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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 11, 2026 has been entered.
Claims 1 and 16 have been amended.
Claims 10-15 have been withdrawn.
Claims 5-7 and 17 have been cancelled
Claims 1-4, 8-16, and 18-20 are pending and claims 1-4, 8-9, 16, and 18-20 are considered.
The effective filing date of the claimed invention is February 13, 2023.
Response to Amendment
Amendments to Claims 1 and 16 are acknowledged. Amendments to Claims 1 and 16 were sufficient to overcome the 35 USC 103 rejection of Claims 1-4, 8-9, 16, and 18-20.
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 1-4, 8-9, 16 and 18-20 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 1-4, and 8-9 are processes and Claims 16 and 18-20 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));
wherein generating the transaction-cost metrics dashboard comprises generating a zero-transactions report that, in response to determining form the transaction information that at least one computing entity of the subset of the plurality of computing entities has zero transaction volume during a predetermined time period, retrieves, form the database of information, infrastructure utilization information and rate-cared information for the at least one computing entity and identifies, in the transaction-cost metrics dashboard, one or more allocated computing resources of the at least one computing entity for capacity buy back (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 are mental processes. The mere nominal recitation of a processor, a non-volatile memory, and a database does not take the claim out of the mental processes. 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, g) generating an output providing a report of information on one or more computing entities in the plurality of computing entities, and h) retrieving infrastructure utilization information and rate-card information for capacity buy back, 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 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).
Prior Art
The prior arts of record fail to teach the overall combination of Claims 1-4 8-9, 16, and 18-20. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A computer-implemented method, comprising:
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;
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 inventory information place at a second predetermined interval;
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;
tracking cost information associated with the transactions of each computing entity, the tracking of cost information taking place at a fourth predetermined interval;
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;
building a database of information for each computing entity, the database comprising at least 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 ratio of aggregated transaction cost to transaction volume for the computing entity; 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, 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,
wherein generating the transaction-cost metrics dashboard comprises generating a zero-transactions report that, in response to determining from the transaction information that at least one computing entity of the subset of the plurality of computing entities has zero transaction volume during a predetermined time period, retrieves, from the database of information, infrastructure utilization information and rate-card information for the at least one computing entity and identifies, in the transaction-cost metrics dashboard, one or more allocated computing resources of the at least one computing entity for capacity buy back. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat 11,770,398 “Erlingsson” discloses a guided anomaly detection framework, including: gathering data describing activity associated with an anomaly detection framework monitoring a cloud deployment; generating, based on the data, a prompt describing one or more natural language inputs for a security workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface. Erlingsson fails to disclose generating a transaction-cost metrics dashboard that displays the transaction-cost metric for each company, wherein generating the transaction-cost metrics dashboard comprises generating a zero-transactions report that retrieves infrastructure utilization information and rate-card information for the at least one computing entity and identifies, in the transaction-cost metrics dashboard, one or more allocated computing resources of the at least one computing entity for capacity buy back.
US Pat Pub 2023/0123322 “Cella” teaches prioritizing predictive model data streams including receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model. Cella fails to teach generating a transaction-cost metrics dashboard that displays the transaction-cost metric for each company, wherein generating the transaction-cost metrics dashboard comprises generating a zero-transactions report that retrieves infrastructure utilization information and rate-card information for the at least one computing entity and identifies, in the transaction-cost metrics dashboard, one or more allocated computing resources of the at least one computing entity for capacity buy back.
US Pat Pub 2020/0233857 “Fehling” teaches a largely automated method of categorizing spend data is provided that does not require a prior in-depth knowledge of an organization's transactional data. Natural language processing is applied to text data from transactional data to generate a consolidated cleaned data set (CDS) containing information for categorization. Logs for transactions are clustered based on similarity, forming the minimal data set (MDS). An automated algorithm selects a subset of high-value clusters that are categorized by requesting users to manually categorize one or more representative logs from each cluster of the subset. A model is then trained using the subset of manually categorized clusters and used to predict spend categories for the remaining logs with high accuracy. The AI engine automatically analyzes the predictions based on client context and either auto-tunes the machine learning model or identifies a new subset of clusters to be manually categorized. This loop may continue until 95%-100% of the spend is categorized. Fehling fails to teach generating a transaction-cost metrics dashboard that displays the transaction-cost metric for each company, wherein generating the transaction-cost metrics dashboard comprises generating a zero-transactions report that retrieves infrastructure utilization information and rate-card information for the at least one computing entity and identifies, in the transaction-cost metrics dashboard, one or more allocated computing resources of the at least one computing entity for capacity buy back.
Response to Arguments
35 USC 101
Applicant's arguments filed May 11, 2026 have been fully considered but they are not persuasive.
Applicant argues that the claims use tracked transaction information to detect a zero-transaction volume condition of a computing entity, use infrastructure utilization information and rate-cared information for that same computing entity to identify allocated computing resources for capacity buy buck, and therefore, apply tracked computing-entity information to a concrete computer-resource management problem. The problem with this argument is that no actions are being performed for the actual information that is being analyzed, nor are any actions performed as a result of the analysis besides identifying resources for buy back. The claims are merely directed to analyzing the collected data in a specific environment to identify results meeting specific parameters. While the environment that the information is being collected and analyzed is in a technical field, the actual claimed limitations are not applied to an improvement of that technical field. See MPEP 2106.04(d)(1).
Applicant further argues that the claims integrate the abstract idea into a practical application because the claims apply the data-analysis concept in a meaningful and specific way to identify allocated computing resources for capacity buy back, and that this is a practical application in computer-resource monitoring and capacity management. Creating index logs, searching and analyzing the index logs, populating a database with information derived from the index logs, and generating an output report by generating a dashboard for presenting the information, are merely an automation of the abstract idea, but not steps to be completed by anything other than generic computing elements. Implementing analytics, populating a database, and identifying resources that meet specific requirements 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);
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed May 11, 2026, with respect to the 35 USC 103 rejection of Claims 1-4, 8-9, 16, and 18-20 have been fully considered and are persuasive. The 35 USC 103 rejection of Claims 1-4, 8-9, 16, and 18-20 has been withdrawn.
Conclusion
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd Obeid can be reached at 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
Prosecution Insights