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 18 May 2026.
Claims 1-4, 6-9, 11-14, and 16-19 are currently pending and have been examined.
Claims 1 and 11 are currently amended.
Claims 10 and 20 have been canceled.
This action is made FINAL.
Response to Arguments
35 U.S.C. § 101 Arguments
Applicant’s arguments regarding 35 U.S.C. § 101 for claims 1-4, 6-9, 11-14, and 16-19 dated 18 May 2026, have been fully considered and are not persuasive. Therefore, the rejection has been maintained.
With regards to the applicants’ argument, the assertion that amended claims 1 and 11 are patentable in view of both Step 2A and Step 2B of the 35 U.S.C. § 101 analysis, are not persuasive. In the arguments, the applicants’ assertion that the 35 U.S.C. § 101 analysis requires the finding of judicial exceptions, then a finding that the exception is either integrated or not integrated into a practical application. While this is not completely incorrect, there is a missing element that is a core set of findings, i.e. one must also identify any additional elements that are not part of the abstract ideas, i.e. the elements, limitations, features, functions, that are not abstract ideas. The applicant does not present what they have determined are the abstract ideas or the additional elements, nor the analyses required in Step 2A Prong 2, to determine if the additional elements, are indicative of integration into a practical application. Instead, the applicant asserts, on page 3, that the claims do not recite the abstract ideas, but instead recite a computing structure that receives data that is comprised of non-functional descriptive information. Sending and receiving data are not abstract ideas, as disclosed in the full analyses below. The root of the information stored in that descriptive information is based around a person’s financial behaviors, further based on biological signals from body fluids or measurement metrics, i.e. data that is merely received and characterized with the biological data. The data is not actively collected in the claims, but is merely received. The data used for training is comprised of correlations between hazard labels and biological metrics, such that the model returns predictions based on learned correlations, that is, the models are merely applied as a tool to receive characterized data sets, then receive a particular data and returning a correlation data based on the training data.
On page 4, the applicants’ arguments, asserting that characterizing the purpose or application as an abstract economic process is not appropriate because it is a technical process, are not persuasive. The claims do recite risk mitigation schemes, where the specification clearly discloses that the bank owned instant application is implemented to authenticate users and then make credit and financing decisions, such that biological signals return correlated risk hazard data based on such correlations. Both of these are methods of mitigating risk in a financial situation and that is clearly disclosed on the defining document, i.e. the instant specification. The risk and decisions are used to identify if/when the users’ creditworthiness correlates with a financial instrument decision to receive financial instruments. The risk mitigation is specifically identified in the specification [0013], devoted to user authentication processes to mitigate said risk of false actors and fraud.” The specification discloses creditworthiness in at least, [0093] discloses an algorithm that receives biological data and assigns a credit worthiness profile based on said biological data along with personal information. The claims are merely performing the task a loan officer would have historically performed, with an added depth of using received, existing biological data, i.e. a mental process. Therefore, upon further examination, the Examiner continues to assert the previous findings of abstract ideas, and adds the assertion of mental processes.
The claims explicitly recite, and the applicant asserts in the arguments that the claim language recited hazard labels that “describe a predisposition to monetary risk based on biological extraction.” Said predisposition to monetary risk are further defined in the specification. While the claims do not recite how the predisposition of monetary risk calculation is performed, the instant specification does define said predisposition of monetary risk in at least [0059] and [0066], but further defines monetary risk and risk in terms of calculating a user account profile in at least [0059-0061], [0063], [0066], [0078-0080], and [0094]. These disclosure ¶’s define the terms of monetary risk, credit risk, risk assessments of awarding credit accounts for debt, lending, risk for lenders in lending money, all based on how said biological data reflects on the hazard labels, which are implemented as training data correlated to the biological data, used to perform the calculation of the user account profile. Therefore, based on the full 35 U.S.C. § 101 rejection below, and the assertions above, the applicant does not present evidence to support their assertions, such that the opinion based commentary and baseless conclusions are not enough to support the assertions on their own.
The applicants’ arguments, on pages 4-5, asserting that the multi-step computational pipeline is a technical process that is directed to a technical improvement in machine learning based account assessment, are not persuasive. While the examples are implemented to assist the Examiner’s in making decisions, they are not the driving force. That is, the Applicants’ assertions that the claims relate to examples 47, claim 3, and Example 48, are not probative. With regards to Example 47, claim 3, the claims implement a specific algorithm, where the instant claims recite a general-purpose characterized model. The instant claims and Example 47, claim 3 are not alike at all. The applicants’ assertions fail to identify any elements as abstract ideas, or any elements not comprised within the abstract ideas as additional elements. Therefore, the assertion that the claims are techencial improvement are not supported by the analyses, such that no additional element is designated as the limitation/s that are indicative of such an integration through technical improvement. In fact, this applicant asserted “improvement to physiologically informed account assessment” is not attributed to any additional elements at all. Therefore, the lack of analyses with abstract ideas and additional elements, does not support the assertion that the claims improve upon the functioning of a computer, or to any other technology or technical field, as required by MPEP 2106.05(a). Therefore, there is no improvement and not integration based on Example 47. Example 48, claim 2 is also not relatable to the instant claims as asserted by the applicant because neither the machine learning models nor the computing structures, i.e. the additional elements, are indicative of integration of the abstract ideas into a practical application.
The applicants’ arguments on pages 7-9, with regards to MPEP 2106.05(d) and Berkheimer, are Moot, because there were no additional elements that were identified in the amended claims, as well-understood, routine, and conventional (WURC). Since there were no limitations identified under MPEP 2106.05(d), the Berkheimer analyses are not required to be performed. The applicants’ arguments, that the Examiner did not assert any limitations as well-understood, routine, and conventional (WURC), such that it poses a factual question as to why the Examiner failed to establish clear and convincing evidence, as required under Berkheimer, to support said no findings of said WURC limitations, merely coincides with the Examiner’s lack of findings. There were no limitations identified as well-understood, routine, and conventional (WURC), and therefore, there was not requirement for a Berkheimer evaluation or findings, nor was there a requirement for any evidence to prove the lack of existence. This argument is Moot, and as such, the 35 U.S.C. § 101 rejection is not overcome due to Berkheimer for claims 1, 11, or the depending claims.
Accordingly, neither Step 2A, Prong 2, nor Step 2B are met, such that the additional elements are not indicative of a practical application of the abstract ideas, nor do the additional elements amount to significantly more than the abstract ideas.
Please find the updated 35 U.S.C. § 101 rejection below, reflecting the amendments.
The 35 U.S.C. § 101 rejection is Maintained.
35 U.S.C. § 103
Applicant’s arguments regarding 35 U.S.C. § 103 for claims 1-4, 6-14, and 16-20 dated 18 May 2026, have been fully considered and are not persuasive. Therefore, the rejection has been maintained.
The Applicant’s arguments, on pages 9-10, asserting that the Office fails to assert disclosure of particular limitations by Deshmukh or teachings of those same limitations by Shoham, alone, are not probative. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As can be seen in the updated rejection, below, the new limitations that are comprised within the applicants’ assertions, are addressed in the updated rejection.
The 35 U.S.C. § 103 rejection is Maintained.
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 1-4,6-9, 11-14 and 16-19 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, two generate a model limitations, calculate a profile, identify user behavior, two receive data limitations, 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)).
These same limitations, disclosed above are also abstract ideas in the category of mental processes. That is, the claims are merely performing the task a loan officer would have historically performed, with an added depth of using medial specimens, to determine the financial risk a customer presents when making a financial request, including the steps to receive an inquiry, identify data, two generate a model limitations, calculate a profile, identify user behavior, two receive data limitations, generate a metric, and determine a response to inquiry. There is nothing in the claims that precludes the limitations from being performed in the human mind, with the aid of pen and paper, or using a computing structure, each used as a tool. At least [0077-0080] in the specification are devoted to describing the tasks of determining that an applicant has a creditworthy profile. Further, claims 1 and 11, for which the dependencies are based, explicitly recite “wherein each hazard label describes a predisposition to monetary risk based on an associated biological extraction.”
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.
In the two limitations for generating a model, the claims recite that these models are generated using the training data, utilizing the correlated data, one as an input to return an output of the corresponding data, returning the biological data correlated hazard label in the hazard model, and generating an account metric correlated to the profile in the account model used to determine a response to an account inquiry utilizing said metrics. These generated models are merely reciting that the model receives data, applies the data to train a generically recited general-purpose machine learning model, to return a characterized general-purpose model. Merely implementing training data to alter the manner in which certain data is processed by a general-purpose machine learning model is not and does not perform a transformation, because the difference is rooted in the characterized data, not in technical steps. In fact, the claims do not reveal any technical steps. The claims do not recite how the training is performed. Returning a recharacterized data or model through correlation and characterizations of data, does not result in a transformation because transformation as in MPEP 2106.05(c) requires a change from one form to another, data or a model that is recharacterized, merely by implanting data, i.e. non-functional descriptive information results in recharacterized data or a recharacterized model.
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 and 13-14 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. 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 1-4,6-9, 11-14 and 16-19 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);
receive hazard training data, wherein the hazard training data comprises a plurality of biological extractions and a plurality of hazard labels, wherein each hazard label describes a predisposition to monetary risk based on an associated biological extraction; [0015] (receive data that includes physiological metrics/data and financial metrics), [0005] (metrics may be comprised of physical and emotional attributes, i.e. biological extractions);
generate a hazard machine-learning model utilizing the hazard training data, wherein the hazard machine-learning model utilizes the biological extraction as an input and outputs a corresponding hazard label; [Figure 1, 122 and 124] (collect metrics in real time and historically processed) )[Figure 2 and [0031]) (train a particular model for the user, based on the data collected from biological and financial data, i.e. generate a model through which the biological extractions and user data for historical transactions are implemented to train the model to make risk and financial determinations to allow the user to proceed with, be denied, or receive an overriding approval or denial based on the training data), [0014] “The evaluator, such as a cloud service evaluator, may consider some or all of these financial metrics as well as physiological metrics of the subscriber and may then render a determination as to whether or not the transaction or expenditure should continue towards finalization,” [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);
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 the computing device identifies the element of user hazard data utilizing the hazard label output by the hazard machine-learning model, and wherein the computing device identifies the elements of user behavior data utilizing an account classifier; [Figure 1, 122 and 124] (collect metrics in real time and historically processed) )[Figure 2 and [0031]) (train a particular model for the user, based on the data collected from biological and financial data, i.e. generate a model through which the biological extractions and user data for historical transactions are implemented to train the model to make risk and financial determinations to allow the user to proceed with, be denied, or receive an overriding approval or denial based on the training data), [0014] “The evaluator, such as a cloud service evaluator, may consider some or all of these financial metrics as well as physiological metrics of the subscriber and may then render a determination as to whether or not the transaction or expenditure should continue towards finalization,” [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.
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 ANGELA HATCH whose telephone number is (571)270-1393. The examiner can normally be reached 10:00-6:00.
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ANGELA HATCH
Examiner
Art Unit 3626
/ANGELA HATCH/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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