Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 2/24/2026, Applicant responded on 5/20/2026. Amended claims 1, 13, 17.
Claims 1-20 are pending in this application have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/20/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 13, 17 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 13, 17 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…The claims, as amended herein, recite a process for applying a machine learning model to refine a computer-implemented procedure in manner that improves the processing time of the computer executing the computer-implemented procedure. See e.g., Applicant's Specification, as filed, [0020] ("Specification"). As claimed, the recited machine learning model is trained and then iteratively retrained using an improved loss function (e.g., the predictive metric data object) and optimization threshold which allow the machine learning model to improve the efficiency of computer-implemented procedure; thereby, improving the performance of the computer implementing the computer-implemented procedure. Specification, [0020]-[0021], [0024], & [0126]-[0129])....it is well established that machine learning models cannot be practically executed, or trained, within the human mind, and therefore cannot be considered mental processes. See e.g., Ex Parte Desjardins and Ex Parte Hannun. The same is true for a computer-implemented procedure, which explicitly requires implementation within a computer, not the human mind. As such, the claims do not recited an abstract idea under Prong 1, Step 2A, of the Alice/Mayo Framework, and even if they did - which they do not - they recite an improvement to technology, such that they integrate any abstract idea into a practical application under Prong 2, Step 2A of the Alice/Mayo Framework…Desjardins Memorandum provides two new examples of patent eligible subject matter that involve machine learning model. The second example, example (xiv), merely requires the following four elements: "[1] improvements to computer component or system performance [2] based upon adjustments to parameters of a [3] machine learning model [4] associated with tasks or workstreams." See Desjardins Memorandum, p. 4. As shown in the table below, the claimed elements, as supported by the Specification, clearly align with at least this example of subject matter eligibility…in an informative decision, the Patent Trial and Appeal Board (PTAB) held that a claim directed to speech recognition systems did not recite a mental process because the claim included steps such as "receiving predicted character probabilities from a trained neural network" that "are not steps that can be practically performed mentally." See e.g., Hannun, No. 2018-003323, p. 9-10. Like the claims of Hannun, claim 1 recites a machine learning technique that cannot as a practical matter be performed in the human mind…As amended, claim 1 recites a process for generating a predictive metric data object and training and retraining a machine learning model to reduce this predictive metric data object in order to improve the performance of a computer-implemented procedure. The human mind cannot practically (i) receive input data objects, (ii) generate input data object parameters, (iii) generate variance and weighting metrics, or (iv) train/retrain or store a machine learning model using the predictive metric data object as a loss function. Accordingly, no element of claim 1, as amended, under its broadest reasonable interpretation may be considered a mental process as defined by the MPEP… , although mathematical elements may be referenced by the claims, the claims are not directed to a mathematical concept. They are directed to a process for training a machine learning model to optimize a loss function and enhance the efficiency of a computer-implemented procedure. For at least these reasons, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101 because the claimed invention is not directed to a judicial exception under prong one of Step 2A…. The (1) machine learning model, (2) the training processing, including the new predictive metric data object that is used as a loss function for optimizing the model, and (3) the computer-implemented procedure recited by claim 1 are all additional elements. In combination, these additional elements improve both the functioning of a computer (e.g., by making the computer-implemented procedure more effect) and the technical field of machine learning (e.g., by providing a new loss function for a machine learning model). Thus, even if claim 1 were directed to an abstract idea-which, Applicant submits, it is not the claim recites a combination of additional elements that improves technology such that the claim as a whole integrates any alleged abstract idea into a practical application that is patent eligible under 35 U.S.C. § 101...The MPEP states that "[l]imitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include an improvement in the functioning of a computer, or an improvement to other technology or technical field." MPEP § 2106.04(d). Ex Parte Desjardines found that improvements to system performance based on the adjustment of the parameters of a machine learning model are sufficient to integrate an exception into a practical application. Ex Parte Desjardines, p. 8; see also Desjardins Memorandum, p. 4. Claim 1 recites an improved training technique for a machine learning model to improve the efficiency of executing a computer-implemented procedure within a computer… As amended, claim 1 recites a process for training a machine learning model to optimize the performance of a computer-implemented procedure as executed by a computer. As explained by the Specification, a computer-implemented procedure requires computing resources, including processing time. See Specification [0020]-[0021]. The recited predictive metric data object of claim 1 evaluates the overall efficiency of the computer-implemented procedure and the machine learning model uses the predictive metric data object as a loss function for iteratively improving the computer-implemented procedure through processing optimization actions. The computer-implemented procedure is executed by a computer. See Specification [0024] & [0126]- [0129]. For example, the claimed machine learning model is trained by reducing the predictive metric data object, thereby resulting in improved system performance based on adjustments to the parameters of the machine learning model. See Specification [0050], [0052], [0054], & [0127]- [0129]. Accordingly, the additional elements of claim 1 recite an improvement to the functionality of a computer and to machine learning technology (e.g., the training of a machine learning model). Therefore, Applicant respectfully submits that independent claim 1 recites patent eligible subject matter under 35 U.S.C. § 101 and requests withdrawal of the rejection to claim 1 (and the claims that depend therefrom) as well as allowance in due course...” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, unlike the Desjardins, the specification of Desjardins specifically discusses problems associated with and that arise during the training process when training deep neural networks and recurrent neural networks. Desjardins’ specification discloses, “[4] Some machine learning models are deep models that employ multiple layers of models to generate an output for a received input. For example, a deep neural network is a deep machine learning model that includes an output layer and one or more hidden layers that each apply a non-linear transformation to a received input to generate an output. However, machine learning models may be subject to "catastrophic forgetting" when trained on multiple tasks, losing knowledge of a previous task when a new task is learned. [5] Some neural networks are recurrent neural networks. A recurrent neural network is a neural network that receives an input sequence and generates an output sequence from the input sequence. In particular, a recurrent neural network uses some or all of the internal state of the network after processing a previous input in the input sequence in generating an output from the current input in the input sequence.”
Applicant’s own specification do not disclose any specific types of machine learning models, but rather discloses organizing human activities using mental processes and mathematical concepts applied with generic computing devices and with generically recited “machine learning models”, as shown here:
[0020] Embodiments of the present invention present new data processing techniques to improve data modeling and evaluation of robust, multi-faceted datasets. To do so, the present disclosure describes an empirical approach for generating comprehensible predictive metrics that accurately demonstrate efficiencies and/or inefficiencies with a developmental process. A developmental process may include a computer-implemented and/or real-world procedure that may be evaluated and improved based at least in part on historical embodiments of the procedure. For example, during each embodiment of the procedure, data may be generated that is indicative of one or more aspects of the procedure including, as examples, a processing time, a cost incurred, an advantage gained, and/or the like for a particular object associated with the embodiment of the developmental procedure. This information may be aggregated and stored in complex, robust, multi-faceted data sets that lack processing techniques, such as those described herein, for accurately evaluating and/or improving certain aspects of the developmental process.
[0021] Embodiments of the present invention present new data structures for diagnosing, targeting, monitoring, and/or otherwise evaluating/improving one or more aspects of a developmental process. The new data structure may include a holistic predictive metric data object that may be indicative of a variation in the developmental process. The variation in the developmental process may be indicative of difference in the way different objects are handled by the developmental process that may lead to inefficiencies and/or provide insights on how to optimize the developmental process. The predictive metric data object may quantify the variation of the developmental process and holistically measure the impact of the variation on an entity's overall activities. By using the techniques described herein, a robust data set may be transformed into one comprehensible predictive metric data object that accurately represents a relative efficiency of the developmental process.
[0022] Typical techniques for evaluating the efficiency of a developmental process may overly rely on unreliable aspects of a robust data set or compare aspects of a robust data set over time without considering unavoidable time period incurred changes. For instance, in one example embodiment, a developmental process may include a clinical process in which individuals are administered care during a clinical encounter. Typical approaches for evaluating the variation in administered care may measure variations in a clinical encounter's length of stay. When a clinical encounter's stay exceeds an expected length of stay, it is assumed that excess days indicate inefficiencies with the clinical process. While comprehensible, these approaches fail to consider that the length of stay is a function of multiple factors and may be an unreliable determinative of care variation and, as a result, a poor indication of efficiency in a developmental process. Other typical approaches compare the costs for a clinical encounter to previous costs for a similar clinical encounters during a prior time period. This approach is also unreliable as it assumes that a mix of cases in every measurement period is the same.
[0023] The predictive metric data object of the present disclosure provides a technical improvement that improves the reliability and comprehensibility of previous data modeling techniques by directly comparing a plurality of instances of a developmental process against each other across a number of different parameters. As one particular example, the predictive metric data object of the present disclosure may be defined by (i) a deviation (e.g., a number of standard deviations) between a cost parameter of a first instance from a median cost parameter for a group of instances, and (ii) an advantage contribution (e.g., a revenue attributed to) of an instance to a total advantage incurred from each instance of the developmental process. In a clinical context, for example, the predictive metric data object may include a measure of the degree of dispersion in clinical care adjusted for revenue contribution to demonstrate opportunity for further improvement. This single unit of measurement may consider multiple influencing components, weighted based at least in part on variable importance and its contribution to health system revenue, and creates a normalized method of comparing departments within a hospital as well as peer group hospitals. As described herein, the predictive metric data object may be measured across an entire hospital or stratified into subcomponents like service lines, diagnosis related groupings, cost centers, etc. to help evaluate areas where a development process is working well and where it has no impact or negative return.
[0024] In this manner, the new data processing techniques of the present disclosure improve quality, while reducing costs and boosting efficiencies for developmental processes such as clinical processes. This is achieved by evaluating and optimizing variation between different instances of the developmental process. The variation solutioning techniques of the present disclosure enable a predictive entity to (i) identify clusters of instances where variation in a developmental process is chronic, (ii) guide predictive entities to further optimize one or more different aspects of the developmental process based at least in part on targeted clusters of variation, and (iii) iteratively evaluate and monitor variation in the developmental process over time. Moreover, the predictive metric data object may be leveraged by predictive entities to measure quality, star ratings, payer initiatives to control costs, facility recommendations, and/or the like.
[0025] The term “input data object” may refer to a data entity that describes a data point of interest for a process optimization scheme. The input data object may be associated with a developmental process. As examples, the input data object may identify a case and/or record of clinical operations that may be optimized through the data modeling and processing techniques described herein. The input data object, for example, may include a clinical encounter and/or a clinical encounter.
[0026] The term “input data object parameter” may refer to a data entity that describes an attribute of an input data object. The input data object may be associated with a plurality of input data object parameters that may describe a plurality of attributes for the input data object. The plurality of attributes may include one or more characteristics that may be relevant to a developmental process. By way of example, the parameters may include contextual attributes for the input data object and/or predictive metric attributes for the input data object.
[0027] The term “predictive entity” may refer to a data entity that describes a common attribute for a plurality of input data objects involved in a developmental process. The predictive entity may be based at least in part on the developmental process. As one example, the developmental process may include clinical operations and the predictive entity may be a hospital and/or health care provider that is configured to facilitate the clinical operations. As other examples, the developmental process may include an object management process and the predictive entity may be an object provider and/or producer that is configured to facilitate the object management process.
[0028] The term “contextual attribute” may refer to a data entity that describes a contextual component of an input data object. An input data object may include one or more contextual attributes that may describe contextual information for grouping one or more different input data objects into input data object cohorts. By way of example, an input data object may include a clinical encounter for an individual. In such an example, a contextual attribute may include the individual's age, an attending physician, a relevant disease, comorbidities, hospital, and/or like. In some embodiments, a contextual attribute may include one or more classifications associated with an input data object. As examples, the classifications may include Medicare Severity Diagnosis. Related Groups (MSDRG), All Patient Refined Diagnosis Related Groups (APRDRG), Severity of Illness (SOI), principal/primary diagnosis/procedure, and/or the like.
[0029] The term “predictive metric attribute” may refer to a data entity that describes a predictive component of an input data object. A predictive component of an input data object may be based at least in part a developmental process. The predictive component may be an indicator of variation in the developmental process. As one example, in a clinical context, a predictive metric attribute may be predictive of care variation for the developmental process. Example predictive metric attributes may include a cost parameter (e.g., a total direct cost associated with the input data object), a timing parameter (e.g., a length of stay for the input data object), an advantage parameter (e.g., a revenue accrued by the input data object), and/or the like.
[0030] The term “input data object cohort” may refer to a subset of a plurality of input data objects. Each of the subset of input data objects may include one or more similar contextual attributes. By way of example, an input data object cohort may be defined by one or more input data object classifications. For instance, the subset of input data objects may include one or more input data objects that are each associated with a MSDRG classification, an APRDRG, an SOI classification, principal/primary diagnosis/procedure classification, and/or the like. In one embodiment, for example, an input data object cohort may be defined based at least in part on a MSDRG classification. In addition, or alternatively, an input data object cohort may be defined based at least in part on an APRDRG and SOI classification, an MSDRG and SOI, a principal/primary diagnosis/procedure classification, and/or the like.
[0031] The term “input data object cohort parameter” may refer to a data entity that describes a generated predictive component of an input data object cohort. A predictive component of an input data object cohort may be based at least in part a developmental process. In some embodiments, an input data object cohort parameter may be based at least in part on a plurality of respective predictive metric attributes for each of respective input data object of an input data object cohort. For instance, an input data object parameter may include an aggregate predictive attribute and/or one or more statistical measurements for the plurality of respective predictive metric attributes. By way of example, an input data object cohort parameter may include a variance-based input data object cohort parameter and/or a timing-based input data object cohort parameter. The variance-based input data object cohort parameter may include a median cost parameter for an input data object cohort. The median cost parameter may identify a median direct cost for the subset of input data objects of an input data object cohort. The timing-based input data object cohort parameter may include a median time parameter for an input data object cohort. The median time parameter may identify a median length of stay for the subset of input data objects of an input data object cohort.
[0050] The system 100 includes a storage subsystem 108 configured to store at least a portion of the data utilized by the predictive data analysis system 101. The predictive data analysis computing entity 106 may be in communication with the external computing entities 102A-N. The predictive data analysis computing entity 106 may be configured to: (i) train one or more machine learning models based on a training data store stored in the storage subsystem 108, (ii) store trained machine learning models as part of a model definition data store of the storage subsystem 108, (iii) utilize trained machine learning models to perform an action, and/or the like.
[0051] In one example, the system predictive data analysis computing entity 106 may be configured to generate a prediction, classification, and/or any other data insight based on data provided by an external computing entity such as external computing entity 102A, external computing entity 102B, and/or the like.
[0052] The storage subsystem 108 may be configured to store the model definition data store and the training data store for one or more machine learning models. The predictive data analysis computing entity 106 may be configured to receive requests and/or data from at least one of the external computing entities 102A-N, process the requests and/or data to generate outputs (e.g., predictive outputs, classification outputs, and/or the like), and provide the outputs to at least one of the external computing entities 102A-N. In some embodiments, the external computing entity 102A, for example, may periodically update/provide raw and/or processed input data to the predictive data analysis system 101. The external computing entities 102A-N may further generate user interface data (e.g., one or more data objects) corresponding to the outputs and may provide (e.g., transmit, send, and/or the like) the user interface data corresponding with the outputs for presentation to the external computing entity 102A (e.g., to an end-user).
[0127] In some embodiments, the predictive data analysis computing entity 106 may include a machine learning model for generating the processing optimization action based at least in part on the developmental process, the input data objects, the input data object cohort, and/or the one or more shared cluster attributes. The machine learning model may include a predictive model that is trained over training data to reduce the cohort predictive metric data object for a respective input data object cohort. By way of example, the cohort predictive metric data object may include a loss function that is optimized by the machine learning model.
Since Applicant’s own specification do not disclose any specific types of machine learning model, under the broadest reasonable interpretation, the disclosed “developmental process” and “machine learning model” in Applicant’s specification can be interpretated to be utilizing a predictive mathematical optimization model with loss function to optimize human healthcare providers’ actions for human clinical developmental processes in a hospital.
Thus, unlike Desjardins Memo examples and Hannun, the present claims and the argued elements, recite and direct to, …a process for generating a predictive metric data…to optimize this predictive metric data object that improves the performance of a developmental process…to optimize a loss function and enhance the efficiency of a developmental process…to evaluate and improve the performance of a developmental process…, is a problem directed to mental process (i.e. human training and using mathematical modeling with mathematical weighing, mathematical variance, mathematical prediction, mathematical loss function, to evaluate and develop more efficient mental methods and mental processes, such as human utilizing a predictive mathematical optimization model with loss function to optimize human healthcare providers’ actions for human clinical developmental processes in a hospital), organizing human activities (i.e. human training and using mathematical modeling with mathematical weighing, mathematical variance, mathematical prediction, mathematical loss function, to evaluate and develop more efficient mental methods and mental processes, such as human utilizing a predictive mathematical optimization model with loss function to optimize human healthcare providers’ actions for human clinical developmental processes in a hospital), mathematical concepts (i.e. human training and using mathematical modeling with mathematical weighing, mathematical variance, mathematical prediction, mathematical loss function, to evaluate and develop more efficient mental methods and mental processes, such as human utilizing a predictive mathematical optimization model with loss function to optimize human healthcare providers’ actions for human clinical developmental processes in a hospital), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer and machine learning. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and WURC).
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Thus, Appellant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes, mathematical concepts, and certain methods of organizing human activities implemented using generic computer components.
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process, mathematical concepts and organizing human activities, generally linked to a technical environment with additional elements recited at a high level of generality applying the recited abstract ideas and performing extra solution activities. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
[A] claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
iv. Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone, TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747;
vi. Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
Examples that the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
iv. Delivering broadcast content to a portable electronic device such as a cellular telephone, when claimed at a high level of generality, Affinity Labs of Tex. v. Amazon.com, 838 F.3d 1266, 1270, 120 USPQ2d 1210, 1213 (Fed. Cir. 2016); Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
v. A general method of screening emails on a generic computer, Symantec, 838 F.3d at 1315-16, 120 USPQ2d at 1358-59;
vi. An advance in the informational content of a download for streaming, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1263, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016); and
vii. Selecting one type of content (e.g., FM radio content) from within a range of existing broadcast content types, or selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of well-known, routine, conventional functions performed by the hardware, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016).
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). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. 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);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication to US Patent Publication to US20210366618A1 to Schoedl et al., (hereinafter referred to as “Schoedl”) in view of US Patent Publication to US20220207425A1 to Nakae et al., (hereinafter referred to as “Nakae”).
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 13, 17,
Claim 1, (similarly 13, 17): A computer-implemented method comprising:
receiving, by one or more processors, a plurality of input data objects associated with a computer-implemented procedure and a machine learning model trained to generate a processing optimization action for modifying the computer-implemented procedure, wherein an input data object of the plurality of input data objects comprises a historical record of a plurality of operations for the computer-implemented procedure and is associated with a predictive entity configured to perform the plurality of operations;
generating, by the one or more processors, an input data object cohort from the plurality of input data objects by grouping a subset of the plurality of input data objects associated with the predictive entity;
generating, by the one or more processors, a weighting-based input data object parameter for the input data object cohort by aggregating a set of reward parameters within the input data object cohort;
generating, by the one or more processors, a variance-based input data object cohort parameter for the input data object cohort by aggregating a set of cost parameters within the input data object cohort;
generating, by the one or more processors, a predictive variance metric for an input data object of the input data object cohort based at least in part on the variance-based input data object cohort parameter, wherein the predictive variance metric comprises a distance between a cost parameter of the input data object and the variance-based input data object cohort parameter;
generating, by the one or more processors, a predictive weighting metric for the input data object based at least in part on the weighting-based input data object parameter, wherein the predictive weighting metric comprises a ratio between a reward parameter of the input data object and the weighting-based input data object parameter;
generating, by the one or more processors, a predictive metric data object that represents an indication of efficiency of the computer-implemented procedure based at least in part on the predictive variance metric and the predictive weighting metric for the input data object;
retraining, by the one or more processors, the machine learning model to reduce the predictive metric data object, wherein the machine learning model is trained over a plurality of iterations until an optimization threshold is achieved; and
storing, by the one or more processors, the machine learning model in association with the computer-implemented procedure.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 13, 17.
The prior art rejection is hereby withdrawn.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1, 13, 17 recite “…a set of reward parameters… a ratio between a reward parameter of the input data object and the weighting-based input data object parameter…”. However, Applicant’s Specification does not expressly or inherently require, …a set of reward parameters… a ratio between a reward parameter of the input data object and the weighting-based input data object parameter…, as required by claim 1, 13, 17.
In order to satisfy the written description requirement, each claim limitation must be expressly or inherently supported by the disclosure. MPEP 2163 (emphasis added). "The 'written description' requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor's obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent's term. See MPEP 2163. For claims directed toward computer-implemented functions, like the presently claimed invention, "[i]f the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made." MPEP 2161.01.
Applicant’s specification discloses:
[0037] By way of example, the predictive metric attribute may include an advantage metric (e.g., a revenue accrued by, etc.) for the input data object and the weighting-based input data object parameter may be determined based at least in part on an aggregate of a plurality of advantage parameters respectfully associated with each of the plurality of input data objects. The predictive weighting metric may be generated based at least in part on an advantage ratio between the advantage parameter and the aggregate advantage parameter. In some embodiments, for example, the weighting-based input data object parameter may include a total revenue accrued by the plurality of input data objects for the predictive entity. The predictive weighting metric may be indicative of a revenue contribution of an input data object as a percentage of a total revenue from the plurality of input data objects.
[0074] The predictive weighting metric may be generated based at least in part on an advantage ratio between the advantage parameter and the aggregate advantage parameter. For example, the predictive data analysis computing entity 106 may determine an advantage ratio between the advantage parameter of the input data object and the aggregate advantage parameter of the plurality of input data objects. The predictive data analysis computing entity 106 may generate the predictive weighting metric for the input data object based at least in part on the advantage ratio. In this way, the predictive weighting metric may be used to weigh the input data object according to the input data object's contribution to a desired advantage. In some embodiments, the predictive weighting metric for the input data object may be combined with predictive weighting metrics generated for each of the subset of input data objects of the input data object cohort to determine a combined advantage achieved by the input data object cohort. The combined advantage may be utilized to prioritize processing
However, the paragraph and figures does not expressly or inherently disclose “…a set of reward parameters… a ratio between a reward parameter of the input data object and the weighting-based input data object parameter….”, as required by claim 1, 13, 17.
Accordingly, the specification does not provide express or inherent support for “…a set of reward parameters… a ratio between a reward parameter of the input data object and the weighting-based input data object parameter…” as recited in claim 1, 13, 17.
Dependent claims do not cure the aforementioned deficiencies of claim 1, 13, 17, and thus, dependent claims are rejected for the reasons set forth above regarding claim 1, 13, 17 as a result.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 13, 17) recite, “A … method comprising:
receiving, by …, a plurality of input data objects associated with a … procedure and a … model trained to generate a processing optimization action for modifying the … procedure, wherein an input data object of the plurality of input data objects comprises a historical record of a plurality of operations for the … procedure and is associated with a predictive entity configured to perform the plurality of operations;
generating, by the …, an input data object cohort from the plurality of input data objects by grouping a subset of the plurality of input data objects associated with the predictive entity;
generating, by the …, a weighting-based input data object parameter for the input data object cohort by aggregating a set of reward parameters within the input data object cohort;
generating, by the …, a variance-based input data object cohort parameter for the input data object cohort by aggregating a set of cost parameters within the input data object cohort;
generating, by the …, a predictive variance metric for an input data object of the input data object cohort based at least in part on the variance-based input data object cohort parameter, wherein the predictive variance metric comprises a distance between a cost parameter of the input data object and the variance-based input data object cohort parameter;
generating, by the …, a predictive weighting metric for the input data object based at least in part on the weighting-based input data object parameter, wherein the predictive weighting metric comprises a ratio between a reward parameter of the input data object and the weighting-based input data object parameter;
generating, by the …, a predictive metric data object that represents an indication of efficiency of the … procedure based at least in part on the predictive variance metric and the predictive weighting metric for the input data object;
retraining, by the …, the … model to reduce the predictive metric data object, wherein the … model is trained over a plurality of iterations until an optimization threshold is achieved; and
storing, by the …., the … model in association with the … procedure.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …receiving, by …, a plurality of input data objects associated with a … procedure and a … model trained to generate a processing optimization action for modifying the … procedure, wherein an input data object of the plurality of input data objects comprises a historical record of a plurality of operations for the … procedure and is associated with a predictive entity configured to perform the plurality of operations; generating, by the …, an input data object cohort from the plurality of input data objects by grouping a subset of the plurality of input data objects associated with the predictive entity; generating, by the …, a weighting-based input data object parameter for the input data object cohort by aggregating a set of reward parameters within the input data object cohort; generating, by the …, a variance-based input data object cohort parameter for the input data object cohort by aggregating a set of cost parameters within the input data object cohort; generating, by the …, a predictive variance metric for an input data object of the input data object cohort based at least in part on the variance-based input data object cohort parameter, wherein the predictive variance metric comprises a distance between a cost parameter of the input data object and the variance-based input data object cohort parameter; generating, by the …, a predictive weighting metric for the input data object based at least in part on the weighting-based input data object parameter, wherein the predictive weighting metric comprises a ratio between a reward parameter of the input data object and the weighting-based input data object parameter; generating, by the …, a predictive metric data object that represents an indication of efficiency of the … procedure based at least in part on the predictive variance metric and the predictive weighting metric for the input data object; retraining, by the …, the … model to reduce the predictive metric data object, wherein the … model is trained over a plurality of iterations until an optimization threshold is achieved; and storing, by the …., the … model in association with the … procedure…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations; therefore, the claims recite a mental process.
Further, the limitations regarding, … receiving, by …, a plurality of input data objects associated with a … procedure and a … model trained to generate a processing optimization action for modifying the … procedure, wherein an input data object of the plurality of input data objects comprises a historical record of a plurality of operations for the … procedure and is associated with a predictive entity configured to perform the plurality of operations; generating, by the …, an input data object cohort from the plurality of input data objects by grouping a subset of the plurality of input data objects associated with the predictive entity; generating, by the …, a weighting-based input data object parameter for the input data object cohort by aggregating a set of reward parameters within the input data object cohort; generating, by the …, a variance-based input data object cohort parameter for the input data object cohort by aggregating a set of cost parameters within the input data object cohort; generating, by the …, a predictive variance metric for an input data object of the input data object cohort based at least in part on the variance-based input data object cohort parameter, wherein the predictive variance metric comprises a distance between a cost parameter of the input data object and the variance-based input data object cohort parameter; generating, by the …, a predictive weighting metric for the input data object based at least in part on the weighting-based input data object parameter, wherein the predictive weighting metric comprises a ratio between a reward parameter of the input data object and the weighting-based input data object parameter; generating, by the …, a predictive metric data object that represents an indication of efficiency of the … procedure based at least in part on the predictive variance metric and the predictive weighting metric for the input data object; retraining, by the …, the … model to reduce the predictive metric data object, wherein the … model is trained over a plurality of iterations until an optimization threshold is achieved; and storing, by the …., the … model in association with the … procedure…, under the broadest reasonable interpretation, is human training and using mathematical modeling with mathematical weighing, mathematical variance, mathematical prediction, mathematical loss function, to evaluate and develop more efficient mental methods and mental processes, such as human utilizing a predictive mathematical optimization model with loss function to optimize human healthcare providers’ actions for human clinical developmental processes in a hospital, which is managing human behaviors and relationships, thus, the claims recite organizing human activities.
Furthermore, …receiving, by …, a plurality of input data objects associated with a … procedure and a … model trained to generate a processing optimization action for modifying the … procedure, wherein an input data object of the plurality of input data objects comprises a historical record of a plurality of operations for the … procedure and is associated with a predictive entity configured to perform the plurality of operations; generating, by the …, an input data object cohort from the plurality of input data objects by grouping a subset of the plurality of input data objects associated with the predictive entity; generating, by the …, a weighting-based input data object parameter for the input data object cohort by aggregating a set of reward parameters within the input data object cohort; generating, by the …, a variance-based input data object cohort parameter for the input data object cohort by aggregating a set of cost parameters within the input data object cohort; generating, by the …, a predictive variance metric for an input data object of the input data object cohort based at least in part on the variance-based input data object cohort parameter, wherein the predictive variance metric comprises a distance between a cost parameter of the input data object and the variance-based input data object cohort parameter; generating, by the …, a predictive weighting metric for the input data object based at least in part on the weighting-based input data object parameter, wherein the predictive weighting metric comprises a ratio between a reward parameter of the input data object and the weighting-based input data object parameter; generating, by the …, a predictive metric data object that represents an indication of efficiency of the … procedure based at least in part on the predictive variance metric and the predictive weighting metric for the input data object; retraining, by the …, the … model to reduce the predictive metric data object, wherein the … model is trained over a plurality of iterations until an optimization threshold is achieved; and storing, by the …., the … model in association with the … procedure…, recite mathematical concepts.
Accordingly, the claims recite a mental process, organizing human activities, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 13, 17: computer-implemented, one or more processors, computer-implemented, machine learning, A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to, One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to, machine learning
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receiving …generating… input…storing... generate a processing optimization action for modifying,,,”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receiving…generating… input… storing…”, data output – “…generating… generate a processing optimization action for modifying”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0014] FIG. 2 provides an example predictive data analysis computing entity 106 in accordance with some embodiments discussed herein. In general, the terms computing entity, computer, entity, device, system, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, notebooks, laptops, distributed systems, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, steps/operations, and/or processes described herein. Such functions, steps/operations, and/or processes may include, for example, transmitting, receiving, operating on, processing, displaying, storing, determining, creating/generating, monitoring, evaluating, comparing, and/or similar terms used herein interchangeably. In one embodiment, these functions, steps/operations, and/or processes may be performed on data, content, information, and/or similar terms used herein interchangeably.
[0015] The predictive data analysis computing entity 106 may include a network interface 208 for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein interchangeably that may be transmitted, received, operated on, processed, displayed, stored, and/or the like.
[0016] In one embodiment, the predictive data analysis computing entity 106 may include or be in communication with a processing element 202 (also referred to as processors, processing circuitry, and/or similar terms used herein interchangeably) that communicate with other elements within the predictive data analysis computing entity 106 via a bus, for example. As will be understood, the processing element 202 may be embodied in a number of different ways including, for example, as at least one processor/processing apparatus, one or more processors/processing apparatuses, and/or the like.
[0017] For example, the processing element 202 may be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, coprocessing entities, application-specific instruction-set processors (ASIPs), microcontrollers, and/or controllers. Further, the processing element 202 may be embodied as one or more other processing devices or circuitry. The term circuitry may refer to an entirely hardware embodiment or a combination of hardware and computer program products. Thus, the processing element 202 may be embodied as integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), hardware accelerators, other circuitry, and/or the like.
[0018] As will therefore be understood, the processing element 202 may be configured for a particular use or configured to execute instructions stored in one or more memory elements including, for example, one or more volatile memories 206 and/or non-volatile memories 204202. As such, whether configured by hardware or computer program products, or by a combination thereof, the processing element 202 may be capable of performing steps or operations according to embodiments of the present disclosure when configured accordingly. The processing element 202, for example in combination with the one or more volatile memories 206 and/or or non-volatile memories 204, may be capable of implementing one or more computer-implemented methods described herein. In some embodiments, the predictive data analysis computing entity 106 may include a computing apparatus, the processing element 202 may include at least one processor of the computing apparatus, and the one or more volatile memories 206 and/or non-volatile memories 204 may include at least one memory including program code. The at least one memory and the program code may be configured to, upon execution by the at least one processor, cause the computing apparatus to perform one or more steps/operations described herein.
[0019] The non-volatile memories 204 (also referred to as non-volatile storage, memory, memory storage, memory circuitry, media, and/or similar terms used herein interchangeably) may include at least one non-volatile memorydevice204, including but not limited to hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like.
[0020] As will be recognized, the non-volatile memories 204 may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like. The term database, database instance, database management system, and/or similar terms used herein interchangeably may refer to a collection of records or data that is stored in a computer-readable storage medium using one or more database models, such as a hierarchical database model, network model, relational model, entity-relationship model, object model, document model, semantic model, graph model, and/or the like. [0021] The one or more volatile memories (also referred to as volatile storage, memory, memory storage, memory circuitry, media, and/or similar terms used herein interchangeably) may include at least one volatile memory 206device, including but not limited to RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like.
[0022] As will be recognized, the volatile memories 206 may be used to store at least portions of the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processing element 202. Thus, the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like may be used to control certain embodiments of the operation of the predictive data analysis computing entity 106 with the assistance of the processing element 202.
[0023] As indicated, in one embodiment, the predictive data analysis computing entity 106 may also include the network interface 208 for communicating with various computing entities, such as by communicating data, content, information, and/or the like that may be transmitted, received, operated on, processed, displayed, stored, and/or the like. Such communication data may be executed using a wired data transmission protocol, such as fiber distributed data interface (FDDI), digital subscriber line (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay, data over cable service interface specification (DOCSIS), or any other wired transmission protocol. Similarly, the predictive data analysis computing entity 106 may be configured to communicate via wireless client communication networks using any of a variety of protocols, such as general packet radio service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1X (1xRTT), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR) protocols, near field communication (NFC) protocols, Wibree, Bluetooth protocols, wireless universal serial bus (USB) protocols, and/or any other wireless protocol.
[0024] FIG. 3 provides an example external computing entity 102A in accordance with some embodiments discussed herein. In general, the terms device, system, computing entity, entity, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, steps/operations, and/or processes described herein. The external computing entities 102A-N may be operated by various parties. As shown in FIG. 3, the external computing entity 102A may include an antenna 312, a transmitter 304 (e.g., radio), a receiver 306 (e.g., radio), and/or an external entity processing element 308 (e.g., CPLDs, microprocessors, multi-core processors, coprocessing entities, ASIPs, microcontrollers, and/or controllers) that provides signals to and receives signals from the transmitter 304 and the receiver 306, correspondingly. As will be understood, the external entity processing element 308 may be embodied in a number of different ways including, for example, as at least one processor/processing apparatus, one or more processors/processing apparatuses, and/or the like as described herein with reference the processing element 202.
[0025] The signals provided to and received from the transmitter 304 and the receiver 306, correspondingly, may include signaling information/data in accordance with air interface standards of applicable wireless systems. In this regard, the external computing entity 102A may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More particularly, the external computing entity 102A may operate in accordance with any of a number of wireless communication standards and protocols, such as those described above with regard to the predictive data analysis computing entity 106. In a particular embodiment, the external computing entity 102A may operate in accordance with multiple wireless communication standards and protocols, such as UMTS, CDMA2000, 1xRTT, WCDMA, GSM, EDGE, TD- SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly, the external computing entity 102A may operate in accordance with multiple wired communication standards and protocols, such as those described above with regard to the predictive data analysis computing entity 106 via an external entity network interface 320.
[0026] Via these communication standards and protocols, the external computing entity 102A may communicate with various other entities using means such as Unstructured Supplementary Service Data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual- Tone Multi-Frequency Signaling (DTMF), and/or Subscriber Identity Module Dialer (SIM dialer). The external computing entity 102A may also download changes, add-ons, and updates, for instance, to its firmware, software (e.g., including executable instructions, applications, program modules), operating system, and/or the like.
[0027] According to one embodiment, the external computing entity 102A may include location determining embodiments, devices, modules, functionalities, and/or the like. For example, the external computing entity 102A may include outdoor positioning embodiments, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data. In one embodiment, the location module may acquire data such as ephemeris data, by identifying the number of satellites in view and the relative positions of those satellites (e.g., using global positioning systems (GPS)). The satellites may be a variety of different satellites, including Low Earth Orbit (LEO) satellite systems, Department of Defense (DOD) satellite systems, the European Union Galileo positioning systems, the Chinese Compass navigation systems, Indian Regional Navigational satellite systems, and/or the like. This data may be collected using a variety of coordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes, Seconds (DMS); Universal Transverse Mercator (UTM); Universal Polar Stereographic (UPS) coordinate systems; and/or the like. Alternatively, the location information/data may be determined by triangulating a position of the external computing entity 102A in connection with a variety of other systems, including cellular towers, Wi-Fi access points, and/or the like. Similarly, the external computing entity 102A may include indoor positioning embodiments, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, time, date, and/or various other information/data. Some of the indoor systems may use various position or location technologies including RFID tags, indoor beacons or transmitters, Wi-Fi access points, cellular towers, nearby computing devices (e.g., smartphones, laptops) and/or the like. For instance, such technologies may include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or the like. These indoor positioning embodiments may be used in a variety of settings to determine the location of someone or something to within inches or centimeters.
[0028] The external computing entity 102A may include a user interface 316 (e.g., a display, speaker, and/or the like) that may be coupled to the external entity processing element 308. In addition, or alternatively, the external computing entity 102A may include a user input interface 319 (e.g., keypad, touch screen, microphone, and/or the like) coupled to the external entity processing element 308).
[0029] For example, the user interface 316 may be a user application, browser, and/or similar words used herein interchangeably executing on and/or accessible via the external computing entity 102A to interact with and/or cause the display, announcement, and/or the like of information/data to a user. The user input interface 318 may comprise any of a number of input devices or interfaces allowing the external computing entity 102A to receive data including, as examples, a keypad (hard or soft), a touch display, voice/speech interfaces, motion interfaces, and/or any other input device. In embodiments including a keypad, the keypad may include (or cause display of) the conventional numeric (0-9) and related keys (#, *, and/or the like), and other keys used for operating the external computing entity 102A and may include a full set of alphabetic keys or set of keys that may be activated to provide a full set of alphanumeric keys. In addition to providing input, the user input interface 318 may be used, for example, to activate or deactivate certain functions, such as screen savers, sleep modes, and/or the like.
[0030] The external computing entity 102A may also include one or more external entity non- volatile memories 322 and/or one or more external entity volatile memories 324, which may be embedded within and/or may be removable from the external computing entity 102A. As will be understood, the external entity non-volatile memories 322 and/or the external entity volatile memories 324 may be embodied in a number of different ways including, for example, as described herein with reference the non-volatile memories 204 and/or the external volatile memories 206.
[0042] As described below, various embodiments of the present invention leverage robust data processing techniques to make important technical contributions to data and data processing intensive developmental processes.
[0043] FIG. 4 provides a flowchart diagram of an example process 402 for an automatic data processing scheme for evaluating robust data sets to optimize procedure efficiency in accordance with some embodiments discussed herein. The dataflow diagram depicts an automatic data processing scheme for generating insights for a developmental process based at least in part on a plurality of input data objects associated with the developmental process. The automatic data processing scheme may be implemented by one or more computing device(s) and/or system(s) described herein. For example, the predictive data analysis computing entity 106 may utilize the automatic data processing scheme to overcome the various limitations with conventional data modeling, processing, and evaluative techniques.
[0050] The system 100 includes a storage subsystem 108 configured to store at least a portion of the data utilized by the predictive data analysis system 101. The predictive data analysis computing entity 106 may be in communication with the external computing entities 102A-N. The predictive data analysis computing entity 106 may be configured to: (i) train one or more machine learning models based on a training data store stored in the storage subsystem 108, (ii) store trained machine learning models as part of a model definition data store of the storage subsystem 108, (iii) utilize trained machine learning models to perform an action, and/or the like.
[0051] In one example, the system predictive data analysis computing entity 106 may be configured to generate a prediction, classification, and/or any other data insight based on data provided by an external computing entity such as external computing entity 102A, external computing entity 102B, and/or the like.
[0052] The storage subsystem 108 may be configured to store the model definition data store and the training data store for one or more machine learning models. The predictive data analysis computing entity 106 may be configured to receive requests and/or data from at least one of the external computing entities 102A-N, process the requests and/or data to generate outputs (e.g., predictive outputs, classification outputs, and/or the like), and provide the outputs to at least one of the external computing entities 102A-N. In some embodiments, the external computing entity 102A, for example, may periodically update/provide raw and/or processed input data to the predictive data analysis system 101. The external computing entities 102A-N may further generate user interface data (e.g., one or more data objects) corresponding to the outputs and may provide (e.g., transmit, send, and/or the like) the user interface data corresponding with the outputs for presentation to the external computing entity 102A (e.g., to an end-user).
[0127] In some embodiments, the predictive data analysis computing entity 106 may include a machine learning model for generating the processing optimization action based at least in part on the developmental process, the input data objects, the input data object cohort, and/or the one or more shared cluster attributes. The machine learning model may include a predictive model that is trained over training data to reduce the cohort predictive metric data object for a respective input data object cohort. By way of example, the cohort predictive metric data object may include a loss function that is optimized by the machine learning model.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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