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 .
Status of Claims
This action is in reply to the amendments and remarks filed on October 10, 2025.
Claims 1-4, 6-14, and 16-20 are currently pending and have been examined.
Claims 1 and 11 are currently amended.
Claims 5 and 15 have been withdrawn. Subsequently, the limitations from these claims have been rolled up into claims 1 and 11 respectively.
Response to Arguments
35 U.S.C. § 103 Arguments
Applicant’s arguments regarding 35 U.S.C. § 103 for claims 1-4, 6-14, and 16-20 dated 10 October 2025 have been fully considered and are not persuasive. Therefore, the rejection has been maintained.
In the Applicant’s remarks, pages 6-7, the Applicant argues that the Office fails to assert that Deshmukh or Shoham, either individually, or cooperatively disclose the amended limitations in claims 1 and 11, and therefore the claims 1 and 11 are patentably distinguishable over the prior art. Further, the Applicant assert’s that Shoham does not teach, suggest, nor motivate the amended limitations for these claims.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In response to applicant’s argument that that Deshmukh does not disclose the limitations, and Shoham does not cure the deficiencies, the Examiner respectfully disagrees. The Office asserts, in the amended rejection below, that the Deshmukh discloses these features in ¶’s [0005], [0007], [0018-0019], [0021], where the new limitations in claims 1 and 11 incorporate limitations from withdrawn claims 5 and 15, i.e. the account profile comprises at least an element behavior data, and from the specification, an account classifier is used to identify behavior data. In the amended rejection, Shoham is no longer germane to the argument regarding patentable distinguishability, as Deshmukh discloses all of the elements under scrutiny.
With regards to the Applicant’s arguments that Shoham does not teach, suggest, nor motivate the amended limitations, the Examiner respectfully disagrees. This particular argument is Moot for two reasons. First, because the question of teaching, suggestion, and motivation is germane only to a combination of references, not to a reference individually. Second, because Deshmukh discloses each limitation presented in the Applicant’s arguments according to the updated 35 U.S.C. § 103 rejection below, on its own, resulting in Shoham no longer being relevant to the prior art rejection for the limitations presented in the Applicant’s arguments, though Shoham is relevant to claim 1 for limitations of training the model as presented below.
Further, on page 7, the Applicant argues that dependent claims, 2-4, 6-10, 12-14, and 16-20 are patentably distinguishable over the prior art due to the Applicant’s assertions claims 1 and 11 are patentably distinct. The Examiner respectfully disagrees.
Again, Applicant's arguments fail to comply with 37 CFR 1.111(b) as presented above. The updated rejection for claims 1 and 11 does not show a patentable distinction over the prior art, therefore, the dependent claims inherit the independent claim rejections.
Please find the updated 35 U.S.C. § 103 rejection for claims 1-4, 6-14, and 16-20, below.
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 is directed to an abstract idea without significantly more.
Independent Claims:
Regarding Claims 1 and 11: The claims recite the following functions: receive an inquiry, identify data, calculate a profile, identify user behavior, receive data, generate a metric, and determine a response to inquiry. These are abstract ideas in the category of "Certain Methods of Organizing Human Activity,” more specifically, fundamental economic principles or practices, i.e. mitigating risk, since the hazard data is disclosed in the specification as ¶ [0004] as “a user’s predisposition to risk based on a user’s biological extractions.” Further, the claims utilize the user’s behavior and biological extractions to drive decisions regarding account inquiries in at least banking and lending, also reciting another abstract idea category of "Certain Methods of Organizing Human Activity,” more specifically “commercial or legal interactions” for business relations (MPEP 2105.04(a)(2)(II)).
Step 2A Prong 2: The claims recite characterized data or groups of data that are non-functional descriptive information limitations. These data limitations do not carry patentable weight in the claim, nor are they limitations that can be relied on to integrate the abstract ideas into a practical application because they do not positively recite any additional functions that limit the claims or the structures of the claims.
The claims recite the following additional elements: in claim 1: A system; in claims 1 and 11: a computer device and a remote device. The claims are simply reciting generic computing structures at a high level of generality without providing advances or improvements to the technology or structures themselves. These recitations amount to “apply it,” mere instructions to apply the Judicial Exceptions on generic computing structures (MPEP 2106.05(f)).
The claims recite the following additional elements: an artificial intelligence, an account classifier (i.e. the instant specification ¶ [0062] discloses the classifier as a machine learning model), an account machine learning model, and a trained machine learning model. The claims recite “utilizing” a generic artificial intelligence and a generic classifier model, such that the claims are merely applying these elements as tools to perform the abstract ideas. The recited generic machine learning model is trained using data specific to this application, to achieve a generic trained machine learning model, which generates a metric used to determine an inquiry response, i.e. the model receives data and returns reorganized data. The models are recited at a high level of generality. The specification does not reveal advances to implementing, training, utilizing, or applying generic machine learning models or generic trained machine learning models, where model is not patentably distinct, i.e. any model = may be implemented, trained, utilized, or applied, such that the use of particularly characterized data in performing the functions merely returns iteratively determined recommendations of best fit data particularly characterized from collected and processed data as a response. The specification discloses that the machine learning models and algorithms are generic, i.e. not patentably distinct, in ¶ [0062] “a machine-learning model, such as a mathematical model, neural net, or program generated by a machine-learning algorithm known as a classification algorithm, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith.” The AI, the generated, trained, and implemented machine learning model, and the classifier model, while differentiated in the claims and the specification by name, could reasonably perform the same functions or even be the same models and algorithms.
Further, the claims merely recite intended results, i.e. they recite broad functions without specifying how the models receive, identify, calculate, utilize, generate, receive, or corelate data, or how the models are iteratively trained to achieve the trained model, used to generate the metric, or used to determine the response to inquiry. These recitations amount to “apply it,” mere instructions to apply the abstract idea, utilizing generic artificial intelligence, classifier machine learning models, machine learning models, and trained machine learning models as tools to perform the abstract ideas. While the technology, when implemented, increases speed and efficiency of the computer, the models and AI are merely computer automations of statistical methods that are inherently iterated using metrics or weighting, until the models converge on a best solution to a problem, i.e. these tools are merely automating historically human activities via a faster and more efficient method/system. The abstract idea category "Certain Methods of Organizing Human Activity" is germane to the functions of the claims (MPEP 2106.05(f)).
The claims recite receiving an inquiry and receiving training data. The specification does not disclose and the claims to not recite advances to sending or receiving data. The specification does not disclose advances to the computing structures, the algorithms, classifier, machine learning models, AI, data science, Networks, banking security or risk analysis.
These functions are recited at a high level of generality, they are simply attempting to limit the use of the Judicial Exception to the technological field of banking, loans, finance, contracts, money, and finance, where the claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond basic data manipulation, where generally linking the Judicial Exception to the technological field does not permit for drafting efforts designed to monopolize the exception (MPEP 2016.05(h)).
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application (MPEP 2106.07(a)).
Step 2B: The analysis above for Step 2A is commensurate with the analysis for this Step 2B, such that the claims do not include additional elements that are sufficient to amount to significantly more when taken individually and in combination. The claimed elements do not result in the claims, as a whole, amounting to significantly more than the judicial exception (MPEP 2106.05).
Dependent Claims:
Regarding claims 2 and 12: The claims merely append the abstract ideas of the independent claims, adding the following limitation: identify a user and account operation, which is an additional abstract idea in one of the categories of the independent claims, in the category of "Certain Methods of Organizing Human Activity" more specifically “commercial or legal interactions” for business relations because the inquiry is received from a third party and identifies a user and account operations such that the system/method returns specific data about a user, a process that historically was performed via a human (MPEP 2105.04(a)(2)(II)).
The additional elements are the same elements inherited from claims 1 and 11: i.e. in claims 1, 2, and 11: a system; and in claims 1 and 11: a computer device and a remote device, and an artificial intelligence, an account classifier (i.e. the instant specification ¶ [0062] discloses the classifier as a machine learning model), an account machine learning model, and a trained machine learning model which are merely general purpose computing structures applied as tools to implement the abstract idea. Thus, for the same reasons disclosed in analyses for the independent claims, they are also not indicative of integration of the abstract idea into a practical application or enough to amount to significantly more than the abstract idea.
Regarding claims 6 and 16: The claims merely append the abstract ideas of the independent claims, adding the following limitation: authenticate the account inquiry, which is an additional abstract idea in the same categories of the independent claims in the category of "Certain Methods of Organizing Human Activity" in the subcategories of both fundamental economic principles or practices due to mitigating risk, and “commercial or legal interactions” for business relations because the inquiry is received from a third party and identifies the authenticity of the account inquiry, a process that historically a human activity (MPEP 2105.04(a)(2)(II)). The additional element in the claims are merely the same elements as recited in claims 1 and 11, disclosed above. Thus, for the same reasons disclosed in analyses for the independent claims, they are also not indicative of integration of the abstract idea into a practical application or enough to amount to significantly more than the abstract idea.
Regarding claims 9 and 19: The claims merely append the abstract ideas of the independent claims, but do not recite an abstract idea themselves. The claims recite appending the generating function, defining further limitations that are utilized along with the training data, i.e. incorporating the use of a first machine learning algorithm, utilized in addition to the previously claim recitation of account training data, to perform the generating. The additional elements, in addition to the additional elements analyzed for the independent claims, are the machine learning model and the algorithm. Since the claims do not recite an abstract idea, the additional elements are not indicative of a practical application of an abstract idea, nor enough to amount to significantly more. Further, the new element, the algorithm, is not indicative of a practical application of the abstract ideas in the independent claims, nor enough to amount to significantly more than the abstract ideas of the independent claims.
The dependent claims 3-4, 10, 13-14, and 20 are merely further reciting data characterizations to be included in the element from the highlighted limitations. In claims 3 and 13, data is added to the account operation identified in claims 2 and 12. In claims 4 and 14, further data is added to the biological extraction data. In claims 7 and 17 further data is added to the calculated values, i.e. score data appends the calculated user account profile. Claims 8 and 18 further add an account history factor to the user account score. Claims 10 and 20 further append the training data to include the previously recited account profiles, but append them to also be comprised of correlated account metrics. The claim limitations are not positively recited as performing any functions; therefore, these claims do not recite an abstract idea. Since there are no abstract ideas, the claims cannot be integrated into a practical application or amount to significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-14, 16-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Deshmukh, US20140122335A1, in view of Shoham, US20040177030A1.
Regarding Claims 1 and 11. Deshmukh teaches:
receive, from a remote device operated by a third party, an account inquiry [0014-0015] (receive a query regarding a user account from a subscriber, i.e. a third party, remotely over a network), [0028] (receive a request for purchase or expense authorization);
identify a biological extraction related to the particular user; [0005, 0007, 0014-0015,0021, 0025, and 0029] (identify various biological/physiological data of the user);
calculate a user account profile utilizing the user biological extraction, wherein the user account profile comprises at least an element of user hazard data and an element of user behavior data, wherein in hazard data describes a user's predisposition to monetary risk based on a user's biological extraction, and wherein the computing device identifies the elements of user behavior data utilizing an account classifier; [0005] “include a consumer's real-time physical and emotional attributes,” (i.e. biological extractions), [0007] (determine clearance or approval odds before finalizing the transaction, i.e. at least an element of hazard data or disposition to risk prior to action), [0018-0019] (an evaluator, which is synonymous with a generic classifier model, determines consumer behaviors from the available consumer data attributes, i.e. collect, monitor and analyze user financial data, i.e. user behaviors, and disposition to monetary risk, i.e. hazard data, cumulatively, for each consumer’s individually, e.g. the prior art calculates an account profile including behavior data utilizing a classifier and hazard data), [0021] (data includes biological extractions, i.e. physiological, data in addition to the hazard data);
receiving account training data, wherein the account training data of correlates a plurality of account profiles to a plurality of correlated account metrics; [0025] “some or more of these metrics and statistics may be used as inputs to a probabilistic and statistical model” (where metrics and statistics are synonymous with account profiles and account metrics), [0019] (account profiles include metrics of financial health of each consumer), (Examiner note: the instant application specification discloses in ¶ [0078] that an account metric “is any textual, pictorial, and/or character data that reflects the monetary well-being and/or monetary stability of a particular user”), [0041] (a, an, the refer to both the singular and the plural, wherein an account profile or metric could also represent a plurality of profiles, metrics, or any other term recited using these characters);
and training, iteratively, the account machine learning model using the training data; a [0029] (metrics and statistics, e.g. user profile and behavior data, are adjusted through assignment of statistical or probabilistic weights, where the statistical model adjusts the metrics functionally and iteratively, i.e. iterative training of the machine learning model);
generating the account metric as a function of the user account profile using the trained account machine-learning model; and [0028] and [Figure 1, item 120] “a real-time state of the subscriber may be determined … informed by real-time metrics 124 and expense/purchase value metrics 122,” (user profile data and user hazard data are functionally utilized to determine the account metric), [0029] (expense and value metrics are measured together to determine real-time state of the user, i.e. measuring the metrics and profiles together as a function of the each other, where the statistical model adjusts the metrics functionally and iteratively using the model trained on the data to produce a state of the user and value of the opportunity, i.e. the account metric);
determine a response to the account inquiry utilizing the account metric; [0025] “some or more of these metrics and statistics may be used as inputs to a probabilistic and statistical model that can generate real-time offers … schemes … in the context of the subscriber’s [data]” (Examiner note: it would have been obvious and reasonable for a person having ordinary skill in the art to have utilized the generic machine learning model, i.e. probabilistic and statistical model, fed with user metrics and statistics correlated together to identify the state of the user, e.g. behaviors and profiles correlated together, to determine a response to the inquiry; the determined response, in the instant application, could reasonably be interpreted to be synonymous with the generated “real-time offers … schemes … in the context of the subscriber’s [data]”), [0029-0031] (Utilize the generated personal criteria, synonymous to the account metric, to determine a response to the account inquiry like an authorization or denial of a transaction, utilizing at least one evaluator, i.e. a machine learning model).
Where Deshmukh does not disclose, Shoham teaches: generate an account machine-learning model, which comprises: [0049] (generate a machine learning model for credit scoring)
and training, iteratively, the account machine learning model using the training data; [0040] “the statistical model is trained iteratively;” [0038] (iterations are necessary to build a robust machine learning model).
It would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the improvement of Shoham to the known method and system of the base disclosure of Deshmukh. While Deshmukh discloses the majority of claim limitations, Shoham supplements the disclosure of Deshmukh with the improvements that expand on the machine learning model generation and training using techniques known and applicable to the base disclosure that may have been present, but not disclosed with the detail required to show obviousness. One of ordinary skill in the art would have recognized that applying the known techniques would have yielded predictable results in an improved combined disclosure.
Claims 2 and 12. Deshmukh discloses and Shoham teaches: The system of claim 1,
Deshmukh discloses: wherein the account inquiry identifies a particular user and an account operation related to the particular user. [0038] (a user attempts to make a purchase at a retailer, the user’s credit card is run by the retailer and the user purchase is denied by the model in the disclosure because the user has high debt on the card, a low banking balance, and will not be paid for 5 days, i.e. the attempted credit card use creates an account inquiry and an account operation directly linked to the particular user).
Claims 3 and 13. Deshmukh discloses and Shoham teaches: The system of claim 2,
Deshmukh discloses: wherein the account operation includes a current proposed monetary agreement between the particular user and the third party [0040] (a user attempts to make a purchase at a retailer using a credit card, where the retailer swipes the card. The user purchase is denied by the model in the disclosure because, even though the user has no debt on the card, a high banking balance, and has a payday arriving soon, the consumer is in a state of hunger, i.e. the credit card use creates an account inquiry and an account operation directly linked to the particular user “based on the real-time emotional and physical metrics reported … and may suggest an alternative for a healthy meal at a local fast food outlet.”)
Claims 4 and 14. Deshmukh discloses and Shoham teaches: The system of claim 1,
Deshmukh discloses: wherein the biological extraction further comprises at least an element of user physiological data; [Figure 1, item 124] (real time metrics of the user such as physiological condition of the user), [0014,0015, 0021, 0025, 0026, and 0029] (obtain biological/physiological data of the user).
Claims 6 and 16. Deshmukh discloses and Shoham teaches: The system of claim 1,
Deshmukh discloses: wherein the computing device is further configured to authenticate the account inquiry; [0029] and [0032] (send an authorization or partial authorization for the inquiry based on the analysis).
Claims 7 and 17. Deshmukh discloses and Shoham teaches: The system of claim 1,
Deshmukh discloses: wherein the computing device is further configured to calculate the user account profile: [0018-0019] (an evaluator, which is synonymous with a generic classifier model, determines consumer behaviors from the available consumer data attributes, [0025], [0026, 0028, and 0031] (determine a user profile including physiological data, where many metrics are based on numerical score data).
Where Deshmukh does not disclose, Shoham teaches: wherein the computing device is further configured to calculate the user account profile to contain a user account score; [0056] (determine a traditional credit score, determine a psychometric interview score, and combine the scores using score fusion process to attain a user score);
It would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the improvement of Shoham to the known method and system of the base disclosure of Deshmukh. While Deshmukh discloses the majority of claim limitations, Shoham supplements the disclosure of Deshmukh with the improvements that expand on the machine learning model generation and training using techniques known and applicable to the base disclosure that may have been present, but not disclosed with the detail required to show obviousness. One of ordinary skill in the art would have recognized that applying the known techniques would have yielded predictable results in an improved combined disclosure.
Claims 8 and 18. Deshmukh discloses and Shoham teaches: The system of claim 7,
Deshmukh discloses: an account history factor. [0029] (previous transaction history is incorporated into the user’s profile).
Where Deshmukh does not disclose, but Shoham teaches: wherein the user account score further comprises at least an account history factor. [0029] (previous transaction history is used to train the model that subsequently generates the predictive model to determine the user’s credit score).
It would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the improvement of Shoham to the known method and system of the base disclosure of Deshmukh. While Deshmukh discloses the majority of claim limitations, Shoham supplements the disclosure of Deshmukh with the improvements that expand on the machine learning model generation and training using techniques known and applicable to the base disclosure that may have been present, but not disclosed with the detail required to show obviousness. One of ordinary skill in the art would have recognized that applying the known techniques would have yielded predictable results in an improved combined disclosure.
Claims 9 and 19. Deshmukh discloses and Shoham teaches: The system of claim 1,
Where Deshmukh does not disclose, but Shoham teaches: wherein the computing device is further configured to generate the account machine-learning model utilizing account training data and a first machine-learning algorithm. [Abstract] and [Figure 1 feature 109], [Figure 2 feature 208] and [0027, 0029, 0040, 0045, 0047, 0409, 0050, and 0065] (training and retraining the predictive model with the account metrics), [0026] (numerous algorithms and machine learning models including neural networks, algorithms are collectively referred to as machine learning techniques, i.e. the algorithm and machine learning model are synonymous).
It would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the improvement of Shoham to the known method and system of the base disclosure of Deshmukh. While Deshmukh discloses the majority of claim limitations, Shoham supplements the disclosure of Deshmukh with the improvements and applicable to the base disclosure that may have been present, but not disclosed with the detail required to show obviousness. One of ordinary skill in the art would have recognized that applying the known techniques would have yielded predictable results in an improved combined disclosure.
Claims 10 and 20. Deshmukh discloses and Shoham teaches: The system of claim 9,
Deshmukh discloses: wherein the account training data comprises a plurality of correlated account metrics. [0025] “some or more of these metrics and statistics may be used as inputs to a probabilistic and statistical model” (where metrics and statistics are synonymous with account profiles and account metrics), [0019] (account profiles include metrics of financial health of each consumer), (Examiner note: the instant application specification discloses in ¶ [0078] that an account metric “is any textual, pictorial, and/or character data that reflects the monetary well-being and/or monetary stability of a particular user”), [0041] (a, an, the refer to both the singular and the plural, wherein an account profile or metric could also represent a plurality of profiles, metrics, or any other term recited using these characters);
Conclusion
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/ANGELA HATCH/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626