DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/31/2025 has been entered.
Status of Claims
The present application is being examined under the AIA first to file provisions.
This action is in reply to the RCE filed on 10/25/2025.
Claims 1, 15, 29, and 33 have been amended and are hereby entered.
Claims 1-36 are currently pending and have been examined.
Claim Interpretation
In claims 1, 15, 29, and 33 the clause “the digital profile specifically configured to track informal economic transactions not captured by traditional credit bureau systems and used by the user in making one or more financial transactions” and “wherein the API gateway uses a digital profile assigned to the user specifically configured to track informal economic transactions not captured by traditional credit bureau systems” is interpreted as an intended use/field of use of digital profile. The intended use in the claim merely states the result of the limitation in the claim and adds nothing to the patentability or substance of the claim. See Texas Instruments Inc. v. International Trade Commission, 26 USPQ2d 1010 (Fed. Cir 1993); Griffin v. Bertina, 62 USPQ2d 1431 (Fed. Cir. 22); Amazon.com Inc. v. Bamesandnoble.com Inc., 57 USPQ2d 1747 (Fed. Cir. 21). Hence the intended use limitations are not given patentable weight.
In general, the grammar and intended meaning of terms used in a claim will dictate whether the language limits the claim scope. Language that suggests or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation. The following are examples of language that may raise a question as to the limiting effect of the language in a claim:
statements of intended use or field of use,
"adapted to" or "adapted for" clauses,
"wherein" clauses, or
"whereby" clauses.
This list of examples is not intended to be exhaustive. See also MPEP § 2111.04.
The rejections given below are interpreted in light of 35 U.S.C. § 112, rejections and the claim interpretation discussed above.
Response to Arguments
Applicant's arguments filed 04/17/2025 with respect to the 101 rejection for the claims being directed towards an abstract idea have been fully considered but they are not persuasive.
Applicant argues #1:
The Claims Are Directed to a Technological Solution to a Technical Problem
The amended independent claims are directed to a specific technological solution that addresses the technical problem of determining creditworthiness for users in emerging markets who lack formal credit histories. As amended, claim 1 recites "create a digital profile for a user in an emerging market who lacks a formal credit history, the digital profile specifically configured to track informal economic transactions not captured by traditional credit bureau systems." This language, supported by the specification's discussion of consumers in emerging markets who cannot build credit scores due to limited access to formal financial products, establishes that the claims address a specific technical challenge that traditional credit systems cannot solve.
The specification explains that "the inability for consumers in emerging markets to build credit scores translates to limited access to various formal financial products such as insurance, pension, credit more generally, and mortgages specifically. As such, consumers have severely limited opportunities to build wealth, secure downside protection from personal disasters, and save for when they are unable to generate incomes in the later years of life." As-Filed Specification, page 1, Lines 2-6. The specification further describes that "there is accordingly a need for systems and methods that track and analyze transactions made by a user that primarily conducts informal economic activity, over a period of time, in order to determine an individualized credit score." As-Filed Specification, Column 1, Lines 11-16.
Examiners response:
The Examiner respectfully disagrees, the problem of determining creditworthiness for users in emerging markets who lack formal credit histories cited is not a technical problem but rather a business problem and the solution of analyze transactions not captured by a traditional credit bureau to determine the score is not technical but rather is describing commercial and legal interactions for a different abstract way to analyze information for determining a credit score. Additionally, MPEP 2106.05(d) provides evidence that this is not technical, see Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);.
For the reasons above, applicant’s arguments are not persuasive.
Applicant argues #2:
The Claims Provide a Specific Improvement to Computer Functionality
Similar to the improvement recognized in Enfish LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), the amended claims provide a specific improvement in how computers process and analyze financial data for credit scoring. The amended claims recite that "the machine learning model is specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories" as recited by claims 1, 15, 29, and 33. This represents a technological advancement that enables computers to process previously untrackable informal transactions to generate credit scores in real-time.
The claims improve computer functionality by enabling the tracking and analysis of "informal economic activities including at least one of rent payments, utility payments, and bill payments that are not formally recorded in traditional credit bureau systems" as recited by the amended claims. This capability represents a specific enhancement to how computers can process financial data, moving beyond the limitations of traditional credit systems that cannot capture or analyze such informal economic activities.
Examiners response:
The Examiner respectfully disagrees, while there may be an improvement in the abstract method for calculating a credit scores this does not render the claims eligible or result in a technical improvement to additional elements themselves. With respect to Enfish, In Enfish, the courts applied the distinction to reject the § 101 challenge at stage one because the claims in Enfish focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement — a particular database technique — in how computers could carry out one of their basic functions of storage and retrieval of data. Enfish, 822 F.3d at 1335-36; see Bascom, 827 F.3d at 1348-49, 2016 WL 3514158, at *5; cf. Alice, 134 S.Ct. at 2360 (noting basic storage function of generic computer). The present case is different: the focus of the claims is not on such an improvement in computers as tools, as there are is no improvement to the computer itself, but on certain independently abstract ideas for tracking information and calculating a credit score based on that information, invoking the computer as a tool. As per MPEP 2106.05(f) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone);. See also Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017) where the patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108 in which the Courts found the claims to be direct towards commercial and legal interactions.
Applicant argues #3:
The Claims Integrate the Abstract Idea Into a Practical Application
"Prong Two of the Alice/Mayo test asks does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP § 2106.04(II)(2). In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. Id. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception and thus is patent eligible. Id." Prong Two Practical Application Test.
The amended claims integrate any abstract idea into a practical application by solving the specific technical challenge of tracking and analyzing informal economic activities through digital profiles and neural networks. The claims recite specific technological elements including "a digital profile specifically configured to track informal economic transactions not captured by traditional credit bureau systems" and "a machine learning model specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories."
These elements work together to provide a technological solution that traditional credit systems cannot accomplish. The specification supports this practical application by explaining that "rent - and mortgage repayments - cater to a fundamental need for shelter, very few consumers default on their rent payments. There is, therefore, an opportunity to develop a credit scoring platform for consumers to access mortgages based on how well they pay their rent and other bills." As-Filed Specification, page 2, Lines 16-22; As-Filed Specification, page 2, Lines 22-24.
Examiners response:
The Examiner respectfully disagrees, the Examiner fails to see how the claims address any technical challenges in the art for tracking transaction information of the user by using a digital profile, as MPEP 2106.05(d) shows it’s well within a computer’s capabilities to send and receive data over a network is WURC, and not an improvement, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);. Additionally with respect to the machine learning, this does not render the eligible for the same reasons as in the new July 2024 Subject Matter Eligibility Examples, which provides additional guidance on Patent Subject Matter Eligibility, including artificial intelligence. Similar to Claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples, the training is recited at high level of generality such that it amounts to using a generic computer to perform generic computer functions, akin to using a computer to perform repetitive calculations (See MPEP 2106.05(d)), and therefore amounts to no more than mere instructions to apply the exception using a generic computer (See MPEP 2106.05(f)), and just because claims may be novel under § 103 over a number of prior art rejections, this does not mean they are not directed to an abstract idea. Cf. Intellectual Ventures ILLCv. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016).
Indeed, “[t]he ‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Diamond v. Diehr, 450 U.S. 175, 188—89 (1981) (emphasis added); see also Mayo, 132 S. Ct. at 1303—04 (rejecting “the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101”). Here, the jury’s general finding that Symantec did not prove by clear and convincing evidence that three particular prior art references do not disclose all the limitations of or render obvious the asserted claims does not resolve the question of whether the claims embody an inventive concept at the second step of Mayo/Alice.
Therefor this argument is not persuasive.
Applicant argues #3:
The Claims Recite Significantly More Than Any Abstract Idea
The amended claims recite significantly more than any abstract idea through their specific technological implementation. The neural network training recited by the claims includes specific steps of "initializing a neural network comprising at least one differential equation and a plurality of neurons,""executing an activation function for each of the plurality of neurons,""evaluating a cost function to determine a value for an error of the neural network," and "adjusting values of the one or more weights to reduce the error of the neural network" as recited by claims 1, 15, 29, and 33.
These specific technological steps, combined with the focus on informal economic transactions not captured by traditional systems, provide meaningful limitations that go well beyond generic computer implementation. The claims are not merely using a computer to perform repetitive calculations, but rather implementing a specific technological solution to enable credit scoring based on previously untrackable data sources.
Application of August 4, 2024 USPTO Guidance on AI-Related Subject Matter Eligibility
The amended claims are consistent with the USPTO's August 4, 2024 guidance memo on subject matter eligibility for AI-related inventions. The guidance emphasizes that AI-based claims are patent-eligible when they are directed to specific technological improvements or practical applications. Here, the amended claims recite a machine learning model "specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories." This is not a generic AI implementation, but rather a specific application of machine learning technology to solve the concrete problem of credit scoring for users in emerging markets who lack formal credit histories-a problem that traditional credit bureau systems cannot address. The claims further recite specific structural limitations including digital profiles "specifically configured to track informal economic transactions not captured by traditional credit bureau systems," which demonstrates that the AI model is integrated into a practical application that improves upon existing technological systems. Under the August 4, 2024 guidance, such specific applications of AI technology to solve concrete technological problems constitute patent-eligible subject matter under 35 U.S.C. § 101.
Examiners response:
The Examiner respectfully disagrees, as an initial matter the focus of the claims is not on untrackable data sources, and it’s only in the dependent claims that the claims further define the data being received from third party systems, even then this does not amount to a practical application or inventive concept as it’s well within a computer’s capabilities to send and receive data over the network, see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); for example. The use of AI in the instant application is akin to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), where the Courts found that instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment and that the requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents was not a technological improvement in that iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Similar to Recentive Analytics, the neural network and machine learning of the instant application is at best being used in a new environment, emerging markets who lack formal credit histories, which does not constitute a technical problem, the requirements of the neural network be trained, initialized, executing activation functions, evaluating weights, and adjusting the weights are basic neural network concepts rooted in mathematics and as explained previously akin to performing repetitive calculations, see pages 22, 24-26 of applicant’s specification for example of such calculations and basic neural network functions.
For the reasons above, the 101 rejection of claims 1-36 is hereby 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.
Subject Matter Eligibility Test under 101
Claims 1-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and fails step 2 of the analysis because the focus of the claims is not on the devices themselves or a practical application but rather directed towards an abstract idea, the analysis is provided below.
Step 1 (Statutory Categories) – The claims pass step 1 of the subject matter eligibility test (see MPEP 2106(III)) as the claims are directed towards a system and methods.
Step 2A – Prong One (Do the claims recite an abstract idea?) -
Claims 1 and 15 recites an idea, in part, by:
create a digital profile for a user in an emerging market who lacks a formal credit history, the digital profile specifically configured to track informal economic transactions not captured by traditional credit bureau systems and used by the user in making one or more financial transactions;
track transaction information of the user based on the digital profile, the transaction information relating to financial transactions made by the user comprising informal economic activities including at least one of rent payments, utility payments, and bill payments that are not formally recorded in traditional credit bureau systems, at least one of the financial transactions comprising an identification of a purchased good or purchased service, and a price for the purchased good or purchased service; 5
store the transaction information relating to the financial transactions made by the user with reference to the digital profile;
train a machine learning model configured to determine, for the user, an individually-determined credit score for the user based on, at least in part, the transaction information relating to the financial transactions made by the user wherein the machine learning model is specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories comprising:
initialize a neural network comprising at least one differential equation and a plurality of neurons having at least a portion of the transaction information as one or more inputs, and one or more weights;
execute an activation function for each of the plurality of neurons of the one or more inputs and the one or more weights wherein a value computed by executing the activation function for a first one of the plurality of neurons is input to a second one of the plurality of neurons;
evaluate a cost function to determine a value for an error of the neural network; and
adjust values of the one or more weights to reduce the error of the neural network;
apply the machine learning model to the transaction information relating to the financial transactions made by the user to determine the individually-determined credit score for the user;
determine interests for the user in a bundle of a plurality of goods or services based on the transaction information; and
determine one or more first terms for at least a first one of the bundle of the plurality of the goods or services based on the individually-determined credit score and one or more second terms for at least a second one of the bundle of the plurality of the goods or services, the one or more first terms and the one or more second terms including price and credit terms; and
generate for a user the one or more first terms for the at least first one of the bundle of the plurality of goods or services and the one or more second terms for the at least second one of the bundle of the plurality of goods or services.
Claim 29 recites an idea, in part, by:
capturing one or more user transactions for a user in an emerging market who lacks a formal credit history with at least one payment or transaction tracking method at least one of the user transactions comprising an identification of a purchased good or purchased service, and a price for the purchased good or purchased service, us[ing] a digital profile assigned to the user specifically configured to track informal economic transactions not captured by traditional credit bureau systems;
storing data associated with the user with reference to the digital profile of the user, including user identification data, user transaction records comprising informal economic activities including at least one of rent payments, utility payments, and bill payments that are not formally recorded in traditional credit bureau systems, user credit score data, user customized pricing data, user recommendations, and user savings data;
processing and categorizing the one or more user transactions into groups including rental payments, bill payments, savings payments and storing the resultant transaction data;
training a machine learning model configured to calculate a credit score associated with the user as a function of the data associated with the user and storing the credit score wherein the machine learning model is specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories comprising:
initializing a neural network comprising at least one differential equation and a plurality of neurons having at least a portion of the user transaction records as one or more inputs, and one or more weights;
executing an activation function for each of the plurality of neurons of the one or more inputs and the one or more weights wherein a value computed by executing the activation function for a first one of the plurality of neurons is input to a second one of the plurality of neurons;
evaluating a cost function to determine a value for an error of the neural network; and
adjusting values of the one or more weights to reduce the error of the neural network;
applying the machine learning model to the user transaction records to calculate the credit score of the user;
determining interests for the user in a bundle of a plurality of goods or services based on the one or more user transactions; and
determining one or more first terms for at least a first one of the bundle of the plurality of the goods or services based on the credit score and one or more second terms for at least a second one of the bundle of the plurality of the goods or services, the one or more first terms and the one or more second terms including price and credit terms; and
generating for a user the one or more first terms for the at least first one of the bundle of the plurality of goods or services and the one or more second terms for the at least second one of the bundle of the plurality of goods or services.
And similarly, claim 33 recites an idea, in part by:
capturing user transactions for a user in an emerging market who lacks a formal credit history with at least one payment or transaction tracking method at least one of the user transactions comprising an identification of a purchased good or purchased service, and a price for the purchased good or purchased service, wherein us[ing] a digital profile assigned to the user specifically configured to track informal economic transactions not captured by traditional credit bureau systems;
storing data associated with the user with reference to the digital profile of the user, including user identification data, user transaction records comprising informal economic activities including at least one of rent payments, utility payments, and bill payments that are not formally recorded in traditional credit bureau systems, user credit score data, user customized pricing data, user recommendations, and user savings data;
processing and categorizing the user transactions into groups including one or more of an extant bundled financial product opted into by the user, mortgage repayments, pension contribution payments, insurance premium payments, rental payments, bill payments, savings payments and storing the user transactions into the groups;
training a machine learning model configured to calculate a credit score associated with the user as a function of the user transactions and data associated with the user and storing the credit score wherein the machine learning model is specifically trained to analyze the informal economic activities to determine creditworthiness for users lacking formal credit histories comprising:
initializing a neural network comprising at least one differential equation and a plurality of neurons having at least a portion of the user transaction records as one or more inputs, and one or more weights;
executing an activation function for each of the plurality of neurons of the one or more inputs and the one or more weights wherein a value computed by executing the activation function for a first one of the plurality of neurons is input to a second one of the plurality of neurons;
evaluating a cost function to determine a value for an error of the neural network; and
adjusting values of the one or more weights to reduce the error of the neural network;
applying the machine learning model to the user transaction records to calculate the credit score of the user;
determining interests for the user in a bundle of a plurality of goods or services based on the user transactions; and
determining one or more first terms for at least a first one of the bundle of the plurality of the goods or services based on the credit score and one or more second terms for at least a second one of the bundle of the plurality of the goods or services, the one or more first terms and the one or more second terms including price and credit terms; and
generating for a user the one or more first terms for the at least first one of the bundle of the plurality of goods or services and the one or more second terms for the at least second one of the bundle of the plurality of goods or services.
The steps recited above under Step 2A prong 1 of the analysis under the broadest reasonable interpretation covers commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations) for calculating credit scores for an individual and offering one or more terms for goods or services based on the information associated with the user, but for the recitation of generic computer components. Other than reciting generic computer components and a machine learning model/unit nothing in the claim elements are directed towards anything other than commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation covers commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A – Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) - This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a memory unit, a computer-readable storage media, one or more processers, processing module, an API gateway, display for a graphical user interface, and machine learning model/unit. The memory unit, computer-readable storage media, one or more processers, processing module, API gateway, display for a graphical user interface, and machine learning model/unit are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components and limits the judicial exception to the particular environment of computers. Mere instructions to apply the judicial exception using generic computer components and limiting the judicial exception to a particular environment are not indicative of a practical application (see MPEP 20106.05(f) and MPEP 20106.05(h)). The specification does not provide any indication that the memory unit, computer-readable storage media, one or more processers, processing module, API gateway, display for a graphical user interface, and machine learning model/unit is other than generic computer components as described in pages 23 and 25-26 as an example. As per MPEP 2106.05(F), use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similar to Claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples, the training is recited at high level of generality such that it amounts to using a generic computer to perform generic computer functions, akin to using a computer to perform repetitive calculations (See MPEP 2106.05(d)) as shown in pages 24-26 of applicant’s specification, and therefore amounts to no more than mere instructions to apply the exception using a generic computer (See MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed towards an abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) - The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, with respect to integration of the abstract idea into a practical application, using the memory unit, computer-readable storage media, one or more processers, processing module, API gateway, display for a graphical user interface, and machine learning model/unit perform the steps recited above under Step 2A Prong 1 of the analysis amounts to no more than mere instructions to apply the exception using generic computer components and limits the idea to the computer environment. Mere instructions to apply an exception using a generic computer components and limiting an idea to a particular environment does not provide an inventive concept. The additional elements have been considered separately, and as an ordered combination, and do not add significantly more (also known as an “inventive concept”) to the judicial exception. The training and use of the machine learning and defining a neural network is merely using the computer and model to perform repetitive calculations and analyze data akin Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values);, further, MPEP 2106.05(d)(ii) provides that receiving and transmitting data over a network (see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), and Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); are well-understood routine and conventional, similar to the instant application claims which recites and sending and receiving data over network, and storing and retrieving information from the memory unit for calculating a credit score. Further, the displaying step falls to transform the claims into patent eligible material, as this is part of the field of use and technical environment in which the abstract idea is being implement and does not result in an improvement to additional elements (see MPEP 2106.05(h) Electric Power Group court decision). Thus, the claims are not patent eligible.
The dependent claims have been given the full analysis including analyzing the additional limitations both individually and in combination as a whole. For instance, claims 2-9, 11-14, 30-32, and 34-36 further define the abstract idea and environment in which the idea is being limited to and are all steps that fall within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas similar to above. Claim 10 recites training the machine learning model at high level of generality such it amounts to using a computer as tool to perform repetitive calculations and analyze data, similar to as discussed above. Claims 16-28 are substantially similar to claims 2-14, and ineligible for the same reasons. The Dependent claims when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 for the same reasoning as above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY S CUNNINGHAM II whose telephone number is (313)446-6564. The examiner can normally be reached Mon-Fri 8:30am-4pm.
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GREGORY S. CUNNINGHAM II
Primary Examiner
Art Unit 3694
/GREGORY S CUNNINGHAM II/Primary Examiner, Art Unit 3694