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 is a final office action in response to the amendment filed 20 October 2025. Claims 1, 3, 5, 9-18, 21, and 33 have been amended. Claims 1-34 are pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 3, 5, 9-18, 21, and 33 has been entered.
Applicant’s amendment is sufficient to overcome the pending 35 U.S.C. 112(b) rejection for lack of antecedent basis. The rejection is respectfully withdrawn.
Applicant’s amendment is insufficient to overcome the 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the claims are not directed to an abstract idea; that the additional elements related to the machine learning integrate any alleged abstract idea into a practical application because the claims recite a specific combination of highly-particular interoperating elements that generate unique data to produce a particular improvement in information technology and computer resource optimization. Examiner respectfully disagrees.
While the amended claims include additional elements such as dynamically allocating a set of electronic resources to a corresponding set of service areas, and concurrently utilizing a plurality of electronic machine learning models in tandem with an ensemble learning algorithm configured for creating classification data, these additional elements do not integrate the abstract idea into a practical application and when considered individually and in combination do not amount to significantly more than the recited abstract idea. The claim language related to the claimed allocation of resources is broadly and generically recited, such that the claimed technological improvement amounts to mere instructions to implement the abstract idea of analyzing performance data for service level risk assessment on a computer, or merely uses a computer as a tool to perform the abstract idea, as discussed in MPEP 2106.05(f). Further, allocation is reasonably construed as assigning a resource or assigning a task to a resource because the claims fail to recite specific functions that are performed to implement the allocation or provisioning of resources in a manner that provides a technical solution to a technical problem that goes beyond data processing and generating an output in the form of an instruction or alert. The determination of whether a performance metric is within a certain predetermined range of a risk threshold could be performed mentally. The claim limitations fail to detail how the set of resources are allocated in a manner that provides a practical application of computing or another technical field.
The machine learning and ensemble learning algorithm related limitations fail to provide a technical solution that transforms the recited abstract idea directed to data collection, analysis, and output into a practical application. These claim elements are analogous to those of Example 47 Claim 2, which is deemed ineligible because the output of the data analysis using the machine learning and ensemble algorithms encompasses mental choices and evaluations and mathematical calculations that do not impose any meaningful limits on the claimed data gathering and outputting. Theses additional elements provide nothing more than mere instructions to implement the abstract idea on a generic computer because the limitations only recite an outcome of the data analysis and manipulation with any details about how the predicted service level metric data output is used to achieve the intended purpose of preventing waste from overprovisioning. Therefore, the 35 U.S.C. 101 rejection is proper, maintained, and updated below, as necessitated by amendment.
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 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a product, independent claim 9 recites a process, and independent claim 18 recites a system for determining the service level delivery of an information technology system via a partial dependence plot. Independent claim 1 and 18 recite substantially similar limitations. The claims are directed to an abstract idea of collecting, analyzing, and manipulating data to output a predicted a marginal contribution of a service level metric or feature for business decision making related to optimizing and assigning information technology resources, without significantly more.
Taking independent claim 1 as representative of independent claim 18, claim 1 recites at least the following limitations:
concurrently utilizing, by the one or more hardware processors, a plurality of electronic machine learning models in tandem with an ensemble learning algorithm configured for creating classification data to: (a) classify, automatically by the one or more hardware processors, each of a first set of one or more variables in a data structure as having a relatively low impact on an identified composite service level metric (SLM); and (b) classify, automatically by the one or more hardware processors, each of a second set of multiple variables in the data structure as having a relatively high impact on the composite SLM,
wherein: the first set of one or more variables in the data structure differs from the second set of multiple variables in the data structure, the data structure is associated with one or both of the healthcare IT system and the medical care facility and stores historical service level delivery data, and an impact of a first variable in the first set of one or more variables in the data structure has a lower impact on the composite SLM than an impact of a second variable in the second set of multiple variables in the data structure;
constructing, the one or more hardware processors and based at least in part on reading by the one or more hardware processors results of the classifying of each of the second set of multiple variables in the data structure, an electronic representation of a partial dependence plot;
predicting, at the one or more hardware processors and using the partial dependence plot, a marginal contribution of a first variable relative to a second variable, the second variable (a) corresponding to the composite SLM and (b) being reflective of both a service level and an effectiveness of the healthcare IT system to empower the medical care facility in diagnosing and treating diseases, and the first variable (a) being identified from the second set of multiple variables in the data structure and (b) being an observable feature impacting the service level;
wherein a threshold associated with the first variable, (a) corresponds to a target probability of dissatisfaction associated with the second variable, and (b) is based on the partial dependence plot; and
dynamically allocating, without user intervention via the one or more hardware processors and an electronic communication interface associated with (a) the one or more hardware processors, (b) the healthcare IT system, and/or (c) the medical care facility, a set of electronic resources to a corresponding set of service areas wherein the allocating of the set of resources to the corresponding set of service areas is initiated by the one or more hardware processors based on the marginal contribution and in response to a first variable value, of a service instance, being within an identified distance to the threshold associated with the first variable, and wherein the first variable value being within the identified distance corresponds to the first variable value being within an alert zone of the threshold associated with the first variable.
Independent claim 9 recites at least the following claim limitations:
concurrently utilizing, by the one or more hardware processors, a plurality of electronic machine learning models in tandem with an ensemble learning algorithm configured for creating classification data to: (a) classify, automatically by the one or more hardware processors, each of a first set of one or more variables in a data structure as having a relatively low impact on an identified composite service level metric (SLM); and (b) classify, automatically by the one or more hardware processors, each of a second set of multiple variables in the data structure as having a relatively high impact on the composite SLM,
wherein: the first set of one or more variables in the data structure differs from the second set of multiple variables in the data structure, the data structure is associated with one or both of the healthcare IT system and the medical care facility and stores historical service level delivery data, and an impact of a first variable in the first set of one or more variables in the data structure has a lower impact on the composite SLM than an impact of a second variable in the second set of multiple variables in the data structure;
constructing, at the one or more hardware processors and based at least in part on reading by the one or more hardware processors results of the classifying of each of the second set of multiple variables in the data structure, an electronic representation of a partial dependence plot;
predicting, at the one or more hardware processors and using the partial dependence plot, a marginal contribution of a first variable relative to a second variable, the second variable (a) corresponding to the composite SLM and (b) being reflective of both a service level and an effectiveness of the healthcare IT system to empower the medical care facility in diagnosing and treating diseases, and the first variable (a) being identified from the second set of multiple variables in the data structure and (b) being an observable feature impacting the service level,
wherein a threshold associated with the first variable (a) corresponds to a target probability of dissatisfaction associated with the second variable and (b) is based on the partial dependence plot; and
dynamically allocating, without user intervention and via the one or more hardware processors and an electronic communication interface associated with (a) the one or more hardware processors, (b) the healthcare IT system, and/or (c) the medical care facility, a set of electronic resources to a corresponding set of service areas, wherein the allocating of the set of electronic resources to the corresponding set of service areas is initiated by the one or more hardware processors based on the marginal contribution and in response to a first variable value, of a service instance, being within an identified distance to the threshold associated with the first variable, and wherein the first variable value being within the identified distance corresponds to the first variable value being within an alert zone of the threshold associated with the first variable.
Under Step 1, independent claims 1, 9, and 18 recite at least one step or act, including constructing an electronic representation of a partial dependence plot. See MPEP 2106.03.
Under Step 2A Prong One, the limitations for concurrently utilizing a plurality of electronic machine learning models in tandem with an ensemble learning algorithm to classify variables, constructing an electronic representation of a partial dependence plot, predicting a marginal contribution of a first and second variable reflective of both a service level and an effectiveness of the healthcare IT system, and dynamically allocating a set of electronic resources within an identified distance to the threshold, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (comparing or categorizing information; an observation, evaluation, judgement, opinion, which could be performed as a mental process) because none of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea. See MPEP § 2106.04(a).
Regarding the limitations for recite “predicting… a marginal contribution of a first variable relative to a second variable,” predicting variables constitutes performing a mathematical concept, such as determining a mathematical relationship or performing a mathematical calculation. Therefore, these limitations are reasonably construed and falling within the mathematical concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2)(I).
The claim language related to the claimed allocation of resources is broadly and generically recited, such that the claimed technological improvement amounts to mere instructions to implement the abstract idea of analyzing performance data for service level risk assessment on a computer, or merely uses a computer as a tool to perform the abstract idea, as discussed in MPEP 2106.05(f). The determination of whether a performance metric is within a certain predetermined range of a risk threshold could be performed mentally or through the use of a pen and paper to draft the dependency plot and make data associations and mathematical correlations to determine risk.
Under Step 2A Prong Two, the judicial exception of claims 1 and 9 is not integrated into a practical application. In particular, the claims only recite a processor and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0033] states: “Each block in FIG. 7, and other processes described herein, comprises a computing process that may be performed using any combination of hardware, firmware, or software. For instance, various functions may be carried out by a processor executing instructions stored in memory.” The specification at paragraph [0049] states: “Referring to FIG. 8, an exemplary operating environment for implementing various aspects of the technologies described herein is shown and designated generally as computing device 800. Computing device 800 is but one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use of the technologies described herein.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Accordingly, the 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.
Ineligible example 47, claim 2 of the Subject Matter Eligibility Guidance is analogous to the machine learning model applied in claims 2 and 10 herein. While claims 1, 9, and 18 include a machine learning model used for creating classification data and generating a prediction, at Step 2A Prong One the machine learning related limitations fall within the mathematical concepts grouping of abstract ideas because the broadest reasonable interpretation of the limitation encompasses a mathematical relationship, mathematical formula or equation, or mathematical calculation. See e.g. MPEP 2106.04(a)(2)(I)(C). The machine learning model is merely a field of use application and does not include limitations that improve the applied technology. Further, the processor used to implement the abstract idea is recited at a high level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). A claim limitation that merely recites the idea of using artificial intelligence to achieve a solution or outcome described in the specification without reciting the details of how the solution or outcome is accomplished is insufficient to integrate a judicial exception into a practical application. The “ensemble machine learning model” and the “machine learning model” are used to generally apply the abstract idea without placing any limits on how model functions and do not include any details on how the determining of impact measures is accomplished. Therefore, the claimed machine learning models do not integrate a judicial exception.
While the amended claims include additional elements such as dynamically allocating a set of electronic resources to a corresponding set of service areas, and concurrently utilizing a plurality of electronic machine learning models in tandem with an ensemble learning algorithm configured for creating classification data, these additional elements do not integrate the abstract idea into a practical application and when considered individually and in combination do not amount to significantly more than the recited abstract idea. The claim language related to the claimed allocation of resources is broadly and generically recited, such that the claimed technological improvement amounts to mere instructions to implement the abstract idea of analyzing performance data for service level risk assessment on a computer, or merely uses a computer as a tool to perform the abstract idea, as discussed in MPEP 2106.05(f). Further, allocation is reasonably construed as assigning a resource or assigning a task to a resource because the claims fail to recite specific functions that are performed to implement the allocation or provisioning of resources in a manner that provides a technical solution to a technical problem that goes beyond data processing and generating an output in the form of an instruction or alert. The determination of whether a performance metric is within a certain predetermined range of a risk threshold could be performed mentally. The claim limitations fail to detail how the set of resources are allocated in a manner that provides a practical application of computing or another technical field.
Further, as claimed, “dynamically allocation, without user intervention and via the one or more hardware processors and an electronic communication interface” is construed as generating an output of the data analysis and manipulation on an interface for display in a manner that constitutes insignificant post solution activity because the recited functions do not add meaningful limitations beyond generally linking the abstract idea to the particular technological environment (use of a graphical user interface to display data transmitted by the processor).
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. The Specification does not provide additional details about the computer system/server that would distinguish it from any generic processing devices that communicate with one another in a network environment. Mere automation of manual processes (analyzing and manipulating known data to generate predictions, correlate calculated values to thresholds and plotted curves, and generating an alert or instruction to allocate resources) using generic computers does not constitute a patentable improvement in computer technology. Therefore, the claims are directed to a judicial exception, without significantly more.
Dependent claims 2-8, 10-17, and 19-34 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/ mathematical concept/ certain methods of organizing human behavior abstract idea by describing how the service level delivery data is analyzed, manipulated, and presented to a user. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly independent claim 18 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1 - 34 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Rao et al. (US 2019/0179927) - generating intelligent actionable information based on tracking and monitoring movements and lineage data across multiple database nodes within an enterprise system are described herein. Data within an enterprise system may be collected and monitored to generate real-time intelligent analytics and predictive insights for presentation. The changes and movements of each data across multiple database nodes within the enterprise system may be monitored, and deviations from a scheduled data flow associated with the data may be traced. Based on the monitoring and tracking of the changes and lineage of the data, performance metrics may be generated along with predictions and prescriptions to improve them. The performance metrics may be visualized via one or more performance reports in response to a user request.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623