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 .
Summary
This Final Office Action in response to the communication received on November 25, 2025.
Claims 1 and 16 have been amended.
Claims 10-15 have been withdrawn.
Claims 1-20 are pending and claims 1-9 and 16-20 being considered.
The effective filing date of the claimed invention is February 13, 2023.
Response to Amendment
Amendments to Claims 1 and 16 are acknowledged.
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 1-9 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 1-9 are processes and Claims 16-20 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 16 (and similarly Claim 1) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
A system, comprising:
a processor; and
a non-volatile memory in operable communication with the processor and storing computer program code that when executed on the processor causes the processor to execute a process operable to perform operations of:
receiving, at a first predetermined interval, historical data associated with operation of a plurality of computing entities, the historical data comprising index logs generated by the plurality of computing entities and stored in a data store configured to be queried by a search and analytics engine to store, search, and analyze log data for the plurality of computing entities (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
tracking inventory information based on the historical data, the tracking comprising discovering when new computing entities are added to the plurality of computing entities, wherein tracking inventor information place at a second predetermined interval (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
tracking transaction information, wherein the tracking is based on the historical data, wherein the transaction information comprises information associated with transactions of the plurality of computing entities and comprises information associated with a volume of transactions, and where the tracking of transaction information takes place at a third predetermined interval (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
tracking cost information associated with the transactions of each computing entity, the tracking of cost information taking place at a fourth predetermined interval (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
tracking utilization information associated with each computing entity, the utilization information comprising information relating to utilization of an infrastructure of each respective computing entity, the tracking of utilization information taking place at a fifth predetermined interval, wherein tracking the inventory information, the transaction information, the cost information, and the utilization information comprises automatically parsing the index logs in the data indexes/store using respective tracking modules executed on a deployment module to populate, for each computing entity, the database of information (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
building a database of information for each computing entity, the database comprising at last one of inventory, transaction, and cost information, wherein building the database of information comprises, for each computing entity, aggregating the transaction information and the cost information over a common time window and computing a transaction-cost metric comprising a ration of aggregated transaction cost to transaction volume for the computing entity (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
generating an output providing a report of information on one or more computing entities in the plurality of computing entities, wherein the report of information is based on information contained in the database of information (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Claim 16 (and similarly Claim 1) is directed to a series of steps for providing a report on computing entities of a plurality of computing entities, which is a mental process. The mere nominal recitation of a processor, a non-volatile memory, and a database does not take the claim out of the mental process. Thus, Claim 16 (and similarly Claim 1) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 16 (and similarly Claim 1) is not integrated into a practical application. The additional elements are: a processor, a non-volatile memory, and a database that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 16 (and similarly Claim 1) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 16 (and similarly Claim 1) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) receiving historical data, b) tracking inventory information, c) tracking transaction information, d) tracking cost information, e) tracking utilization information associated with each computing entity, f) building a database of information for each computing entity, and g) generating an output providing a report of information on one or more computing entities in the plurality of computing entities, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 16 (and similarly Claim 1) is ineligible.
Claim 2 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 4 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 7 (and similarly Claim 17) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 8 (and similarly Claim 18) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 9 (and similarly Claim 19) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a machine learning regressor model, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 20 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a machine learning regressor model, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
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.
Claim(s) 1-9 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat 11,770,398 “Erlingsson”, in view of US Pat Pub 2023/0123322 “Cella”, in view of US Pat Pub 2020/0233857 “Fehling”.
As per Claims 1 and 16, Erlingsson discloses a computer-implemented method and system, comprising:
receiving, at a first predetermined interval, historical data associated with operation of a plurality of computing entities (Erlingsson: Column 4 lines 57-67 and Column 5, lines 1-8 A data warehouse may be embodied as an analytic database (e.g., a relational database) that is created from two or more data sources. Such a data warehouse may be leveraged to store historical data, often on the scale of petabytes. Data warehouses may have compute and memory resources for running complicated queries and generating reports. Data warehouses may be the data sources for business intelligence (‘BI’) systems, machine learning applications, and/or other applications. By leveraging a data warehouse, data that has been copied into the data warehouse may be indexed for good analytic query performance, without affecting the write performance of a database (e.g., an Online Transaction Processing (‘OLTP’) database). Data warehouses also enable joining data from multiple sources for analysis. For example, a sales OLTP application probably has no need to know about the weather at various sales locations, but sales predictions could take advantage of that data. By adding historical weather data to a data warehouse, it would be possible to factor it into models of historical sales data.);
tracking inventory information based on the historical data, the tracking comprising discovering when new computing entities are added to the plurality of computing entities, wherein tracking inventor information place at a second predetermined interval (Erlingsson: Column 28, lines 24-52 For nodes with a threshold amount of commonality (e.g., at least 66% members in common), any new nodes (i.e., appearing in the snapshot's graph but not the cumulative graph) are assigned the same PType identifier as is assigned to the corresponding node in the cumulative graph. For each node that is not classified (i.e., has not been assigned a PType identifier), a network signature is generated (i.e., indicative of the kinds of network connections the node makes, who the node communicates with, etc.). The following processing is then performed until convergence. If a match of the network signature is found in the cumulative graph, the unclassified node is assigned the PType identifier of the corresponding node in the cumulative graph. Any nodes which remain unclassified after convergence are new PTypes and are assigned new identifiers and added to the cumulative graph as new. As applicable, the detection of a new PType can be used to generate an alert. If the new PType has a new CmdType, a severity of the alert can be increased. If any surviving nodes (i.e., present in both the cumulative graph and the snapshot graph) change PTypes, such change is noted as a transition, and an alert can be generated.);
tracking cost information associated with the transactions of each computing entity, the tracking of cost information taking place at a fourth predetermined interval (Erlingsson: Column 77, lines 24-43, The trajectory of a particular cloud deployment 508, 514 may be determined, for example, by evaluating the particular cloud deployment's 508, 514 performance over time as measured by one or more metrics. For example, the performance of each cloud deployment may be periodically scored based on a weighted combination of multiple metrics (e.g., reliability, cost, regulatory compliance).);
tracking utilization information associated with each computing entity, the utilization information comprising information relating to utilization of an infrastructure of each respective computing entity, the tracking of utilization information taking place at a fifth predetermined interval (Erlingsson: Column 63 lines 1-11 . Through the usage of eBPF, user mode processes can hook into specific trace points in the kernel and access data structures and other information. For example, eBPF may be used to gather information that enables the systems described herein to attribute the utilization of networking resources or network traffic to specific processes. This may be useful in analyzing the behavior of a particular process, which may be important for observability/SIEM.);
building a database of information for each computing entity, the database comprising at last one of inventory, transaction, and cost information (Erlingsson: Column 4 lines 57-67 and Column 5, lines 1-8 A data warehouse may be embodied as an analytic database (e.g., a relational database) that is created from two or more data sources. Such a data warehouse may be leveraged to store historical data, often on the scale of petabytes. Data warehouses may have compute and memory resources for running complicated queries and generating reports. Data warehouses may be the data sources for business intelligence (‘BI’) systems, machine learning applications, and/or other applications. By leveraging a data warehouse, data that has been copied into the data warehouse may be indexed for good analytic query performance, without affecting the write performance of a database (e.g., an Online Transaction Processing (‘OLTP’) database). Data warehouses also enable joining data from multiple sources for analysis. For example, a sales OLTP application probably has no need to know about the weather at various sales locations, but sales predictions could take advantage of that data. By adding historical weather data to a data warehouse, it would be possible to factor it into models of historical sales data.); and
generating an output providing a report of information on one or more computing entities in the plurality of computing entities, wherein the report of information is based on information contained in the database of information (Erlingsson: Column 5 lines 63-67 and Column 6, lines 1-8 One or more operations performed by data processing resources 20 may be performed periodically according to a predetermined schedule. For example, one or more operations may be performed by data processing resources 20 every hour or any other suitable time interval. Additionally or alternatively, one or more operations performed by data processing resources 20 may be performed in substantially real-time (or near real-time) as data is ingested into data platform 12. In this manner, the results of such operations (e.g., one or more detected anomalies in the data) may be provided to one or more external entities (e.g., computing device 24 and/or one or more users) in substantially real-time and/or in near real-time.).
Erlingsson fails to disclose a computer-implemented method and system, comprising:
the historical data comprising index logs generated by the plurality of computing entities and stored in a data store configured to be queried by a search and analytics engine to store, search, and analyze log data for the plurality of computing entities;
tracking transaction information, wherein the tracking is based on the historical data, wherein the transaction information comprises information associated with transactions of the plurality of computing entities and comprises information associated with a volume of transactions, and where the tracking of transaction information takes place at a third predetermined interval;
wherein tracking the plurality of information comprises automatically parsing the index logs in the data indexes/store using respective tracking modules executed on a deployment module to populate, for each computing entity, the database information;
wherein building the database of information comprises, for each entity, aggregating the transaction information and cost information over a common time window and computing a transaction-cost metric comprising a ratio of aggregated transaction cost to transaction volume for the computing entity; and
wherein generating the output comprises generating a transaction-cost metrics dashboard that for at least a subset of the plurality of computing entities displays the transaction-cost metric for each computing entity.
Cella teaches a computer-implemented method and system, comprising:
tracking transaction information, wherein the tracking is based on the historical data, wherein the transaction information comprises information associated with transactions of the plurality of computing entities and comprises information associated with a volume of transactions, and where the tracking of transaction information takes place at a third predetermined interval (Cella: [2090] the intelligence service 13004 may receive data from various sources described throughout this document and the documents incorporated by reference herein and may generate a set of feature vectors based on the received data… Data sources used to produce the set of feature vectors, may include, but are not limited to, order data, demand data, supply data, cost data, volatility data, pricing pattern data, order size data, order volume data, geographic trading data, maritime data, trucking fleet data, railway data, traffic data, weather data, social media sites, external data (such as news involving smart containers or shipping or the like), and many others.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Erlingsson to include tracking historical transaction data as taught by Cella, when generating a report based off of information stored in a database as taught by Erlingsson with the motivation to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (Cella: [0006]).
Erlingsson and Cella fail to disclose a computer-implemented method and system, comprising:
the historical data comprising index logs generated by the plurality of computing entities and stored in a data store configured to be queried by a search and analytics engine to store, search, and analyze log data for the plurality of computing entities;
wherein tracking the plurality of information comprises automatically parsing the index logs in the data indexes/store using respective tracking modules executed on a deployment module to populate, for each computing entity, the database information;
wherein building the database of information comprises, for each entity, aggregating the transaction information and cost information over a common time window and computing a transaction-cost metric comprising a ratio of aggregated transaction cost to transaction volume for the computing entity; and
wherein generating the output comprises generating a transaction-cost metrics dashboard that for at least a subset of the plurality of computing entities displays the transaction-cost metric for each computing entity.
Fehling teaches a computer-implemented method and system, comprising:
the historical data comprising index logs generated by the plurality of computing entities and stored in a data store configured to be queried by a search and analytics engine to store, search, and analyze log data for the plurality of computing entities (Fehling: [0013] various embodiments described herein provide methods for categorizing spend data which may include general ledger (GL), accounts payable (AP), purchase order (PO) information, including but not limited to transactions, invoices, expenditure receipts, supplier-based data sets and other documented expenses, herein collectively referred to as spend logs (or simply logs). After collecting spend data from all relevant data systems and/or sources, the data is processed and consolidated to generate a cleaned data set (CDS). The CDS includes spend data that has been filtered to remove less important information and/or processed to standardize information used for log categorization. In some cases, the CDS includes an organized structure that breaks spend information down by field types (e.g., total cost, vendor, transaction date, etc.). In some cases, standardizing spend data involves applying natural language processing operations to text information associated with logs. Logs from the CDS are then clustered into groups based on a similarity of words, costs, dates, or other patterns and features, which generates a new data set of smaller size: the minimal data set (MDS). The MDS constitutes groups of logs representing the same type of transaction (e.g. “Taxi fare” and “Supplier A”). Based on clustering operations, each log within the same cluster can be mapped to the same cost category. [0017] In some cases, a user may filter spend data based on expense logs that satisfy date criteria, location criteria, department criteria, or any other selected criteria that may be useful for classifying expense data. In other cases, a user can search spend data based on an alternate hierarchical category structure [0022] When consolidating raw spend data, logs are stored in a common database, herein referred to as a cost database. [0040] Clustering (502) the logs from the CDS results in the minimal data set (MDS) 512 which constitutes cluster groups 506, 508, and 510. After the clustering, the log groups are analyzed with their respective client's account structure from the profit & loss statement, general ledger, AP and PO Accrual Reconciliation Report.);
wherein tracking the plurality of information comprises automatically parsing the index logs in the data indexes/store using respective tracking modules executed on a deployment module to populate, for each computing entity, the database information (Fehling: [0031] In some embodiments, a dictionary of terms can be generated based on the words and common phrases found in the MDS. In some cases, as depicted in FIG. 3, a term-occurrence matrix 300 can be created which has log vectors 302 and term vectors 304. Log vectors can record various log features along a plurality of dimensions of the log. For instance, indices of log vectors may provide values indicating a number of occurrences of a term in the log or a value indicating some other characteristic (e.g., a cost, transaction time, transaction location, etc.).);
wherein building the database of information comprises, for each entity, aggregating the transaction information and cost information over a common time window and computing a transaction-cost metric comprising a ratio of aggregated transaction cost to transaction volume for the computing entity (Fehling: [0013] various embodiments described herein provide methods for categorizing spend data which may include general ledger (GL), accounts payable (AP), purchase order (PO) information, including but not limited to transactions, invoices, expenditure receipts, supplier-based data sets and other documented expenses, herein collectively referred to as spend logs (or simply logs). After collecting spend data from all relevant data systems and/or sources, the data is processed and consolidated to generate a cleaned data set (CDS). The CDS includes spend data that has been filtered to remove less important information and/or processed to standardize information used for log categorization. In some cases, the CDS includes an organized structure that breaks spend information down by field types (e.g., total cost, vendor, transaction date, etc.). In some cases, standardizing spend data involves applying natural language processing operations to text information associated with logs. Logs from the CDS are then clustered into groups based on a similarity of words, costs, dates, or other patterns and features, which generates a new data set of smaller size: the minimal data set (MDS). The MDS constitutes groups of logs representing the same type of transaction (e.g. “Taxi fare” and “Supplier A”). Based on clustering operations, each log within the same cluster can be mapped to the same cost category.); and
wherein generating the output comprises generating a transaction-cost metrics dashboard that for at least a subset of the plurality of computing entities displays the transaction-cost metric for each computing entity (Fehling: [0016] FIG. 1A depicts an application for presenting categorized spend data to a user, often referred to as a spend cube. In the depicted interface 100, expense totals 102 for various level 1 categories 104a are depicted, as well as visual data 106 (graphs, charts, diagrams, and the like) for conveying the breakdown of categorized spend data to a user. An interface may provide a variety of user selectable features for allowing a user to explore the spend data—allowing a user to quickly understand the state of the organization's spending over a selected period (e.g., a 1 month, 6 months, 1 year, or another selected amount for which spend data has been provided). [0027] In generating the CDS, various natural language processing (NLP) steps are applied to the cost data to aid subsequent analysis of each transaction. These operations can aid in determining relevant keywords and in determining relationships between transactions for clustering.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Erlingsson and Cella to include computing and displaying a transaction cost metric as taught by Fehling, when generating a report based off of information stored in a database as taught by Erlingsson and Cella with the motivation to improve classification and categorization of transactional data (Fehling: [0002]).
As per Claim 2, Erlingsson discloses a computer-implemented method, wherein at least two of the first predetermined interval, second predetermined interval, third predetermined interval, fourth predetermined interval, and fifth predetermined interval, correspond to time intervals that are identical (Erlingsson: Column 5 lines 63-67 and Column 6, lines 1-8).
As per Claim 3, Erlingsson discloses a computer-implemented method, wherein the second predetermined interval is configured so that inventory information is tracked in real time to ensure that the database of information is up to date (Erlingsson: Column 5 lines 63-67 and Column 6, lines 1-8).
As per Claim 4, Erlingsson discloses a computer-implemented method, wherein the first predetermined interval is configured so that historical data is received before tracking transaction information, tracking cost information, and tracking utilization information (Erlingsson: Column 5 lines 63-67 and Column 6, lines 1-8).
As per Claim 5, Erlingsson discloses a computer-implemented method, wherein the transaction information associated with transactions of the plurality of computing entities comprises at least one of usage, metrics, cost, capital expenses, infrastructure information, licensing information, and operating expenses(Erlingsson: Column 76, lines 16-42).
As per Claim 6, Erlingsson discloses a computer-implemented method, wherein the report comprises a transaction cost metrics dashboard providing at least one of transaction information, cost information, and utilization information, for at least one computing entity of the plurality of computing entities (Erlingsson: Column 76, lines 16-42).
As per Claims 7 and 17, Erlingsson discloses a computer-implemented method and system, wherein the report is generated automatically based on information in the database and comprises at least one recommended action to optimize at least one of cost and efficiency associated with operation of at least one computing entity in the plurality of computing entities (Erlingsson: Column 5 lines 63-67 and Column 6 lines 1-8).
As per Claims 8 and 18, Erlingsson discloses a computer-implemented method and system, wherein the report comprises at least one predicted transaction volume associated with at least one computing entity in the plurality of computing entities (Erlingsson: Column 5 lines 63-67 and Column 6 lines 1-8).
As per Claims 9 and 19, Erlingsson fails to disclose but Cella teaches a computer-implemented method and system, further comprising:
building a training dataset from a first subset of the historical data (Cella: [0232]);
building a testing dataset from a second subset of the historical data (Cella: [0373]);
performing first training of a machine learning regressor model, with the training dataset, to learn transaction volumes associated with the plurality of computing entities (Cella: [0625]);
performing a second training of the machine learning regressor model, with the testing dataset, to validate the machine learning regressor model (Cella: [1068] and [1238]); and
generating the predicted transaction volume using the machine learning regressor model (Cella: [1068] and [2090]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Erlingsson to include tracking historical transaction data as taught by Cella, when generating a report based off of information stored in a database as taught by Erlingsson with the motivation to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (Cella: [0006]).
As per Claim 20, Erlingsson fails to disclose but Cella teaches a system, wherein the machine learning regressor model is configured using multiple regressors, wherein each of the multiple regressors is trained on at least one of different samples of data from the historical data and different features of the data from the historical data (Cella: [0592] and [1068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Erlingsson to include tracking historical transaction data as taught by Cella, when generating a report based off of information stored in a database as taught by Erlingsson with the motivation to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations (Cella: [0006]).
Response to Arguments
35 USC 101
Applicant's arguments filed November 25, 2025 have been fully considered but they are not persuasive.
Applicant argues that the claims integrate the abstract idea into a practical application because the claims recite a concrete technical arrangement – index logs, a data indexes/store, a search and analytics engine, and automated tracking modules executing on a deployment module – that cannot practically be performed in the human mind and that provide a particular way of processing large volumes of machine-generated log data to produce per-entity transaction-cost metrics. Creating index logs, searching and analyzing the index logs, and populating a database with information derived from the index logs, are merely an automation of the abstract idea, but not steps to completed by anything other than generic computing elements. Implementing analytics and populating a database do not require anything more than generic computing elements that have been programmed to implement the abstract idea. See MPEP 2106.05(h) – Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception: iv. Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016);
(5 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed November 25, 2025, with respect to the rejection(s) of claim(s) 1-9 and 16-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US Pat 11,770,398 “Erlingsson”, in view of US Pat Pub 2023/0123322 “Cella”, in view of US Pat Pub 2020/0233857 “Fehling”.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/PETER LUDWIG/Primary Examiner, Art Unit 3627