer Applications. The interface 1400 may display a search form 1420, a Search button 1429, a list of applications 1430 from the search, and a “Send Confirmed Slots to My Calendar” button 1440. The search form 1420 may include search criteria such as time of the applied request slot, username of the requester, location of the request, title of the request, whether the request is been cancelled by the requester, etc. The resulted application list 1430 may include the starting and ending time of the applied request 1431 and 1432, username of the requester 1433, location of the request 1434, title of the request 1435 which is also a link to the philanthropic work request post, a confirm column 1436 displaying an “Confirmed” mark if an application is confirmed by the requester, and a cancel column 1437 displaying an “Cancelled” mark if a request is cancelled by the requester. By default, if no starting time is given when searching, only those request slots that have not started yet are displayed. The “Send Confirmed Slots to My Calendar” button 1440 allows a user to send system generated calendar file of all the active applied request time slots via email to be imported to the user's personal calendar such as iCalendar, Outlook Calendar, or Google Calendar. The interface 1400 is meant to be illustrative only, as a variety of other search criteria, result information, default conditions, widgets or calendar importing ways are possible. For example, in some embodiments, system generated calendar file of a user's request time slots can be imported to its personal calendar via browser extensions.”);
receiving one or more selections for the one or more additional point earning activities and one or more planned times for conducting the additional point earning activities from the one or more determined recipients facing one or more crises (see Fig. 15, paragraph [0051]: “FIG. 15 depicts an interface displaying an automatically generated philanthropic work submission based on a finished philanthropic work request, according to an exemplary embodiment. The interface 1500 may be displayed on a user's device after a request user clicking the Generate Work Post button 1140 in interface 1100. The interface 1500 may include a form with a Submit button 1580 for submitting philanthropic work, which is automatically filled with information based on a finished philanthropic work request or any of its finished time slots. Some information of the form can be edited and overridden by user's input. This example interface 1500 displays philanthropic work title 1505, publish preference of the work submission 1510, work time 1515, work location 1520, philanthropic causes related to the work 1525, work description 1530, evidence of the work 1535 such as image and file, work type 1540, work role 1545, benefactor of the work 1550, work unit 1555, benefactor's contribution 1560, and participated volunteers section 1570. The participated volunteers section 1570 allows the user to edit actual volunteers for each of the request time slots, and includes each time slot's starting time 1572, ending time 1574, list of confirmed volunteer applicants 1576 which is a widget pre-filled with usernames of confirmed applicants for the request user to accept or select/unselect from in case some of them did not show up, and an input field 1578 for the request user to add usernames of volunteers who actually volunteered for the time slot but are not included in the confirmed volunteer applicants list 1576. Specifically in this example interface 1500, the requester user is adding a participated volunteer who were not confirmed into field 1578. The interface 1500 is meant to be illustrative only, as a variety of other information, fields, pre-filling and overriding conditions, arrangements, formats or widgets are possible.”);
generating one or more reminder notification generation schedules (see paragraph [0032]: “In various embodiments, a philanthropic work requester is enabled to manage the request for coordination and communications such as scheduling, confirming applications, sending messages or cancelling time slots via system provided management dashboard that is configured to simplify the flow of work, offer efficient batch operations and avoid omissions or repetitions. In one embodiment, the management dashboard may include dynamically generated request time slots, buttons, links, user lists and message templates depending on different stages of the request management process and current requesting and applying situations. All the buttons, links and lists are shown in the dashboard only when certain options are available, certain actions are necessary, or certain operations are executable, and are shown in one place, so that the requester can have the picture of the whole while not getting lost in a sea of options...");
assigning and /or scheduling the generated one or more point consumption activities to the one or more determined recipients facing one or more crises (see paragraph [0044]: “FIG. 9A depicts an interface displaying basic information of a user's rewarding accounts according to an exemplary embodiment. The interface 900 may be displayed on a user's device after a user logging in and clicking the Accounts and Profile button 901 on a navigation bar and selecting Your EP Accounts. This example interface 900 displays the basic information of rewarding accounts the user owns, which includes an account that contains rewarding points measured in monetary unit, and an account that contains rewarding points measured in time unit. The basic information of a rewarding account may include account name 921, account balance 922, account unit 923, credit limit of account 924, and a link to the transaction details of the account 925. Depending on how the system has initialized the rewarding accounts of a user, instead of assigning a credit limit, an amount of rewarding points may be added directly to the user's account and highlighted in other embodiments.”).
The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the parent claims above.
With respect to claims 12 and 19, the combination of Chang, Trivedi, Bloy and Ding teaches all the subject matter of the system and method as described above with respect to claims 10 and 17. Furthermore, Chang discloses a system and method wherein the point earning activities performed by the one or more student program participants associated with the one or more program participants, performed by one or more persons on behalf of the one or more student program participants, or performed by both and, for the purpose of providing help to other one or more student program participants, optionally include at least one of: one or more temporary accommodation providing activities; one or more textbook donation activities; one or more food donation activities; one or more sick care activities excluding one or more activities that require involvement of regulated medical professional; one or more non-prescribed medication donation activities; one or more clothes donation activities; one or more transit pass donation activities; one or more course tuition fee donation activities; one or more computing equipment lending activities; one or more emergency travel cost activities; one or more communication cost donation activities (see tasks within the educational pathway, paragraphs [0009]-[0014]; tasks and projects that improve their business, professional and education development, regardless of whether such tasks are mandatory or not, paragraphs [0065] and [0074]).
Response to Arguments/Amendments
Claim rejections - 35 USC § 101
Applicant’s amendments and arguments (see remarks, page 8, filed on 09/29/2025), with respect to the rejection of claims 1-20 under 35 USC § 101 as being directed to an abstract idea have been fully considered but are not persuasive. Examiner respectfully disagrees. The new and amended claims do not offer significantly more than the abstract idea itself, therefore the claims are still rejected under 35 USC § 101 as further detailed above.
Claim rejections - 35 USC § 103
Applicant’s amendments and arguments (see remarks, pages 1-15, filed on 09/29/2025), with respect to the rejection of claims 1-20 under 35 USC § 103 have been fully considered , but are moot because the arguments do not apply to the reference being used in the current rejection of the amended claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDUARDO D CASTILHO whose telephone number is (571)270-1592. The examiner can normally be reached Mon-Fri 8-5.
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/EDUARDO CASTILHO/Primary Examiner, Art Unit 3698
1 Examiner notes functions/steps directed to contingent limitations (i.e. determining, generating, receiving) are not required. The BRI of the claims was adopted for analysis purposes.