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 .
Information Disclosure Statement
The information disclosure statements entered on December 9th, 2025 and January 18th, 2026 have been considered. A copy of the cited statement(s) including the notation indicating its respective consideration is attached for the Applicant's records.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention as a whole, considering all claim elements both individually and in combination, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As summarized in MPEP § 2106, subject matter eligibility is determined based on a Two-Part Analysis for Judicial Exceptions. In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant application includes claims concerning a computer gaming system/ computer system (i.e., a machine) in claims 1-11, and a computer implemented method (i.e., a process) in claims 12-20.
In Prong 1 of Step 2A, it must be determined whether the claimed invention recites an Abstract Idea, Law of Nature or a Natural Phenomenon.
In particular exemplary presented claim 1 includes the following underlined claim elements:
1. A computer gaming system for establishing and achieving objectives, the computer system comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
a memory device storing executable code that, when executed, causes the processor to:
determine that a user has accessed, via a user device, a gaming profile associated with an entity;
transmit to the user, via the user device, a digital communication, wherein the digital communication comprises one or more queries for determining a preferred objective of the user;
receive, from the user device, one or more user inputs in response to the one or more queries;
iteratively train a computer-implemented machine learning model, wherein model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploy the trained model;
predict, using the trained model, based on the one or more inputs, the user’s preferred objective;
determine one or more gaming actions capable of being performed via the user device and associated with the user’s preferred objective that would further the user’s preferred objective;
store the user’s preferred objective in an entity data source, wherein the entity data source stores user data corresponding to a user profile of the user;
determine that the user has accessed, via the user device, the gaming profile of the gaming software application associated with the entity;
monitor the gaming profile of the user for completion of the one or more gaming actions; and
allocate to the gaming profile remuneration in an amount correlated to the user’s preferred objective.
The claim elements underlined above, concern the court enumerated abstract ideas of Mental Processes including observation, evaluation, and judgement because the claims are directed to series of steps for establishing a an objective, tracking the objective, and providing an award based on the completion of the objective as well as Certain Methods of Organizing Human Activity including commercial or legal interactions including sales activities and business relations and managing personal behavior including interactions between people including social activities and following rules or instructions because the claims set forth the interactions involving one or more parties in the context of rules for a game or objective and the awarding of a prize to the user based on the completion thereof.
As the exemplary claim recites an Abstract Idea, Law of Nature or a Natural Phenomenon it is further considered under Prong 2 of Step 2A to determine if the claim recites additional elements that would integrate the judicial exception into a practical application. Wherein the practical applications are set forth by MPEP §2106.05(a-c,e) are broadly directed to: the improvement in technology, use of a particular machine and applying or using the judicial exception in a meaningful way beyond generally linking the use thereof to a technology environment. Limitations that explicitly do not support the integration of the judicial exception in to a practical application are defined by MPEP 2106.05(f-h) and include merely using a computer to implement the abstract idea, insignificant extra solution activity, and generally linking the use of the judicial exception to a particular technology environment or field of use.
With respect to the above the claimed invention is not integrated into a practical application because it does not meet the criteria of MPEP §2106.05(a-c,e) and although it is performed on processor(s), a communication interface, a memory device, and a user device it is not directed to a particular machine because the hardware elements are not linked to a specific device/machine and would reasonably include other network connected devices such as generic computers, smart phones, game consoles, and the like. Accordingly, the claims limitations are not indicative of the integration of the identified judicial exception into a practical application, and the consideration of patent eligibility continues to step 2B.
Step 2B requires that if the claim encompasses a judicially recognized exception, it must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception. The additional element(s) or combination of elements in the claim(s) other than the abstract idea(s) per se including processor(s), a communication interface, a memory device, and a user device amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structures that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry per the applicant’s description (Applicant’s specification Paragraphs [0003], [0064], [0067]-[0068], [0081], [0102]). Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Accordingly, as presented the claimed invention when considered as a whole amounts to the mere instructions to implement an abstract idea [i.e. software or equivalent process steps] on a generic computer [i.e. controller or processor] without causing the improvement of the generic computer or another technology field.
The applicant’s specification is further noted as supporting the above rejection wherein neither the abstract idea nor the associated generic computer structure as claimed are disclosed as improving another technological field, improvements to the function of the computer itself, or meaningfully linking the use of an abstract idea to a particular technological environment (Applicant’s specification Paragraphs [0003], [0064], [0067]-[0068], [0081], [0102]). In particular the applicant’s specification only contains computing elements which are conventional and generally widely known in the field of the invention described, and accordingly their exact nature or type is not necessary for an understanding and use of the invention by a person skilled in the art per the requirements of 37 CFR 1.71. Were these elements of the applicant’s invention to be presented in the future as non-conventional and non-generic involvement of a computing structure, such would stand at odds with the disclosure of the applicant's invention as found in their specification as originally filed.
“[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implemen[t]’ an abstract idea ‘on . . .a computer,’ . . . that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132S. Ct. at 1301). In this case, the claims recite a generic computer implementation of the covered abstract idea.
The remaining presented claims 2-20 incorporate substantially similar abstract concepts as noted with respect to the exemplary claim 1, while the additional elements recited by the additional claims including one or more of processor(s), a communication interface, a memory device, and a user device and a ticket as respectively presented in certain claims that when considered both individually and as a whole in the respective combinations of each of the additional claims are not sufficient to support patent eligibility under prong 2 of step 2A or step 2B because they each present substantially similar abstract concepts as noted with reflection to exemplary claim 1 above and accordingly for the same reasons set forth above with respect to the exemplary claim 1 are similarly directed to or otherwise include abstract ideas.
Therefore, the listed claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Faleti et al (US 2023/0126190) in view of Tuo et al (US 2022/0012643).
Claim 1: The combination of Faleti & Tuo teaches a computer gaming system for establishing and achieving objectives, the computer system comprising:
at least one processor(Faleti Figure 1; Element 86; Paragraph [0018]);
a communication interface communicatively coupled to the at least one processor (Faleti Figure 1; Element 30; Paragraph [0018]); and
a memory device storing executable code (Faleti Figure 1; Element 30; Paragraph [0022]) that, when executed, causes the processor to:
determine that a user has accessed, via a user device, a gaming profile associated with an entity(-Request for access- Faleti Figure 2; Paragraph [0020]);
transmit to the user, via the user device, a digital communication, wherein the digital communication comprises one or more queries for determining a preferred objective of the user (-query and identification of debt vehicle(s) and/or offer of financial investment product- Faleti Figures 2, 3; Paragraphs [0014], [0020]);
receive, from the user device, one or more user inputs in response to the one or more queries (-query and identification of debt vehicle(s) and/or acceptance of offer- Faleti Figures 2, 3; Paragraph [0020]);
iteratively train a computer-implemented machine learning model (Tuo Abstract; Paragraph [0052]), wherein model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable(Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level (-models are trained to a desired accuracy using both iterations and weights and subsequently updated user responsive to user feedback- Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
deploy the trained model (Tuo Abstract; Paragraphs [0019]. [0038]);
predict, using the trained model, based on the one or more inputs, the user’s preferred objective (-query and identification of debt vehicle(s) and accept/rejection response to financial investment product offer- Faleti Figures 2-3; Paragraphs [0014], [0020]-[0021] & Tuo Abstract; Paragraphs [0019]. [0038]);
determine one or more gaming actions capable of being performed via the user device and associated with the user’s preferred objective that would further the user’s preferred objective (-transfer of funds- Faleti Figure 2; Paragraphs [0019] [0020]);
store the user’s preferred objective in an entity data source, wherein the entity data source stores user data corresponding to a user profile of the user (understood as inherent to offer criteria including investment amount and/or identification of debt vehicle(s) that are subsequently paid- Faleti Figure 2; Paragraphs [0020]-[0021]);
determine that the user has accessed, via the user device, the gaming profile of the gaming software application associated with the entity (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]);
monitor the gaming profile of the user for completion of the one or more gaming actions (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]); and
allocate to the gaming profile remuneration in an amount correlated to the user’s preferred objective(-applying he amount to the outstanding balance of the of debt vehicle(s)- Faleti Figure 2; Paragraphs [0019]-[0020], [0025]).
Faleti teaches the invention directed to determining and acting according to a user’s preferred objective and performed actions as presented above. While Faleti is silent regarding the iterative training and deployment of machine learning to determine a user’s objectives, Tuo teaches that this was a known feature of user interfaces in an analogous invention (Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]). It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to have incorporated the iterative training and deployment of machine learning to determine a user’s objectives as taught by Tuo in the invention of Faleti in order to provide the predictable and expected result of enabling the operator understanding of user interests with a machine learning that updates according to ongoing customer interactions and/or provide more efficient user interface.
Claim 2: The combination of Faleti & Tuo teaches the computer gaming system of claim 1, wherein the user’s preferred objective is at least one of saving, purchasing, retiring, increasing credit score, and funding for an emergency (-wherein investment products are a general form of savings and specific examples including 401k, IRAs are retirement savings vehicles- Faleti Paragraph [0014]).
Claim 3: The combination of Faleti & Tuo teaches the computer gaming system of claim 1, wherein the user’s preferred objective comprises a goal that is mutually beneficial to the user and to the entity (-wherein both the financial institution and customer benefit from the arrangement- Faleti Abstract).
Claim 4: The combination of Faleti & Tuo teaches the computer gaming system of claim 3, wherein the mutually beneficial goal is at least one of increasing user savings, increasing user engagement, promoting cross-selling with partner entities, promoting fiscal learning, borrowing facility initiation, and increasing user retention (-wherein the customers savings and commercial engagement increases result of the promotion- Faleti Abstract).
Claim 5: The combination of Faleti & Tuo teaches the computer gaming system of claim 1, wherein the user’s preferred objective comprises a series of steps to complete the one or more gaming actions (-accepting the offer, monitoring and determining when a use has transferred the funds to the financial account- Faleti Figure 2; Paragraph [0019]).
Claim 6: The combination of Faleti & Tuo teaches the computer system of claim 5, wherein based at least in part on monitoring the gaming profile of the user for completion of the one or more gaming actions, the code when executed further causes the processor to:
receive an indication from the user device that the user has completed each of the series of steps (-describing the transfer of funds- Faleti Figure 1; Paragraph [0019]); and
determine that the user has achieved the user’s preferred objective (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Figure 2; Paragraph [0019]).
Claim 7: The combination of Faleti & Tuo teaches a computer system for establishing and achieving objectives via a software application, the system comprising:
at least one processor (Faleti Figure 1; Element 86; Paragraph [0018]);
a communication interface communicatively coupled to the at least one processor (Faleti Figure 1; Element 30; Paragraph [0018]); and
a memory device storing executable code (Faleti Figure 1; Element 30; Paragraph [0022]) that, when executed, causes the processor to:
determine that a user has accessed, via a user device, a gaming profile associated with an entity (-Request for access- Faleti Figure 2; Paragraph [0020]);
transmit to the user, via the user device, a digital communication, wherein the digital communication comprises one or more queries for determining a preferred objective of the user (-query and identification of debt vehicle(s) and/or offer of financial investment product- Faleti Figures 2, 3; Paragraphs [0014], [0020]);
receive, from the user device, one or more user inputs in response to the one or more queries (-query and identification of debt vehicle(s) and/or acceptance of offer- Faleti Figures 2, 3; Paragraph [0020]);
iteratively train a computer-implemented machine learning model comprising an artificial neural network (Tuo Abstract; Paragraphs [0052]-[0053]), wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable (Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level (-models are trained to a desired accuracy using both iterations and weights and subsequently updated user responsive to user feedback- Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
deploy the trained model (Tuo Abstract; Paragraphs [0019]. [0038]);
predict, using the trained model, based on the one or more inputs, the user’s preferred objective, wherein decision tree analysis is utilized to derive the user’s preferred objective (-query and identification of debt vehicle(s) and accept/rejection decision tree response to financial investment product offer- Faleti Figures 2-3; Paragraphs [0014], [0020]-[0021] & Tuo Abstract; Paragraphs [0019]. [0038]);
determine one or more gaming actions capable of being performed via the user device and associated with the user’s preferred objective that would further the user’s preferred objective (-transfer of funds- Faleti Figure 2; Paragraphs [0019] [0020]) ;
store the user’s preferred objective in an entity data source, wherein the entity data source stores user data corresponding to a user profile of the user (understood as inherent to offer criteria including investment amount and/or identification of debt vehicle(s) that are subsequently paid- Faleti Figure 2; Paragraphs [0020]-[0021]);
determine that the user has accessed, via the user device, the gaming profile of the gaming software application associated with the entity (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]);
monitor the gaming profile of the user for completion of the one or more gaming actions, wherein the one or more gaming actions comprises a series of steps to complete the user’s preferred objective (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]); and
allocate to the gaming profile remuneration in an amount correlated to the user’s preferred objective (-applying he amount to the outstanding balance of the of debt vehicle(s)- Faleti Figure 2; Paragraphs [0019]-[0020], [0025]).
Faleti teaches the invention directed to determining and acting according to a user’s preferred objective and performed actions as presented above. While Faleti is silent regarding the iterative training and deployment of machine learning to determine a user’s objectives, Tuo teaches that this was a known feature of user interfaces in an analogous invention (Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]). It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to have incorporated the iterative training and deployment of machine learning to determine a user’s objectives as taught by Tuo in the invention of Faleti in order to provide the predictable and expected result of enabling the operator understanding of user interests with a machine learning that updates according to ongoing customer interactions and/or provide more efficient user interface.
Claim 8: The combination of Faleti & Tuo teaches the computer system of claim 7, wherein the digital communication is at least one of a query, question, inquiry, and survey (-query and identification of debt vehicle(s) and/or acceptance of offer- Faleti Figures 2, 3; Paragraph [0020]).
Claim 9: The combination of Faleti & Tuo teaches the computer system of claim 7, wherein the decision tree analysis is configured to prioritize a preferred objective of the user that furthers one or more objectives of the entity (-wherein both the financial institution and customer benefit from the offered arrangement- Faleti Abstract).
Claim 10: The combination of Faleti & Tuo teaches the computer system of claim 9, wherein the one or more objectives of the entity is at least one of increasing user savings, increasing user engagement, promoting cross-selling with partner entities, promoting fiscal learning, borrowing facility initiation, and increasing user retention (-wherein the customers savings and commercial engagement increases result of the promotion- Faleti Abstract).
Claim 11: The combination of Faleti & Tuo teaches the computer system of claim 7, wherein the series of steps to complete the user’s preferred objective comprises a first step and a second step, wherein the second step has a higher remuneration amount than the first step (-Wherein the user must accept the offer as the first step and transfer the funds as a second step- Faleti Figure 2; Paragraph [0019]).
Claim 12: The combination of Faleti & Tuo teaches a computer-implemented method for establishing and achieving objectives via a gaming software application, the method comprising:
determining that a user has accessed, via a user device, a gaming profile associated with an entity (-Request for access- Faleti Figure 2; Paragraph [0020]);
transmitting to the user, via the user device, a digital communication, wherein the digital communication comprises one or more queries for determining a preferred objective of the user (-query and identification of debt vehicle(s) and/or offer of financial investment product- Faleti Figures 2, 3; Paragraphs [0014], [0020]);
receiving, from the user device, one or more user inputs in response to the one or more queries (-query and identification of debt vehicle(s) and/or acceptance of offer- Faleti Figures 2, 3; Paragraph [0020]);
iteratively training a computer-implemented machine learning model comprising an artificial neural network (Tuo Abstract; Paragraph [0052]-[0053]), wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable (Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level (-models are trained to a desired accuracy using both iterations and weights and subsequently updated user responsive to user feedback- Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]);
deploying the trained model (Tuo Abstract; Paragraphs [0019]. [0038]);
predicting, using the trained model, based on the one or more inputs, the user’s preferred objective (-query and identification of debt vehicle(s) and accept/rejection response to financial investment product offer- Faleti Figures 2-3; Paragraphs [0014], [0020]-[0021] & Tuo Abstract; Paragraphs [0019]. [0038]);
determining one or more gaming actions capable of being performed via the user device and associated with the user’s preferred objective that would further the user’s preferred objective (-transfer of funds- Faleti Figure 2; Paragraphs [0019] [0020]);
storing the user’s preferred objective in an entity data source, wherein the entity data source stores user data corresponding to a user profile of the user (understood as inherent to offer criteria including investment amount and/or identification of debt vehicle(s) that are subsequently paid- Faleti Figure 2; Paragraphs [0020]-[0021]);
determining that the user has accessed, via the user device, the gaming profile of the gaming software application associated with the entity (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]);
monitoring the gaming profile of the user for completion of the one or more gaming actions (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Paragraph [0019]); and
allocating to the gaming profile remuneration in an amount correlated to the user’s preferred objective (-applying he amount to the outstanding balance of the of debt vehicle(s)- Faleti Figure 2; Paragraphs [0019]-[0020], [0025]).
Faleti teaches the invention directed to determining and acting according to a user’s preferred objective and performed actions as presented above. While Faleti is silent regarding the iterative training and deployment of machine learning to determine a user’s objectives, Tuo teaches that this was a known feature of user interfaces in an analogous invention (Tuo Abstract; Paragraphs [0019]. [0038], [0052], [0061]). It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to have incorporated the iterative training and deployment of machine learning to determine a user’s objectives as taught by Tuo in the invention of Faleti in order to provide the predictable and expected result of enabling the operator understanding of user interests with a machine learning that updates according to ongoing customer interactions and/or provide more efficient user interface.
Claim 13: The combination of Faleti & Tuo teaches the method of claim 12, wherein the user’s preferred objective is at least one of saving, purchasing, retiring, increasing credit score, and funding for an emergency (-wherein investment products are a general form of savings and specific examples including 401k, IRAs are retirement savings vehicles- Faleti Paragraph [0014]).
Claim 14: The combination of Faleti & Tuo teaches the method of claim 12, wherein the user’s preferred objective comprises a goal that is mutually beneficial to the user and to the entity (-wherein both the financial institution and customer benefit from the arrangement- Faleti Abstract).
Claim 15: The combination of Faleti & Tuo teaches the method of claim 14, wherein the mutually beneficial goal is at least one of increasing user savings, increasing user engagement, promoting cross-selling with partner entities, promoting fiscal learning, borrowing facility initiation, and increasing user retention (-wherein the customers savings and commercial engagement increases result of the promotion- Faleti Abstract).
Claim 16: The combination of Faleti & Tuo teaches the method of claim 12, wherein the user’s preferred objective comprises a series of steps to complete the one or more gaming actions (-accepting the offer, monitoring and determining when a use has transferred the funds to the financial account- Faleti Figure 2; Paragraph [0019]).
Claim 17: The combination of Faleti & Tuo teaches the method of claim 16, wherein based at least in part on monitoring the gaming profile of the user for completion of the one or more gaming actions, the method further comprises:
receiving an indication from the user device that the user has completed each of the series of steps (-describing the transfer of funds- Faleti Figure 1; Paragraph [0019]); and
determining that the user has achieved the user’s preferred objective (-monitoring and determining when a use has transferred the funds to the financial account- Faleti Figure 2; Paragraph [0019]).
Claim 18: The combination of Faleti & Tuo teaches the method of claim 16, wherein the digital communication is at least one of a query, question, inquiry, and survey (-query and identification of debt vehicle(s) and/or acceptance of offer- Faleti Figures 2, 3; Paragraph [0020]).
Claim 19: The combination of Faleti & Tuo teaches the method of claim 16, wherein the deriving of the user’s preferred objective comprises prioritizing a preferred objective of the user that furthers one or more objectives of the entity (-wherein both the financial institution and customer benefit from the offered arrangement- Faleti Abstract).
Claim 20: The combination of Faleti & Tuo teaches the method of claim 19, wherein the one or more objectives of the entity is at least one of increasing user savings, increasing user engagement, promoting cross-selling with partner entities, promoting fiscal learning, borrowing facility initiation, and increasing user retention (-wherein the customers savings and commercial engagement increases result of the promotion- Faleti Abstract).
Response to Arguments
Applicant's arguments filed November 28th, 2025 have been fully considered but they are not persuasive.
Commencing on pages 9-12, section (2) of the applicant above dated remarks, the Applicant presents that the rejection of claims under 35 USC §101 has been overcome based on the incorporation of steps describing training a computer-implemented machine learning model as particularly presented in pending independent claims 1, 7, and 12. Specifically the applicant proposes that the incorporation of these features reflect a claimed invention analogous to the USPTO published example 39 that was identified as not being directed to one or more of the enumerated grouping of abstract ideas.
Reflecting the consideration of the Applicant presented amendments and Applicant arguments reflecting the same, the rejection of claims as being directed to a judicial exception without significantly more respectfully maintained because the recited inclusion of computer-implemented machine learning model is recited at a high level of generality that does not explain or describe what the target variable is, what criteria is utilized ascertain the accuracy of the predicted target variable is at an acceptable level, and/or how the weighing of the iterations is determined. Considering both the language of the presented amendment as well as the remaining elements of the claimed invention both individually and as a whole the claimed invention is understood to be more closely analogous to non-eligible claim 2 of USPTO published Example 47 rather than applicant cited USPTO published example 39. The rejection under this section is accordingly respectfully maintained as presented herein above.
Continuing on pages 12-14, section (2) of the applicant above dated remarks, the Applicant presents that the rejection of claims under 35 USC §102(a)(1-2) as being anticipated by Faleti et al (US 2023/0126190) has been overcome based on the amended incorporation of features broadly directed the use of machine learning in determining the user’s preferences.
Responsive to the consideration of the Applicant presented amendments, and an updated prior art search reflecting the same, the newly discovered prior art of Tuo et al (US 2022/0012643) has been discovered and has been determined to be relevant to the claimed invention when considered in combination of with the previously applied prior art of Tuo et al (US 2022/0012643). Accordingly, claims 1-20 are now are rejected under 35 U.S.C. 103 as being unpatentable over Faleti et al (US 2023/0126190) in view of Tuo et al (US 2022/0012643) as presented herein above.
In view of the preceding the rejection of claims is respectfully maintained as presented herein above.
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 ROBERT E MOSSER whose telephone number is (571)272-4451. The examiner can normally be reached M-F 6:45-3:45.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Lewis can be reached at 571-272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ROBERT E. MOSSER
Primary Examiner
Art Unit 3715
/ROBERT E MOSSER/Primary Examiner, Art Unit 3715