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 .
Notice to Applicant
This application, 18/282,401 is a 371 of PCT/CN2019/113675 filed on 06/02/2023.
Status of the Claims
Claims 14-15, 17-24 and 34 will be examined in this patent application. Claims 25-33 have been withdrawn from consideration. Claims 1-13 and 16 have been canceled. Claim 14 has been amended and Claim 34 is new.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and newly cited art.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that the claims do not recite an abstract idea under Step 2A Prong 1, Examiner respectfully disagrees. The claim limitations as currently written still recite abstract ideas even with the mere generic recitation of a machine learning model recited in its generic capacity and generic functionality. Examiner will also remind applicant, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Applicant’s arguments that the claims recite a specific technical solution to a specific technical problem, Examiner respectfully disagrees. The limitations are mere instructions to implement the abstract idea on a computer and further uses a computer (ex: machine learning) as a tool to perform the abstract idea. Examiner will note an important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology. MPEP 2106.04(a) and 2106.05(a) provide a detailed explanation of how to perform this analysis. In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. In analyzing the specification, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, (ref: 2106.04(d)(1)).
Regarding Enfish, the Court found the claims to be 101 eligible because the claims claimed a specific asserted improvement in computer capabilities (i.e, the self-referential table for computer database). To make their determination of whether the claims are directed to an improvement in existing computer technology, the court looked to the teachings of the specification. The court identified the specification's teachings that the claimed invention achieves other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. On the other hand, here, the current claims are not directed to improving a computer or technological process. The elements presented do not improve the functioning of the computer itself or another technical field. These claimed features are generic components of the computer itself. The claims do not claim anything specific or that differentiates the limitations claimed from limitation of a generic computer. Therefore, any improvements claimed by the Applicant is an inherent quality of the linking of the abstract idea to these generic computer components. These alleged improvements are not exclusive to the current claimed invention. Therefore, the claims presented are distinguishable from Enfish.
Regarding Step 2B, Applicant appears to lean on amended claim language in light of the 35 USC 103 rejection to support that the claims are eligible under Step 2B, Examiner respectfully disagrees. Applicant is respectfully reminded novelty and non-obviousness over the prior art, have no bearing on whether a claim recites or is directed to an abstract idea. The Federal Circuit has made this clear - rejecting an argument substantially similar to Applicants’ in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) ("We do not agree . . . that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete."). Also, MPEP 2106.05 (i) Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) ("The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103 ) is further discussed in MPEP § 2106.05(d).
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.
Regarding Claims 14-15, 17-24 and 34, they are directed to a non-transitory computer readable medium, however the claims are directed to a judicial exception without significantly more. Claims 14-15, 17-24 and 34 are directed to the abstract idea of analyzing enterprise data.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 14, claim 14 recites receiving, first enterprise data; receiving, second enterprise data; processing the first and second enterprise data for storage, wherein processing comprises aligning a format of the first enterprise data and second enterprise data with a format of the centralized data warehouse; in response to receipt of a query identifying a metric associated with a policy, determining a value for the metric based on the processed data in the centralized data warehouse; receiving, updated enterprise data; and in real-time and in response to the updated enterprise data, updating the determine value.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 15, 18, 20-24 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a processor, a computer system, at least one database, at least one application, machine learning model and a centralized data warehouse. The processor, a computer system, at least one database, machine learning model, at least one application, and a centralized data warehouse are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 14, 17, 19 and 34 includes various elements that are not directed to the abstract idea under 2A. These elements include a processor, a computer system, at least one database, at least one application, a centralized data warehouse, a machine learning model and the generic computing elements described in the Applicant's specification in at least Para 0012-0019, 0047-0052. In addition, Claim 14 recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claims 14, 17, 19 and 34, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive. Furthermore, Applicant’s arguments are moot in light of newly amended language.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 14-15, 17-18 and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingali et al. (US 2021/0264332 A1) in view of Bellenguez (US 2022/0156667 A1) in view of Zhao (US 2022/0100726 A1) further in view of Franklin (US 2021/0263818 A1).
Regarding Claim 14, Pingali teaches the limitation of Claim 1 which states
receiving, from at least one database, first enterprise data (Pingali: Para 0102 via The activity log may be received from any of multiple subsystems and sub-processes within the enterprise process, e.g., subsystems 150, 152, 154, 156, or 158).
However, Pingali does not explicitly disclose the limitation of receiving, from at least one application, second enterprise data.
Bellenguez though, with the teachings of Pingali, teaches of
receiving, from at least one application, second enterprise data (Bellenguez: Para 0026 via The data capture engine 122 may be configured to receive various types of operational data from the one or more data sources 140 and to ingest, process, and format the data for use by other components of the enterprise forecast device 102. For example, the various types of operational data may include data output by multiple different applications, some of which may not integrated or related, at least according to a current configuration by the enterprise. As non-limiting examples, the data capture engine 122 may be configured to receive and process data such as online transaction data, batch volume data, manual work volume data, key process configuration data, integration data, data profiles across various areas of the enterprise, infrastructure data, other application data, or a combination thereof. In some implementations, the data capture engine 122 may be configured to perform one or more pre-processing operations on the received data to standardize the received data into a common format capable of being processed downstream).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali with the teachings of Bellenguez in order to have receiving, from at least one application, second enterprise data. The motivations behind this being to incorporate the teachings of using collected data to forecast performance of enterprises. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Pingali/Bellenguez further teaches the limitation of Claim 14 which states
processing the first and second enterprise data for storage in a centralized data warehouse (Pingali: Para 0078 via An event store 170 may be utilized to store event data across the enterprise process. Events could be transactions taking place between various entities in the IT systems in the organization like ERP/CRM systems).
wherein processing comprises aligning a format of the first enterprise data and second enterprise dat with a format of the centralized data warehouse (Bellenguez: Para 0026 via the data capture engine 122 may be configured to receive and process data such as online transaction data, batch volume data, manual work volume data, key process configuration data, integration data, data profiles across various areas of the enterprise, infrastructure data, other application data, or a combination thereof. In some implementations, the data capture engine 122 may be configured to perform one or more pre-processing operations on the received data to standardize the received data into a common format capable of being processed downstream).
However, the combination does not explicitly disclose the limitation of Claim 14 which states in response to receipt of a query identifying a metric associated with a policy, determining a value for the metric based on the processed data in the centralized data warehouse, wherein the value is determined by a machine learning model trained using sets of enterprise data from the centralized data warehouse and known values for each set.
Zhao though, with the teachings of Pingali/Bellenguez, teaches of
in response to receipt of a query identifying a metric associated with a policy, determining a value for the metric based on the processed data in the centralized data warehouse, wherein the value is determined by a machine learning model trained using sets of enterprise data from the centralized data warehouse and known values for each set (Zhao: Para 0027, 0051 via The real-time cube may query the data aggregator for real-time aggregated data based on a model generated by the machine learning component. The real-time cube may receive the queried aggregated data and may extract updated data from the queried aggregated data. The updated data may refer to data that has changed since a previous query, such that only data that has changed is used by the real-time cube. By identifying the updated data (i.e., data that has changed since a previous query) system performance may significantly increase due to the reduced amount of data used for subsequent analysis. The real-time cube may use one or more application programming interfaces (APIs) to apply push and/or poll queries from the data aggregator…The data aggregator 210 may be used to perform any applicable aggregation tasks including, but not limited to, clickstream analytics (e.g., web and mobile analytics), network telemetry analytics (e.g., network performance monitoring), server metrics storage, supply chain analytics (e.g., manufacturing metrics), application performance metrics, digital marketing/advertising analytics, business intelligence/OLAP, or the like. The data aggregator 210 may be used for processing real-time data aggregation, historical data, and real-time analytics and OLAP for BI).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali/Bellenguez with the teachings of Zhao in order to have in response to receipt of a query identifying a metric associated with a policy, determining a value for the metric based on the processed data in the centralized data warehouse, wherein the value is determined by a machine learning model trained using sets of enterprise data from the centralized data warehouse and known values for each set. The motivations behind this being to incorporate the teachings of data aggregation and analytics as taught by Zhao. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, the combination does not explicitly disclose the limitation of Claim 14 which states continuously receiving, from the at least one database or the at least one application, updated enterprise data.
Franklin though with the teachings of Pingali/Bellenguez/Zhao, teaches of
continuously receiving, from the at least one database or the at least one application, updated enterprise data (Franklin: Para 0032 via the parameter segregation server 106 may employ one or more of a web crawler and a data miner, which may be used to retrieve parameter data from one or more of the profile databases 108. The parameter segregation server 106 may retrieve the parameter data either continuously, periodically, or when prompted by an intake server 110 within the compliance prediction system 100 to do so).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali/Bellenguez/Zhao with the teachings of Franklin in order to have continuously receiving, from the at least one database or the at least one application, updated enterprise data. The motivations behind this being to incorporate the teachings of data analysis and compliance prediction. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Pingali/Bellenguez/Zhao/Franklin, further teaches the limitations of Claim 14 which states
in real-time and in response to the updated enterprise data, updating the determined value with the trained machine learning model (Zhao: Para 0012, 0027 via The operations may include: receiving indexed streaming data from a plurality of sources, receiving historical data form the plurality of sources, aggregating the indexed streaming data and the historical data in real time to generate aggregated data, generating a machine learning model based on the historical data and providing the machine learning model to the real-time cube, providing the aggregated data to a real-time cube based on a query for the aggregated data, extracting updated data from the aggregated data and, providing the extracted updated data for visualization. The query for the aggregated data may be based on the machine learning model and the machine learning model may provide one or more triggers based on the historical data… The real-time cube may query the data aggregator for real-time aggregated data based on a model generated by the machine learning component. The real-time cube may receive the queried aggregated data and may extract updated data from the queried aggregated data. The updated data may refer to data that has changed since a previous query, such that only data that has changed is used by the real-time cube).
Regarding Claim 15, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 15 which state
wherein the at least one database comprises at least one relational database external to the computing system, and the at least one application comprises a streaming application external to the computing system (Bellenguez: Para 0026, 0033 via The data capture engine 122 may be configured to receive various types of operational data from the one or more data sources 140 and to ingest, process, and format the data for use by other components of the enterprise forecast device 102. For example, the various types of operational data may include data output by multiple different applications, some of which may not integrated or related, at least according to a current configuration by the enterprise. As non-limiting examples, the data capture engine 122 may be configured to receive and process data such as online transaction data, batch volume data, manual work volume data, key process configuration data, integration data, data profiles across various areas of the enterprise, infrastructure data, other application data, or a combination thereof. In some implementations, the data capture engine 122 may be configured to perform one or more pre-processing operations on the received data to standardize the received data into a common format capable of being processed downstream… During operation of the system 100, the enterprise forecast device 102 may receive the operational data 141, including the application data 142, the integration data 144, and the infrastructure data 146, from the data sources 140. In some implementations, the data sources 140 may include streaming data sources, and the operational data 141 may be streamed to the enterprise forecast device 102).
Regarding Claim 17, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 17 which state
deriving, from the processed data, a training set; training a machine learning model using the training set (Bellenguez: Para 0036, 0052 via The performance forecast engine 126 may provide the model data 110 (and optionally one or more portions of the operational data 141) as training data to the ML models 128 to train the ML models 128 (e.g., to configure the ML models 128 to forecast performance indicators of the enterprise's system based on changes to the enterprise)…The method 300 includes providing model data corresponding to the virtual model as training data to one or more ML models to configure the one or more ML models to forecast performance indicators of the system of the enterprise based on changes to the enterprise, at 306. For example, the one or more ML models may include or correspond to the ML models 128 of FIG. 1. The method 300 includes providing state change data as input data to the one or more ML models to generate one or more forecasted performance indicators corresponding to the system of the enterprise, at 308. For example, the state change data may include or correspond to the state change data 152 of FIG. 1, and the one or more forecasted performance indicators may include or correspond to the forecasted KPIs 112 of FIG. 1);
receiving a query identifying a predicted datapoint (Belleguez: Para 0007, 0018 via a user may input a target change to the enterprise, and the server may provide this state change data (e.g., based on the user input) as input data to the one or more ML models to generate one or more forecasted performance indicators, such as forecasted KPIs. The server may output a system performance forecast to the client device that includes the forecasted KPIs to provide the user with relevant information regarding the enterprise's system and forecasted performance. For example, the client device may receive the system performance forecast and display a graphical user interface (GUI) that displays information derived from the virtual model and the forecasted KPIs, thereby enabling the user to understand the relationships between people, processes, and technology with respect to the system and how the enterprise, through the system, is forecasted to react to changes…A user, such as by using a client device, may interact with one of the selectable indicators to input a potential change to the enterprise's system, and the GUI may be updated to indicate forecasted KPIs based on the indicated change);
determining, by the machine learning model, a predicted value for the metric based on the predicted datapoint; and presenting the predicted value (Bellenguez: Para 0052-0054 via The method 300 includes providing model data corresponding to the virtual model as training data to one or more ML models to configure the one or more ML models to forecast performance indicators of the system of the enterprise based on changes to the enterprise, at 306. For example, the one or more ML models may include or correspond to the ML models 128 of FIG. 1. The method 300 includes providing state change data as input data to the one or more ML models to generate one or more forecasted performance indicators corresponding to the system of the enterprise… In some such implementations, the GUI may include one or more selectable indicators configured to enable user input of one or more changes to the system or the enterprise to trigger updates to the one or more forecasted performance indicators. For example, the client device 150 of FIG. 1 may be configured to display a GUI based on the performance forecast 170 (which includes the forecasted KPIs 112 and, optionally, the recommended actions 114). The GUI may include selectable indicators to enable a user of the client device 150 to input changes to the enterprise, such as changes represented by the state change data 152 of FIG. 1. Additionally or alternatively, the method 300 may include initiating automatic performance of a recommended action that is based on the one or more forecasted performance indicators. For example, the enterprise forecast device 102 of FIG. 1 may provide the automated instructions 172 to an automated system or semi-automated system of the enterprise to trigger automatic performance of one or more actions based on the forecasted KPIs 112).
Regarding Claim 18, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 18 which state
wherein the predicted value is presented on a graphical user interface (GUI) (Bellenguez: Para 0054 via outputting the system performance forecast includes initiating display of a GUI that includes one or more indicators of current system performance and the one or more forecasted performance indicators. In some such implementations, the GUI may include one or more selectable indicators configured to enable user input of one or more changes to the system or the enterprise to trigger updates to the one or more forecasted performance indicators. For example, the client device 150 of FIG. 1 may be configured to display a GUI based on the performance forecast 170 (which includes the forecasted KPIs 112 and, optionally, the recommended actions 114). The GUI may include selectable indicators to enable a user of the client device 150 to input changes to the enterprise, such as changes represented by the state change data 152 of FIG. 1. Additionally or alternatively, the method 300 may include initiating automatic performance of a recommended action that is based on the one or more forecasted performance indicators. For example, the enterprise forecast device 102 of FIG. 1 may provide the automated instructions 172 to an automated system or semi-automated system of the enterprise to trigger automatic performance of one or more actions based on the forecasted KPIs 112).
Regarding Claim 20, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 20 which state
wherein the operations further comprise: continuously refreshing the centralized data warehouse for any update to the enterprise data from the at least one database or the at least one application (Belleguez: Para 0020 via benefits of the Customer Digital Twin include understanding the impact of events on business and technology performance in a current state, understanding a target state of the enterprise's system, continuously refining predictions based on key decisions and updated data from the current enterprise ecosystem, and aligning the end state to target KPI).
Regarding Claim 21, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 21 which state
wherein processing the enterprise data for storage in the centralized data warehouse comprises aligning the enterprise data into a common format and appending metadata including one or more of a department identifier, a data owner identifier, or a data sensitivity classification (Bellenguez: Para 0026 via The data capture engine 122 may be configured to receive various types of operational data from the one or more data sources 140 and to ingest, process, and format the data for use by other components of the enterprise forecast device 102. For example, the various types of operational data may include data output by multiple different applications, some of which may not integrated or related, at least according to a current configuration by the enterprise. As non-limiting examples, the data capture engine 122 may be configured to receive and process data such as online transaction data, batch volume data, manual work volume data, key process configuration data, integration data, data profiles across various areas of the enterprise, infrastructure data, other application data, or a combination thereof. In some implementations, the data capture engine 122 may be configured to perform one or more pre-processing operations on the received data to standardize the received data into a common format capable of being processed downstream. For example, the pre-processing operations may include discarding incomplete, irrelevant, or duplicative data entries, converting data from multiple diverse formats into one or more common formats, condensing or otherwise dimensionally reducing data to reduce a memory footprint, other pre-processing operations, or a combination thereof).
Regarding Claim 22, the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 22 which state
wherein determining the value for the metric comprises calculating a weighted compliance metric based on user- specified weights for one or more compliance parameters (Franklin: Para 0101, 0106 via In addition to determining values for each of these factors, weights may also be assigned to these factors based on their degree of relevance. By way of an example, the factors: “Test Methods” and “Specifications” may be given the highest weightage, while “terminology” may be given lowest weightage. Once the values and weightages for each of the factors has been determined, the ML algorithm engine 836 shares these with the scoring processor 828, which determines a third score for the primary test target based on a weighted average of the factor values. The scoring processor 828 shares the third score along with the first and the second scores with the prediction engine 132. The prediction engine 132 then generates a compliance metric for the primary test target, such that, the compliance metric is a function of the first, second, and third scores… The compliance metric generator 902b depicted in FIG. 9B includes a score extractor 916 that may extract the first score, the second score, and the third score from the test target scorer 804. The score extractor 916 may share the first score, the second score, and the third score with a score cumulator 918, which may determine a cumulative score for the primary test target. The cumulative score, for example, may be determined based on a simple average. Alternatively, the cumulative score may be determined based on a weighted average of the first, second, and third scores. The weights may either be system defined or may be assigned or modified by a user…).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingali et al. (US 2021/0264332 A1) in view of Bellenguez (US 2022/0156667 A1) in view of Zhao (US 2022/0100726 A1) in view of Franklin (US 2021/0263818 A1) further in view of Wollstadt et al. (US 2024/0095853 A1).
Regarding Claim 19, while the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 17, it does not explicitly disclose the limitation of Claim 19 which states wherein training the machine learning model comprises: associating, for the machine learning model, a first datapoint of the training set with an actual first value for the metric, the first value included in the first datapoint; determining, by the machine learning model, a predicted second value for the metric based on a second datapoint of the training set; determining an error for the machine learning model based on a difference between the predicted second value and an actual second value from the second datapoint; and adjusting the machine learning model based on the determined error.
Wollstadt though, with the teachings of Pingali/Bellenguez/Zhao/Franklin, teaches of
wherein training the machine learning model comprises: associating, for the machine learning model, a first datapoint of the training set with an actual first value for the metric, the first value included in the first datapoint; determining, by the machine learning model, a predicted second value for the metric based on a second datapoint of the training set; determining an error for the machine learning model based on a difference between the predicted second value and an actual second value from the second datapoint; and adjusting the machine learning model based on the determined error (Wollstadt: Para 0067-0068 via at predetermined intervals, repeating steps of obtaining the historical supplier data, obtaining the process parameters of a manufacturing process; and obtaining the external data, applying the machine learning algorithm to generate a retrained model for predicting the time delay risk in the manufacturing process based on the obtained historical supplier data, the process parameters of the manufacturing process and the obtained external data, and of recording the generated retrained model in the database. The method may comprise monitoring an error that the trained model makes during operation, by comparing an actual event that was predicted with a calculated prediction for the event. In case the monitored error exceeds a threshold, the method may proceed by manually or automatically triggering a step of applying a machine learning algorithm to generate a retrained model for predicting the time delay risk in the manufacturing process based on the obtained historical supplier data, the process parameters of the manufacturing process and the obtained external data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali/Bellenguez/Zhao/Franklin with the teachings of Wollstadt in order to have wherein training the machine learning model comprises: associating, for the machine learning model, a first datapoint of the training set with an actual first value for the metric, the first value included in the first datapoint; determining, by the machine learning model, a predicted second value for the metric based on a second datapoint of the training set; determining an error for the machine learning model based on a difference between the predicted second value and an actual second value from the second datapoint; and adjusting the machine learning model based on the determined error. The motivations behind this being to incorporate the teachings of supply chain risk prediction and interactive mitigation of supply chain risk using machine learning. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingali et al. (US 2021/0264332 A1) in view of Bellenguez (US 2022/0156667 A1) in view of Zhao (US 2022/0100726 A1) in view of Franklin (US 2021/0263818 A1) further in view of Valsaraj et al. (US 10,255,085 B1).
Regarding Claim 23, while the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 14, it does not explicitly disclose the limitation of Claim 23 which states generating a suggested corrective action when the updated metric value indicates a non- compliant condition; and presenting the suggested corrective action through a graphical user interface.
Valsaraj though, with the teachings of Pingali/Bellenguez/Zhao/Franklin, teaches of
generating a suggested corrective action when the updated metric value indicates a non- compliant condition; and presenting the suggested corrective action through a graphical user interface (Valsaraj: Col 25 line 39 – Col 26 line 14 via In some examples, the GUI system may further include one or more machine-learning models. Machine learning is a branch of artificial intelligence that involves the use of models capable of learning from, categorizing, and making predictions about data. Such models are referred to herein as machine-learning models. Examples of machine-learning models can include neural networks, decision trees, classifiers, clusterer, factorizers, or any combination of theses. The GUI system can use the machine-learning model(s) to determine which, if any, of the data points in the dataset should be overridden. For example, the dataset can have errors if it was generated by another, less accurate system. So, the GUI system can use the machine-learning model(s) to analyze the dataset and identify data points with potentially erroneous values, which can serve as candidates for overrides. If the GUI system determines that a data point should be overridden (e.g., because it is likely erroneous), the GUI system can next use one or more machine-learning models to determine whether the override value for the data point should be greater than or less than the data point's current value. The GUI system can then display a visual directionality cue in the GUI 1100 representing this information. For example, in FIG. 11, the GUI system has determined that the values for the March, May, and June data points should be overridden, so the GUI system has highlighted the corresponding cells in the “Override” row of the data table 1104 in a particular color (e.g., orange), which can be explained by legend 1108. This highlighting may signify that the user can input override values into those cells. The GUI system has also determined that the data-point values for March, May, and June should be overridden with new values that are greater than the existing values for those data points. So, the GUI system 1100 has inserted upward-facing arrows into the “Recommended Adjustment Direction” row of the data table 1104, thereby signifying that the override values should be higher than the existing values of the data points. Had the GUI system determined that a data point's value should be overridden with lower value, the GUI system would have inserted a downward-facing arrow into the “Recommended Adjustment Direction” row of the data table 1104). Making the determination if a value should be overridden indicates non-compliance.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali/Bellenguez/Zhao/Franklin with the teachings of Valsaraj in order to have generating a suggested corrective action when the updated metric value indicates a non- compliant condition; and presenting the suggested corrective action through a graphical user interface. The motivations behind this being to incorporate the teachings of an interactive graphical user interface with override guidance as taught by Valsaraj. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingali et al. (US 2021/0264332 A1) in view of Bellenguez (US 2022/0156667 A1) in view of Zhao (US 2022/0100726 A1) in view of Franklin (US 2021/0263818 A1) further in view of Martin (US 2009/0177988 A1).
Regarding Claim 24, while the combination of Pingali/Bellenguez/Zhao/Franklin teaches the limitations of Claim 14, it does not explicitly disclose the limitations of Claim 24 which state wherein receiving updated enterprise data comprises continuously receiving, from the at least one application, a stream of enterprise data updates and automatically refreshing the metric value based on the received updates.
Martins though, with the teachings of Pingali/Bellenguez/Zhao/Franklin, teaches of
wherein receiving updated enterprise data comprises continuously receiving, from the at least one application, a stream of enterprise data updates and automatically refreshing the metric value based on the received updates (Martins: Para 0024 via Within enterprise system 100, user computing devices 102A-102N (collectively, 102) interact with data storage systems 106A-106N (collectively, 106) via network 104. In one embodiment, a continuous stream of data and/or data changes may be provided from data storage systems 106 to user devices 102. Enterprise system 100 is capable of processing heterogeneous data from multiple different sources. Thus, as a result, data storage systems 106 are coupled to one or more external data sources 108. External data sources 108 may include transactional and non-transactional systems and data, and data from external data sources 108 may be generated, or provided, by external business applications, databases, message servers, text files, or other sources. Data storage systems 106 receive or retrieve data from external data sources 108 as event data that can be asynchronously or synchronously processed. Data storage systems 106 may include various agents to process incoming event data, and these agents handle the specific interfaces or processing mechanisms for the type of external data provided by a particular external data source. These agents are based on industry standard technologies, according to one embodiment. The use of these agents within data storage systems 106 allows data from multiple data sources to be virtualized as streams of real-time data that may be processed and transmitted, in some form, to the user devices 102 for further processing or display. For example, a large global enterprise that has silos of data in heterogeneous applications (which may be included within external data sources 108) can create a single hierarchical view of the data. This single view of the enterprise can, in one embodiment, be accessed via a graphical dashboard that contains the aggregated data across these heterogeneous systems from a single metric. This dashboard may be displayed on one or more of the user devices 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pingali/Bellenguez/Zhao/Franklin with the teachings of Martins in order to have wherein receiving updated enterprise data comprises continuously receiving, from the at least one application, a stream of enterprise data updates and automatically refreshing the metric value based on the received updates. The motivations behind this being to incorporate the teachings of data selection and retrieval in an enterprise computer system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Allowable Subject Matter
Regarding Claim 34, it has been deemed distinguishable over the prior art for the following reasons.
While Valsaraj et al. (US 10,255,085 B1) teaches of “If the GUI system determines that a data point should be overridden (e.g., because it is likely erroneous), the GUI system can next use one or more machine-learning models to determine whether the override value for the data point should be greater than or less than the data point's current value. The GUI system can then display a visual directionality cue in the GUI 1100 representing this information. For example, in FIG. 11, the GUI system has determined that the values for the March, May, and June data points should be overridden, so the GUI system has highlighted the corresponding cells in the “Override” row of the data table 1104 in a particular color (e.g., orange), which can be explained by legend 1108. This highlighting may signify that the user can input override values into those cells. The GUI system has also determined that the data-point values for March, May, and June should be overridden with new values that are greater than the existing values for those data points. So, the GUI system 1100 has inserted upward-facing arrows into the “Recommended Adjustment Direction” row of the data table 1104, thereby signifying that the override values should be higher than the existing values of the data points. Had the GUI system determined that a data point's value should be overridden with lower value, the GUI system would have inserted a downward-facing arrow into the “Recommended Adjustment Direction” row of the data table 1104”.
And while Maughan et al. (US 2017/0372232 A1) teaches of “The corrective action module 204 may be configured to perform certain corrective actions automatically and others in response to user input, to perform all corrective actions automatically, or the like. In embodiments where the corrective action module 204 performs an automated corrective action, the corrective action module 204 may notify a user (e.g., in a GUI; in an email, text message, push notification, or other message; in a log; or the like) that the automated corrective action was taken. The corrective action module 204 may provide an interface allowing a user to reverse an automated corrective action after it is taken (e.g., providing a reverse button or other user interface element with the notification and explanation of the automated corrective action, or the like). In certain embodiments, each corrective action taken by the corrective action module 204 is reversible (e.g., the corrective action module 204 may store a copy of the original data, may store a reverse or undo log, or the like), allowing a user to undo or reverse actions taken by the corrective action module 204, returning data to a previous state, an original state, or the like”.
And Vadera et al. (US 2022/0405570 A1) teaches of “The computing device accesses a trained first machine learning (ML) model, a dataset, and a utility function. The computing device trains a second ML model based on performing post-hoc correction of a first set of decisions generated by the first ML model on the dataset. The training includes processing the first set of decisions with respect to a second set of decisions made by the second ML model on the dataset. The training further includes configuring, based on the processing, the second ML model with parameters from a set of parameters optimizing a loss-objective function that concurrently maximizes utility of the second set of decisions according to the utility function and a log-likelihood on the dataset. After training, the second ML model is outputted as a loss-calibrated ML model”.
Neither art, alone or in combination with previously cited art explicitly teach the limitation of Claim 34 which states
“wherein the operations further comprise: receiving an override value from a user in response to a suggested corrective action; and adjusting the machine learning model according to a loss function, the loss function receiving, as input, the value for the metric from the machine learning model and the override value from the user”.
Conclusion
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.
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625
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