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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites “A method comprising: collecting operational data from a plurality of devices; predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data; and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices; wherein the steps of the method are executed by a processing device operatively coupled to a memory” which is a process.
Step 2, Prong One: Judicial exception? Yes.
The claim when viewed as a whole recites an abstract idea, e.g. mental process. The broadest reasonable interpretation of the limitations is that those limitations fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
The recited steps “collecting operational data from a plurality of devices; predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data; and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices” encompasses data gathering, observation, evaluation, judgement, and opinion.
The steps are not performed by any particular devices. Instead, they are performed by a processing device which is a tool used to perform the abstract idea.
The step “predicting…” is a mathematical concept. The claim does not recite a particular equation or algorithm for making the recited combining and performing steps, this just means that the abstract idea is being recited broadly enough to monopolize all possible equations or algorithms that might be used (Please also see MPEP 2106.04(a)(2)(III)(A), (B), (C), and (D). The step “predicting…” is further limit in claim 4 as “wherein the predicting is performed using one or more machine learning algorithms comprising at least one of a multiple linear regression algorithm, a convolutional neural network and one or more decision trees” which is related to mathematical concept/calculation.
The grouping of “mathematical concepts” in the 2019 PEG includes “mathematical calculations” as an exemplar of an abstract idea. 2019 PEG Section I, 84 Fed. Reg. at 52. Thus, limitation (a) falls into the “mathematical concept” grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(ii) and (iii). The recited steps in claim 1 when viewed as a whole recites a mental process per MPEP 2106.04(a)(2)(III)(A),(B), (C) and (D).
The recited steps may be carried out as a mental process if the algorithm is simple enough, and as a mathematical process if the algorithm is more complicated. The claimed invention thus recites an abstract idea.
Step 2, Prong Two: Practical application? No.
Claim 1 when viewed as a whole or in ordered combination does not integrate the abstract idea into a practical application. The recited steps “collecting operational data from a plurality of devices; predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data; and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices” encompasses data gathering, observation, evaluation, judgement, and opinion.
The recited “processing device” is merely a tool on which the method operates and hence does not integrate the abstract idea into a practical application (MPEP. 2106.05 (b)(II)).
The step “predicting…” encompasses a data gathering. Further, the step encompasses a mathematical concept (see explanation above in step 2, Prong One) would not integrate the judicial exception into a practical application (MPEP 2106.05(b)(II)). Further, the recited step does not include details of how the prediction was accomplished.
The step “generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices” encompasses an insignificant extra solution. The “(generated) transmission” and “alerts” are insignificant. It does not provide any information as to how the alerts are used, is at best the equivalent of merely adding the words “apply it” to the judicial exception. represents extra solution activity because it is a mere nomial or tangential addition to the claim. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). This limitation represents extra-solution activity because it is a mere nominal or tangential addition to the claim. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity, for instance the step of printing a menu that was generated through an abstract process in Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241-42 (Fed. Cir. 2016) and the mere generic presentation of collected and analyzed data in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016).
Step 2B: this part of the eligibility analysis evaluates whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. As explained with respect to Step 2A Prong Two, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, for reasons that are analogous to the discussion of additional elements at Prong 2.
Therefore, the claim is not eligible.
Independent claims 14 and 18 recite an apparatus and an article of manufacture which do not offer a meaningful limitation beyond generally linking the system and product claims to a particular technological environment, that is, implementation via a processing device. In other words, the system claim and the product claim are no different from the method claim 1 in substance; the method claim recites the abstract idea while the device claim recites generic components configured to implement the same abstract idea. The claims do not amount to significantly more than the underlying abstract idea.
Dependent claims 2, 3, 6-9, 15, 17, 19, add limitations which are data and data gathering merely extending the abstract idea without adding any additional element.
Dependent claims 4, 5, 16, add limitations which are data and mathematical calculation merely extending the abstract idea without adding any additional element.
Dependent claims 10-12 add imitations which is insignificant extra solution merely extending the abstract idea without adding any additional element.
Dependent claim 20 adds a limitation which is mathematical calculation and data gathering merely extending the abstract idea without adding any additional element.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 102 as (a)(1)being anticipated by Kuriakose et al. (hereinafter ‘Kuriakose)(USPAP. 20230099325).
Regarding claims 1, 14, and 18, Kuriakose discloses a method comprising:
collecting operational data from a plurality of devices (Pars. 6, 7, 32: The processing subsystem includes an operational details collection module configured to collect enterprise operational details associated with one or more enterprise services from an operational database, end devices or IT systems; also see Par. 54);
predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data (Pars. 6, 7, 32-34: the operational details analysis module is also configured to process each of the one or more log messages and the one or more key performance indicator metrics identified by using a corresponding log message parsing technique and a metrics processing technique respectively. The operational details analysis module is also configured to analyze each of the one or more log messages and the one or more key performance indicator metrics using a log analysis technique and a multivariate metric analysis technique respectively upon processing. The processing subsystem also includes an anomaly detection module configured to detect one or more anomalies within one or more analyzed log messages and one or more analyzed key performance indicator metrics using a corresponding point process anomaly detection technique and a multivariate metric anomaly detection technique respectively by utilizing a trained neural network models. The anomaly detection module is also configured to obtain one or more log clusters and one or more key performance indicator metrics clusters based on detection of the one or more anomalies within the one or more analyzed log messages and the one or more analyzed key performance indicator metrics respectively);
and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices (Pars. 6, 7, 32, 33: The incident recognition module also includes an incident cause description sub-module configured to generate an incident description for user interpretation by utilizing an incident recognition summarization model based on an analysis of the root cause associated with the one or more incidents); wherein the steps of the method are executed by a processing device operatively coupled to a memory (Abstract; Par. 6).
Regarding claims 2, 15, and 19, Kuriakose discloses wherein the operational data is collected via respective software agents in the respective ones of the plurality of devices and comprises data corresponding to at least one of connection status, power consumption, workloads, crashes, processing failures, data transmission failures, throughput, latency, central processing unit utilization and memory utilization of the respective ones of the plurality of devices (Par. 26: failed log category may include log cluster which contains messages with erroneous levels or erroneous keywords. Par. 27: performance indicator metrics. Par. 34: the operational details analysis module also processes each of the one or more log messages and the one or more key performance indicator metrics identified by using a corresponding log message parsing technique and a metrics processing technique respectively. Here, the log message parsing technique includes identifying parameters of the one or more log messages through regex match and replacing one or more symbols and one or more numbers of the one or more log messages. Again, the metric processing technique includes key performance indicator filtering technique and key performance indicator normalization. The KPI selection for dimension reduction functions is based on the concept of correlation clusters. The operational details analysis module clusters various metrics based on correlation between the metrics. Then, representatives with high variations are selected from each cluster so metrics with all patterns for analysis are available).
Regarding claim 3, Kuriakose discloses wherein predicting the one or more details corresponding to the disposition of the respective ones of the plurality of devices comprises determining whether there is a degradation of health of the respective ones of the plurality of devices based on a least one of a rate of the crashes, a rate of the processing failures, a rate of the data transmission failures, decreased throughput, decreased workloads, decreased connectivity, increased power consumption, increased latency, increased central processing unit utilization and increased memory utilization over designated time periods (Pars. 31-34 and 54).
Regarding claim 4, Kuriakose discloses wherein the predicting is performed using one or more machine learning algorithms, the one or more machine learning algorithms comprising at least one of a multiple linear regression algorithm, a convolutional neural network and one or more decision trees (Pars. 43, 49, 54).
Regarding claim 5, Kuriakose discloses the predicting is performed using one or more machine learning algorithms; and the method further comprises training the one or more machine learning algorithms with historical data comprising disposition of multiple devices and corresponding operational data and warranty data for the multiple devices (Pars. 43, and 45-49).
Regarding claims 6, 17, and 20, Kuriakose discloses wherein the predicting comprises using one or more machine learning algorithms to analyze an input dataset comprising one or more independent variables, wherein the one or more independent variables comprise data corresponding to at least one of connection status, power consumption, workloads, crashes, processing failures, data transmission failures, throughput, latency, central processing unit utilization, memory utilization, age, warranty status, warranty type and model of the respective ones of the plurality of devices (Pars. 45-50).
Regarding claim 7, Kuriakose discloses wherein the predicting is further based at least in part on data corresponding to at least one of age, warranty status, warranty type, and model of the respective ones of the plurality of devices (Par. 32).
Regarding claim 8, Kuriakose discloses the predicting is further based at least in part on data corresponding to at least one of a status of one or more parts, a health of one or more parts, an age of one or more parts, a model of one or more parts and a type of one or more parts of the respective ones of the plurality of devices (Pars. 32-36).
Regarding claim 9, Kuriakose discloses wherein collecting the operational data comprises scanning at least one network to detect whether the respective ones of the plurality of devices are active on the at least one network (Pars. 6 and 19-21).
Regarding claim 16, the predicting is performed using one or more machine learning algorithms; and the processing device is further configured to train the one or more machine learning algorithms with historical data comprising disposition of multiple devices and corresponding operational data and warranty data for the multiple devices (Pars. 43, 49, 54).
Claim Rejections - 35 USC § 103
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.
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriakose and Mahajan et al. (USPAP. 20200126027)(hereinafter “Mahajan”).
Regarding claim 9, Kuriakose does not explicitly disclose “wherein the one or more details comprise at least one of whether the respective ones of the plurality of devices are recommended to be resold, whether the respective ones of the plurality of devices are recommended to be recycled, when the respective ones of the plurality of devices are recommended to be resold, when the respective ones of the plurality of devices are recommended to be recycled, a resale value of the respective ones of the plurality of devices, and a recycle value of the respective ones of the plurality of devices”.
Mahajan teaches “wherein the one or more details comprise at least one of whether the respective ones of the plurality of devices are recommended to be resold, whether the respective ones of the plurality of devices are recommended to be recycled, when the respective ones of the plurality of devices are recommended to be resold, when the respective ones of the plurality of devices are recommended to be recycled, a resale value of the respective ones of the plurality of devices, and a recycle value of the respective ones of the plurality of devices” (Mahajan: Abstract, Pars. 48 and 49 teaches that the asset may require diagnostics to determine operational/functional and cosmetic status and/or other services to prepare it for final disposition. Data may be collected from the asset, the receiving process and a database containing information related to the asset item number and the record for the specific asset. An asset profile is created to which business rules are applied which may determine a preliminary disposition for the asset. Profiles for assets passing preliminarily dispositioning are processed through an optimal value server. The embodiments disclosed herein perform a value-based process to identify and direct the optimal disposition of new or used items. Devices (i.e. items) may be dispositioned along multiple categories, for example, an item may go to (1) a forward channel, such as an insurance channel or re-shelved for resale as-is, (2) auction (may be sold to resellers as is or with minor repairs), (3) remanufacture (may be repaired by an outside vendor and resold), (4) refurbish (minor cosmetic improvements required by an outside vendor) or (5) salvage (scrapped), and more. Intermediate dispositional categories may also be included for adjustments that may be made in a receiving warehouse, such as repair for minor repairs or buff and polish for removing light scratches or other defects. Categories may be identified according to product, receiving and market needs. In an exemplary embodiment, a rules logic module and repository comprise business and operational rules related to the process steps required for receiving a mobile device into inventory with the goal of recognizing its maximum value disposition. Example gateway dispositions for a mobile device may include: rejection when a device is returned locked and must be returned to the sender; disposition requirements that are set by the original selling channel; disposition rules based on item details, such as obsolete SKUs that may be required to be sent to auction regardless of its functional status or physical condition; rules that override warranties, and more. Rules may be set for any kind of business or operational requirements. Rule complexity may range from very simple standalone rules to very complex rules with multiple dependencies).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Kuriakos’ invention using Mahajan's invention to arrive at the claimed invention specified in claim 9 to analyze an individual item based on item, product and market/financial factors and directs the item to the appropriate disposition bucket (Mahajan: Par. 5).
Regarding claim 10, Kuriakose and Mahajan disclose everything as applied above. In addition, Mahajan teaches wherein the one or more alerts comprise a recommendation to at least one of resell and recycle a given one of the respective ones of the plurality of devices within a designated time period (Pars. 48 and 49).
Regarding claim 11, Kuriakose and Mahajan disclose everything as applied above. In addition, Mahajan teaches generating at least one user interface comprising the respective ones of the plurality of devices that are recommended to be resold with corresponding resale values of the respective ones of the plurality of devices that are recommended to be resold (see Fig. 4 and Pars. 48-52).
Regarding claim 12, Kuriakose and Mahajan disclose everything as applied above. In addition, Mahajan teaches generating at least one user interface comprising the respective ones of the plurality of devices that are recommended to be recycled with corresponding recycle values of the respective ones of the plurality of devices that are recommended to be recycled (Fig. 4, Pars. 48-52).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
S. Petryschuk, “How to find IP Adresses on a network and monitor their usage,” https://www.auvik.com/franklyit/blog/find-ip-addresses-on-network/, Accessed April 30, 2024, 12 pages (hereinafter “Petryschuk) (submitted by Applicants) discloses how to find IP addresses on a network and monitor their usage. Petryschuk discloses the importance of IP addressing in networking, how to assign IP addresses, how to find all IP addresses on a network, and how useful a work scanner is (Pages. 1-7).
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/PHUONG HUYNH/Primary Examiner, Art Unit 2857 September 1, 2026
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