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 08/07/2026.
Claims 1, 11, and 20 have been amended and are hereby entered.
Claims 1, 3-4, 6-11, 13-14, and 16-20 are currently pending and have been examined.
This action is made Non-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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/07/2026 has been entered.
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, wherein the inter-relation corresponds to a hierarchical relation among said feature attributes, wherein a conditional distribution representation associated with a first feature attribute at a first level of said hierarchical relation is correlated with a conditional distribution representation associated with a second feature attribute at a second level of said hierarchical relation different from said first level; generating one or more conditional distribution representations to encode said inter-relation between feature attributes, said conditional distribution representations defining a probabilistic output for one or more feature attributes based on values of said historical resource allocation data set; and bypassing removal of said data fields having said one or more missing values by 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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 08/07/2026, 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 “although amended claim 1 may involve mathematical relationships, the claim as a whole is not directed to those mathematical relationships, and that any such mathematical relationships are integrated into a learning training and inference architecture. The Applicant submits that the focus of the claims is an improvement to how machine learning models operate when confronted with incomplete or unrepresentative training data, rather than to "mitigating risk" in any business sense.
Examiner respectfully disagrees.
Under Step 2A: Prong 1, Examiner respectfully notes that claims 1, 11, and 20, as amended, is 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. The series of steps recited in claims 1, 11, and 20, as amended, 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. Furthermore, the system limitations, e.g., a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, computed node, signal, query signal, explanation query, and graphical user interface do not necessarily restrict the claim from reciting an abstract idea. Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, and as discussed in the Guidance on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the recited features in the limitations: “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, wherein the inter-relation corresponds to a hierarchical relation among said feature attributes, wherein a conditional distribution representation associated with a first feature attribute at a first level of said hierarchical relation is correlated with a conditional distribution representation associated with a second feature attribute at a second level of said hierarchical relation different from said first level; generating one or more conditional distribution representations to encode said inter-relation between feature attributes, said conditional distribution representations defining a probabilistic output for one or more feature attributes based on values of said historical resource allocation data set; and bypassing removal of said data fields having said one or more missing values by 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” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Applicant argues that even if the claims were directed to a judicial exception (the Applicant does not concede this), that any such judicial exception is integrated into a practical application because the claims are directed to an improvement to the technical field of machine learning. …. The Applicant further submits that the claims are analogous to those in Desjardins. For example, it was found that claims to training a machine learning model to learn new tasks while protecting knowledge of previously learned tasks (overcoming so-called "catastrophic forgetting") were directed to an improvement to how the machine learning model itself functions, and that the disclosed improvement was reflected in the claim limitations. Here, the claims recite a specific mechanism (exploiting hierarchically related conditional distribution representations) to enable ML models to be trained on incomplete datasets and to handle missing data values. Just as Desjardins related to improvements including reduced storage capacity and reduced system complexity, amended claim 1 yields technical improvements, including model training without discarding any incomplete data records, and increasing model accuracy by leveraging hierarchical relationships among feature attributes….. Similarly, the Applicant submits that amended claim 1 recites a particular solution to a technical problem of ML training and inference with incomplete training data. The Applicant further submits that the claims do not merely recite the idea of a solution or outcome, but recite the details as to how the particular solution to the technical problem is accomplished.”
Examiner respectfully disagrees.
Under Step 2A: Prong 2, as previously discussed, 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, 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, wherein the inter-relation corresponds to a hierarchical relation among said feature attributes, wherein a conditional distribution representation associated with a first feature attribute at a first level of said hierarchical relation is correlated with a conditional distribution representation associated with a second feature attribute at a second level of said hierarchical relation different from said first level; generating one or more conditional distribution representations to encode said inter-relation between feature attributes, said conditional distribution representations defining a probabilistic output for one or more feature attributes based on values of said historical resource allocation data set; and bypassing removal of said data fields having said one or more missing values by 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.” Additionally, unlike Ex Parte Desjardins, 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.
Additionally, as previously discussed, 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.
Applicant argues that the “Examiner further submits that the ordered combination of identifying hierarchically inter-related feature attributes, generating conditional distribution representations encoding those interrelations, and bypassing data record removal by generating imputed values for missing values during training constitutes an inventive concept that goes beyond routine or conventional activity. The Applicant submits that no evidence has been provided demonstrating that the claimed configuration is well-understood, routine, or conventional.”
Examiner respectfully disagrees.
Under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. As discussed in RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017), “adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract.” Instead, an “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. Furthermore, Examiner notes that the courts have determined that processing data is well-understood, routine, and conventional functions of a computer when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)). Thus, the recited combination of steps in claims 1, 11, and 20 operate in a well-understood, routine, conventional and generic way. As noted above, the claims do 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, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, 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.
Applying the Guidance on Patent Subject Matter Eligibility here, and as explained with respect to Step 2A, Prong 2, the additional elements: a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, computed node, signal, query signal, explanation query, and graphical user interface, are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). The additional elements: a system, machine learning system, processor, memory, one or more data source devices, machine learning (ML) resource allocation model, conditional distribution representation associated with a first feature attribute, conditional distribution representation associated with a second feature attribute, one or more conditional distribution representations, computed node, signal, query signal, explanation query, and graphical user interface are found to be insignificant extra-solution activity in Step 2A, Prong Two, because they are determined to be insignificant limitations as necessary for data gathering, processing, and outputting. The evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong Two above, the recitation of a computer/processor to perform recited limitations, as amended, amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
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 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
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693
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