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 .
DETAILED ACTION
This office action is in response to an amendment received on 7/7/26 for patent application 18/541,880.
Claims 1, 10,16 are amended.
Claims 1-20 are pending.
RESPONSE TO ARGUMENTS
Applicant argues#1
I. The Claims are Not Directed to an Abstract Idea
The Examiner states that the claims recite a "mental process." (Non-Final Office Action at page 12).
Applicant respectfully submits that the Examiner's characterization of the claims reciting a "mental process" is incorrect. For example, Applicant respectfully submits that the claims themselves do not recite a mental process, at least because claim 1 recites a variety of non- human activities including "[a] system for evaluating alternative data sources, the system comprising." "one or more memories," and "one or more processors, communicatively coupled to the one or more memories, configured to:" "present an interface associated with an application to a first client device associated with a first user, wherein the interface indicates a first set of alternative data sources available for providing information related to behavioral attributes of the first user," "receive, from the first client device via the interface, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user," "electronically communicate with at least one of the one or more alternative data sources that are selected via the interface," "generate a decision associated with the application for the first user based on electronically communicating with the at least one of the one or more alternative data sources," "evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, wherein the machine learning model receives the set of observations as input from the at least one or more alternative data sources, wherein the machine learning model is trained to recognize patterns to make a prediction, wherein the machine learning model outputs a value of a target variable, wherein the target variable is effectiveness, and wherein the value is high or low," and "present the interface associated with the application to a second client device associated with a second user, wherein the interface presented to the second client device indicates, based on filtering available data sources using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user," and "update the machine learning model based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application." (Emphasis added).
Accordingly, for at least the reasons provided above, amended claim 1 does not recite a mental process.
Examiner Response
Examiner respectfully disagrees.
The claim limitations in bold below are reciting the identified abstract idea:
[a] system for evaluating alternative data sources, the system comprising." "one or more memories," and "one or more processors, communicatively coupled to the one or more memories, configured to:" "present an interface associated with an application to a first client device associated with a first user, wherein the interface indicates a first set of alternative data sources available for providing information related to behavioral attributes of the first user," "receive, from the first client device via the interface, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user," "electronically communicate with at least one of the one or more alternative data sources that are selected via the interface," "generate a decision associated with the application for the first user based on electronically communicating with the at least one of the one or more alternative data sources," "evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, wherein the machine learning model receives the set of observations as input from the at least one or more alternative data sources, wherein the machine learning model is trained to recognize patterns to make a prediction, wherein the machine learning model outputs a value of a target variable, wherein the target variable is effectiveness, and wherein the value is high or low," and "present the interface associated with the application to a second client device associated with a second user, wherein the interface presented to the second client device indicates, based on filtering available data sources using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user," and "update the machine learning model based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application." (Emphasis added).
Applicant is pointed to MPEP section 2106.04(a)(2) III:
B. A Claim That Encompasses a Human Performing the Step(s) Mentally With or Without a Physical Aid Recites a Mental Process.
If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper").
C. A Claim That Requires a Computer May Still Recite a Mental Process.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
The additional elements outside of the abstract idea (the interface, the one more processors coupled to one or more memories, first client device, second client device,
using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sourced and updating the machine learning model based on the determined effectiveness of the one more alternative data sources) are recited at a high level of generality and operating in their ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea.
The rejection is maintained.
Applicant argues#2
II. The Claims Integrate any Alleged Abstract Idea into a Practical Application
In the present application, the specification's paragraph [0012] states:
For example, a potential borrower with an insufficient credit history essentially has little or no proven track record that lenders or creditors can refer to in order to assess the creditworthiness of the potential borrower (e.g., distinct from a bad credit history that may include past due payments and/or collection actions, among other examples). For example, a potential borrower may have an insufficient credit history when a credit reporting agency (or credit bureau) has no credit record for the potential borrower, or when the credit record for the potential borrower does not contain enough accounts, a sufficient payment history, or recently reported activity to calculate a reliable credit score. In some cases, young or inexperienced borrowers (e.g., students) may have insufficient credit histories because building a credit history by making consistent payments can take significant time (e.g., several years). In other cases, a potential borrower may have a credit history that is inaccessible to an institution attempting to render a credit decision for the potential borrower. For example, when a person with an extensive credit history in a home country emigrates to a different country, the credit history (including credit score) in the home country is not portable to the new country (e.g., due to variations in data protection laws and/or differences in how credit scoring and credit reports work in different countries). As a result, because a lender or creditor typically only has access to data related to a domestic credit history and lacks access to non- domestic credit reporting agencies or other data sources that may provide a non- domestic credit history, experienced immigrant borrowers that may be good credit risks (e.g., have a low probability of default and/or have demonstrated an ability to make consistent debt payments over time) may be unable to obtain a vehicle loan, a mortgage, a credit card, and/or other credit products.
Accordingly, the specification's paragraph [0012] explains that the conventional technological processing fails to provide a streamlined manner for determining credit using alternative sources which, in turn, wastes computing resources. The specification's paragraph [0013] further states:
Some implementations described herein relate to a decisioning system that may use one or more alternative data sources to estimate a creditworthiness for a loan or credit applicant and use one or more machine learning techniques to evaluate and recommend alternative data sources that are most likely to be effective in generating a credit decision for a loan or credit applicant with an insufficient credit history and/or a low credit score based on one or more profile attributes associated with the loan or credit applicant (e.g., depending on whether the applicant is a student, an immigrant, or associated with other suitable profile attributes). For example, in cases where a credit applicant has an insufficient credit history or a low credit score, the decisioning system may allow one or more alternative data sources (e.g., other than credit bureau data) to be used to determine income status, a bill payment history, a rental payment history, or other information that may be relevant to the creditworthiness of the applicant. However, the effectiveness or relevance of certain alternative data sources may vary depending on the type of applicant. For example, utility or telecommunication bill payments may have little or no relevant data for a student who has spent their whole life on their parents' accounts. In another example, an immigrant may have no history of making rent payments on time. Accordingly, the decisioning system may support techniques to allow a credit applicant to select one or more alternative data sources to be relied upon when there is insufficient credit bureau data or the credit bureau indicates a low credit score. The decisioning system may then use machine learning techniques to evaluate the effectiveness of the alternative data source(s) used to render the credit decision. In this way, when subsequent users access the credit application, the available data sources may be filtered or customized based on the available data sources that are most likely to be effective in informing the credit decision. In this way, by recommending alternative data sources that are most likely to provide information relevant to the credit decision, the decisioning system may conserve resources that would otherwise have been wasted communicating with alternative data sources providing data that is likely to be ineffective in informing the credit decision and/or processing data obtained from such alternative data sources.
Therefore, the present application's invention clearly improves upon conventional functioning of a technological process.
Examiner Response
Examiner respectfully disagrees.
Para 13 of the instant specification discloses:
[0013]Some implementations described herein relate to a decisioning system that may use one or more alternative data sources to estimate a creditworthiness for a loan or credit applicant and use one or more machine learning techniques to evaluate and recommend alternative data sources that are most likely to be effective in generating a credit decision for a loan or credit applicant with an insufficient credit history and/or a low credit score based on one or more profile attributes associated with the loan or credit applicant (e.g., depending on whether the applicant is a student, an immigrant, or associated with other suitable profile attributes).. The decisioning system may then use machine learning techniques to evaluate the effectiveness of the alternative data source(s) used to render the credit decision. In this way, when subsequent users access the credit application, the available data sources may be filtered or customized based on the available data sources that are most likely to be effective in informing the credit decision. In this way, by recommending alternative data sources that are most likely to provide information relevant to the credit decision, the decisioning system may conserve resources that would otherwise have been wasted communicating with alternative data sources providing data that is likely to be ineffective in informing the credit decision and/or processing data obtained from such alternative data sources.
The “streamlined manner for determining credit using alternative sources” is not
improving upon conventional functioning of a technological process.
A human with a pen and paper solution is able to evaluate the effective of different sources and make a determination for creditworthiness.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is merely using existing technology (for its intended purpose) to implement the steps of the identified abstract idea. Any improvements lie in the abstract idea itself, not in underlying technology
The rejection is maintained.
Applicant argues#3
Further, claim 1 reflects the specifications disclosed improvement in technology. Specifically, claim 1 at least recites to "evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, wherein the machine learning model receives the set of observations as input from the at least one or more alternative data sources, wherein the machine learning model is trained to recognize patterns to make a prediction, wherein the machine learning model outputs a value of a target variable, wherein the target variable is effectiveness, and wherein the value is high or low." Accordingly, claim 1 reflects the improvement in evaluating "an effectiveness of the one or more alternative data sources used to generate a decision associated with the application for the first user" by having a machine learning model recognize patterns to make a prediction and determine whether the effectiveness is high or low.
For at least the reasons presented in the interview and without acquiescing in the Examiner's rejection, independent claims 1, 10, and 16, as amended, and the claims that depend thereon, are patent-eligible under 35 U.S.C. § 101.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection of claims 1-20 under 35 U.S.C. § 101.
Examiner Response
Examiner respectfully disagrees.
Examiner reproduces spec paras 34&36 below:
[0034] The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values SO that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model
[0036] As shown by reference number 220, the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, or the like. For example, in some implementations, the machine learning system may evaluate whether data obtained from a given alternative data source was effective in informing a credit decision for a given credit applicant based on a type or profile associated with the credit applicant, based on whether the credit decision was to approve or reject the credit application, and/or based on one or more terms (e.g., an interest rate) that were offered in cases where the credit decision was to approve the credit application, among other examples. After training, the machine learning system may store the machine learning model as a trained machine learning model 225 to be used to analyze new observations
It can be seen from these spec paras, the machine learning model is recited a high level of generality and is operating in its ordinary capacity and is being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Therefore, there are no additional elements that are indicative of integration into a practical application.
The rejection is maintained.
Claim Rejections- 35 U.S.C § 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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a method, system and computer readable medium which are one of the statutory categories of invention. (Step 1: YES).
Representative Claim 1 recites the limitations of:
A system for evaluating alternative data sources, the system
comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
present an interface associated with an application to a first client device
associated with a first user,
wherein the interface indicates a first set of alternative data sources
available for providing information related to behavioral attributes of the first
user;
receive, from the first client device via the interface, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user;
electronically communicate with at least one of the one or more alternative data sources that are selected via the interface;
generate a decision associated with the application for the first user based on electronically communicating with the at least one of the one or more alternative data sources;
evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user;
wherein the machine learning model receives a set of observations as input from the at least one or more alternative sources,
wherein the machine learning is trained to recognize patterns to make a prediction,
wherein the machine learning model outputs a value of a target variable,
wherein the target variable is effectiveness, and
wherein the value is high or low;
present the interface associated with the application to a second client device
associated with a second user,
wherein the interface presented to the second client device indicates, based on
filtering available data sources using the machine learning model after the
machine learning model is trained based on the effectiveness of the one or more
alternative data sources used to generate the decision associated with the
application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user; and
update the machine learning model based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application.
The claim recites elements that are in bold above, (e.g., present an application to a
first user, indicates a first set of alternative data sources available for providing information related to behavioral attributes of the first user; receive, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user; communicate with at least one of the one or more alternative data sources that are selected; generate a decision associated with the application for the first user based on communicating with the at least one of the one or more alternative data sources; evaluate, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user; receives a set of observations as input from the at least one or more alternative sources, to recognize patterns to make a prediction, outputs a value of a target variable, wherein the target variable is effectiveness, and wherein the value is high or low; present the application to a second user, indicates, based on filtering available data source on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user; to generate the decision associated with the application), under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process, more specifically a concept performed mentally by a human with a pen and paper, (steps for generating a recommendation of alternative data sources based on a set observations related to an effectiveness of alternative data sources)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a certain method of a concept performed in the human mind, then it falls within the “mental process” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea. Claims 10,16 recite substantially the same subject matter as claim 1 and are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1,10, 16 include the following additional elements:
-One or more memories
-One or more processors
-Using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources and updating the machine learning model based on the determined effectiveness of the one more alternative data sources
-An interface associated with an application to a first client device
-A first client device
-A second client device
-A decisioning system
-A non-transitory computer readable medium
The one or more memories, one or more processors, using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sourced and updating the machine learning model based on the determined effectiveness of the one more alternative data sources, interface associated with an application to a first client device, second client device, a decisioning system and a non-transitory computer readable medium are recited at a high level of generality and being used in its ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Therefore, there are no additional elements in the claim that amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 10,16 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1,10, 16 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-9, 11-15, 17-20 which further define the abstract idea that is present in their respective independent claims 1, 10, 16 and thus correspond to a Mental process and hence are abstract for the reasons presented above.
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Therefore, the dependent claims 2-9, 11-15, 17-20 are directed to an abstract idea. Thus, claims 1-20 are not patent-eligible.
CONCLUSION
THIS ACTION IS MADE FINAL. 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 MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 9/8/2026
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