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 communications filed on 03/19/2026.
Claims 1, 3-4, 6-11, 13-14, and 16-20 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
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, 3-4, 6-11, 13-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing and displaying resource allocation predictions without significantly more.
Examiner has identified claim 1 as the claim that represents the claimed invention presented in independent claims 1, 11, and 20.
Claim 1 is directed to a system, which is one of the statutory categories of invention; Claim 11 is directed to a method, which is one of the statutory categories of invention; and Claim 20 is directed to a non-transitory computer-readable storage medium, which is one of the statutory categories of invention (Step 1: YES).
Claim 1 is directed to a computer-implemented system for a machine learning system for resource allocation using partial or incomplete training data, the system comprising: a processor; a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: obtain unprocessed data sets from one or more data source devices; transform said unprocessed data sets into a historical resource allocation data set having a tabular format with a granular combination of data representing customer data fields associated with resource allocation queries and applications, wherein an inter-relation exists between one more of said data fields, and said data fields include one or more missing values; train a machine learning (ML) resource allocation model using said historical resource allocation data set, said ML resource allocation model being encoded and provided as a predictive model markup language (PMML) file, said training including: identifying feature attributes associated with said data fields characterized by an inter-relation with other feature attributes associated with said data fields; generating one or more conditional distribution representations to encode said inter-relation between feature attributes; and generating an imputed value for said one or more missing values based on said one or more conditional distribution representations; receive a resource allocation query including target data associated with a plurality of target feature attributes related to generating a resource allocation prediction; determine that the resource allocation query includes at least one unavailable data value associated with a given feature attribute from the plurality of target feature attributes; and prior to generating the resource allocation prediction, generate, based on the ML resource allocation model, an imputed data value in place of the unavailable data value based on one or more conditional distribution representation associated with the given feature attribute, the conditional distribution representation associated with the given feature attribute is based on historical data values of the given feature attribute; generate the resource allocation prediction using operations defined by the PMML file for the ML resource allocation model and the target data, the ML resource allocation model defined by at least one conditional distribution representation for providing an interim prediction corresponding to one or more feature attributes from the plurality of target feature attributes; iteratively refine parameters of the ML resource allocation model at a computed node associated with the at least one conditional distribution representation; transmit a signal representing the resource allocation prediction for display on a graphical user interface; display the target data associated with the plurality of target feature attributes on the graphical user interface with the resource allocation prediction; receive a query signal representing an explanation query associated with at least one queried feature attribute from the plurality of target feature attributes; and in response to receiving the query signal, generate, based on the ML resource allocation model, a signal for rendering, on the graphical user interface, a graphical user interface element representing an explanation representation based on a respective conditional distribution representation corresponding to the at least one queried feature attribute, the graphical user interface element comprising a visual indication for indicating a confidence measure corresponding to the resource allocation prediction. These series of steps describe the abstract idea of processing and displaying resource allocation predictions (with the exception of the italicized and bolded terms above), which is mitigating risk by generating and evaluating resource allocation queries or loan applications based on historical resource loan allocation data, and providing a loan application result which may include approve, deny, or re-submit; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing allocation queries or loan applications based on historical resource loan allocation data and transmitting resource allocation queries, mortgage loan applications, line-of-credit applications between entities, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Similar arguments can be extended to the other independent claims, claims 11 and 20; and hence claims 11 and 20 are rejected on similar grounds as claim 1.
Dependent claims 3-4, 6-10, 13-14, and 16-19 are directed to a system and method, respectively, which perform steps that describe the abstract idea of processing and displaying resource allocation predictions, which is mitigating risk by generating and evaluating resource allocation queries or loan applications based on historical resource loan allocation data, and providing a loan application result which may include approve, deny, or re-submit; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing allocation queries or loan applications based on historical resource loan allocation data and transmitting resource allocation queries, mortgage loan applications, line-of-credit applications between entities, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-4, 6-10, 12-14, and 16-19 are directed to an abstract idea. The additional limitations of a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface are no more than simply applying the abstract idea using generic computer elements. Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 3-4, 6-10, 13-14, and 16-19 have further defined the abstract idea that is present in their respective independent claims 1 and 11; and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract in nature for the reason presented above. The dependent claims 3-4, 6-10, 13-14, and 16-19 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, claims 3-4, 6-10, 13-14, and 16-19 are directed to an abstract idea without significantly more.
Thus, claims 1, 3-4, 6-11, 13-14, and 16-20 are not patent-eligible.
Response to Arguments
Applicant's arguments, dated 03/19/2026, have been fully considered, but they are not persuasive due to the following reasons:
With respect to the rejection of claims 1, 3-4, 6-11, 13-14, and 16-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “the focus of the claims is not a business method, but rather a refinement to the field of machine learning which allows machine learning models to exploit inter-relations between feature attributes so as to allow ML models to be trained using imperfect training data (e.g., training data which lacks values for one or more relevant data fields…. Applicant submits that it is clear that the claimed invention is directed to improvements in the technical field of machine learning, namely in enabling the training of machine learning models with reduces volumes of training data, and/or training data which contains deficient values. Rather than discarding deficient training data (thereby reducing the amount of training data, making the model less accurate), the claimed embodiments may exploit inter-relations between feature attributes to generate probability distribution representations. The Applicant submits that these claims are not directed to business methods - these principles can be applied to any use case in which the training data includes feature attributes which are inter-related. Whether this is for a resource allocation prediction or for some other purpose does not change that the claims recite a technical improvement to the field of machine learning….. The memorandum further outlines numerous amendments to sections which clarify that the integration of a judicial exception into a practical application can be established by the claimed invention providing an improvement in the functioning of a computer or a technical field. The Applicant submits that an improvement to the field of machine learning indeed represents an improvement to a technical field, and therefore qualifies as the claims being integrated into a practical application….. the Applicant submits that in being "careful to avoid oversimplifying the claims", the characterization in the subject Office Action of the claims being directed to "processing, transmitting, and displaying data" is a gross oversimplification of the claimed operations relating to the development of an improvement method for training machine learning models which can use smaller training data sets and avoid discarding data sets which are incomplete. The specification is rife with descriptions of the benefits of such an approach…..the Applicant submits that the claims are directed to patent-eligible subject matter and that any alleged judicial exception is integrated into a practical application. In particular, the Applicant submits that the claims are directed to an invention that provides an improvement to a technical field. For at least the above-noted reasons, the Applicant submits that amended independent claims 1, 11 and 20 (and the claims depending therefrom) are directed to patent-eligible subject matter which is compliant with 35 USC 101. Withdrawal of the rejection under 35 USC 101 is requested. ”
Examiner respectfully disagrees.
Under Step 2A: Prong 2, Examiner respectfully notes that there is no improved technology in simply obtaining, training (i.e., inputting and processing), receiving, storing, generating, refining (i.e., editing), transmitting, processing, encoding, and displaying data (e.g., predicted resource allocation information, mortgage loan applications data, line-of-credit applications data, etc.). As previously discussed in the Final Office Action dated 08/22/2025 and Non-Final Office Action dated 12/19/2025, the disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “a computer-implemented system for a machine learning system for resource allocation using partial or incomplete training data, the system comprising: a processor; a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: obtain unprocessed data sets from one or more data source devices; transform said unprocessed data sets into a historical resource allocation data set having a tabular format with a granular combination of data representing customer data fields associated with resource allocation queries and applications, wherein an inter-relation exists between one more of said data fields, and said data fields include one or more missing values; train a machine learning (ML) resource allocation model using said historical resource allocation data set, said ML resource allocation model being encoded and provided as a predictive model markup language (PMML) file, said training including: identifying feature attributes associated with said data fields characterized by an inter-relation with other feature attributes associated with said data fields; generating one or more conditional distribution representations to encode said inter-relation between feature attributes; and generating an imputed value for said one or more missing values based on said one or more conditional distribution representations; receive a resource allocation query including target data associated with a plurality of target feature attributes related to generating a resource allocation prediction; determine that the resource allocation query includes at least one unavailable data value associated with a given feature attribute from the plurality of target feature attributes; and prior to generating the resource allocation prediction, generate, based on the ML resource allocation model, an imputed data value in place of the unavailable data value based on one or more conditional distribution representation associated with the given feature attribute, the conditional distribution representation associated with the given feature attribute is based on historical data values of the given feature attribute; generate the resource allocation prediction using operations defined by the PMML file for the ML resource allocation model and the target data, the ML resource allocation model defined by at least one conditional distribution representation for providing an interim prediction corresponding to one or more feature attributes from the plurality of target feature attributes; iteratively refine parameters of the ML resource allocation model at a computed node associated with the at least one conditional distribution representation; transmit a signal representing the resource allocation prediction for display on a graphical user interface; display the target data associated with the plurality of target feature attributes on the graphical user interface with the resource allocation prediction; receive a query signal representing an explanation query associated with at least one queried feature attribute from the plurality of target feature attributes; and in response to receiving the query signal, generate, based on the ML resource allocation model, a signal for rendering, on the graphical user interface, a graphical user interface element representing an explanation representation based on a respective conditional distribution representation corresponding to the at least one queried feature attribute, the graphical user interface element comprising a visual indication for indicating a confidence measure corresponding to the resource allocation prediction.” Unlike Ex Parte Desjardins, as previously discussed, the recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. The recited steps in the claims are abstract in nature as there are no technical/technology improvements as a result of these steps. However, unlike Ex Parte Desjardins, the present claims, as amended, simply apply an abstract idea using a computer as a tool without offering any improvements to the computer or technology. Thus, Applicant has simply provided a business method practice of processing, transmitting, and displaying data (e.g., resource allocation predictions, consumer data, historical resource allocation data, historical data values, target data, and etc), and no technical solution or improvement has been disclosed.
Moreover, as previously discussed in the Final Office Action dated 08/22/2025 and Non-Final Office Action dated 12/19/2025, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of processing and displaying resource allocation predictions. Therefore, unlike Ex Parte Desjardins, there is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Furthermore, these steps are recited as being performed by a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface. These additional elements are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Claims 1, 11, and 20 recites a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface, which are simply used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, computed node, signal, query signal, explanation query, and graphical user interface in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. The claims merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, Claims 1, 11, and 20 do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1, 3-4, 6-11, 13-14, and 16-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Nagpal (U.S. Patent No. US 10,089,144 B1) “Scheduling computing jobs over forecasted demands for computing resources”
Achin (U.S. Patent Pub. No. US 2017/0243140 A1) “Systems and techniques for predictive data analytics”
Amaral (U.S. Patent Pub. No. US 2017/0255999 A1) “Processing system to predict performance value based on assigned resource allocation”
Wang (C.N. Patent Pub. No. CN 107222787 A) “Video resource popularity prediction method”
Yan (C.N. Patent Pub. No. CN 111124676 A) “Resource allocation method and device, readable storage medium and electronic equipment”
Any inquiry concerning this communication or earlier communications from the examiner s 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W. ANDERSON can be reached on (571) 270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/BRUCE I EBERSMAN/ Primary Examiner, Art Unit 3693