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 .
1. This action is responsive to the application filed on 03/31/2025.
2. Claims 1-20 are pending.
3. Claims 1-20 are rejected.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a device, method, or product and is a method of assigning as task using a computer which is a process (Step 1: YES).
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent medium Claim 11. Claim 1 recites the limitations of (abstract ideas highlighted in italics):
receiving data from managed devices in a managed network, the data being indicative of device function and user interaction with managed devices;
discovering an anomaly in the data, the anomaly being indicative of an event experienced at a portion of the managed network;
using an AI engine, analyzing the anomaly and data related to the anomaly to determine additional information relevant to the anomaly that is not present in the received data;
generating, based on the AI engine, a data gathering mechanism to collect the additional information;
distributing the data gathering mechanism to one or more of the managed devices;
receiving collected data responsive to the distributed data gathering mechanism;
based on the collected data, determining an alteration to the managed network to resolve the anomaly;
and implementing the alteration in the managed network.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of mental process. Receiving data, discovering an anomaly, analyzing the anomaly and data, distributing the data, receiving collected data, and determining an alteration based on the collected data, recite a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process that can be performed by the human mind, including, for example, observations, evaluations, judgements, and opinions, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The method in Claim 1 is just applying generic computer components (i.e., computing devices and artificial intelligence) to the recited abstract limitations. Claim 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Additionally the limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Receiving data, discovering an anomaly, analyzing the anomaly and data, distributing the data, receiving collected data, and determining an alteration based on the collected data recite a concept performed in the human mind. But for the “computing devices” language, the claim encompasses a user simply using judgement and observation to compare requests to access a resource using his/her mind. The mere nominal recitation of a generic device does not take the claim out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The method in Claim 1 is just applying generic computer components and artificial intelligence to the recited abstract limitations. Claim 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a method with a computer-based device (Claim 1) and a medium (claim 11). The computer hardware is recited at a high-level of generality (i.e., as a generic device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 11 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Insert comment if appropriate, MPEP 2106.05(f), where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1 and 10 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-10 and 12-20 further define the abstract idea that is present in their respective independent claims 1 and 11 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 2-10 and 12-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
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.
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.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Michael Zaron et al (US 20210105293 A1), hereinafter “Zaron” in view of Awadesh Tiwari et al (US 20210256397 A1), hereinafter “Tiwari”.
Regarding Claim 1, Zaron discloses a method of artificial intelligence (AI)-based system anomaly diagnosis and remediation (Zaron, Abstract, anomaly detection in a networked control system. Paragraph 0043, detecting abnormal fluctuations in order to perform proper actions), the method comprising:
receiving data from managed devices in a managed network, the data being indicative of device function and user interaction with managed devices (Zaron, Fig 1, Paragraph 0024, receiving information (e.g., data packets or network traffic of any type or protocol and containing information such as sensor data) transmitted over a network at a device);
discovering an anomaly in the data, the anomaly being indicative of an event experienced at a portion of the managed network (Zaron, Paragraph 0024, anomaly detection within received data over a network. Paragraph 0025, parsing the data packets to determine whether information relevant to anomaly detection is contained);
However, Zaron fails to explicitly disclose using an AI engine, analyzing the anomaly and data related to the anomaly to determine additional information relevant to the anomaly that is not present in the received data; generating, based on the AI engine, a data gathering mechanism to collect the additional information; distributing the data gathering mechanism to one or more of the managed devices; receiving collected data responsive to the distributed data gathering mechanism; based on the collected data, determining an alteration to the managed network to resolve the anomaly.
Tiwari, from the same or similar field of endeavor, discloses using an AI engine, analyzing the anomaly and data related to the anomaly to determine additional information relevant to the anomaly that is not present in the received data (Tiwari, Paragraph 0041, detecting and predicting anomalies in any data type using artificial intelligence. Paragraph 0046, utilize the anomalous behavior characteristics to classify the detected anomalies into an anomaly category. Based on the anomaly category, illustrative embodiments determine a root cause of an anomaly and automatically perform a set of one or more action steps to pre-empt, prevent, or remediate the anomaly. Paragraph 0064, determining a CPU behavior to be anomalous, then the computer looks at the application running, also looks at predicted future behavior, as well past behavior, for the same system versus time and applications and business key performance indicators as response time);
generating, based on the AI engine, a data gathering mechanism to collect the additional information (Tiwari, Paragraphs 0042-0043, assembling parameters within a tie series data into different zones, wherein parameters include metrics data, event logs, system level notifications, sensor readings, or the like. Illustrative embodiments identify and derive these zones using an auto-detection engine. The artificial intelligence models use this information as input);
distributing the data gathering mechanism to one or more of the managed devices (Tiwari, Paragraph 0042, analyze second order nature of an initial point and a final point of the parameters for a given period of time or an interval of time);
receiving collected data responsive to the distributed data gathering mechanism (Tiwari, Paragraph 0042, identify relationships between parameters within defined beginning and ending points of the given time period);
based on the collected data, determining an alteration to the managed network to resolve the anomaly (Tiwari, Paragraph 0041, using artificial intelligence in order to predict and detect anomalies and recommend next action steps for remediation. Paragraph 0044, using artificial intelligence to classify anomalies into various categories, such as, for example, a point anomaly category, a contextual anomaly category, or a collective anomaly category, based on the anomalous behavior characteristics. Paragraph 0046, utilize the anomalous behavior characteristics to classify the detected anomalies into an anomaly category. Based on the anomaly category, illustrative embodiments determine a root cause of an anomaly and automatically perform a set of one or more action steps to pre-empt, prevent, or remediate the anomaly. Paragraph 0064, if CPU behavior for a zone appears anomalous, then the computer looks at the application running, also looks at predicted future behavior, as well past behavior, for the same system versus time and applications and business key performance indicators as response time. If all these appear to be “non-obvious”, then the computer initiates process kill or diversion of work load actions to clear the anomalies. Thereafter, the process terminates).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Zaron in view of Tiwari in order to further modify the method of anomaly detection in a networked control system from the teachings of Zaron with the method of anomaly detection using artificial intelligence from the teachings of Tiwari.
One of ordinary skill in the art would have been motivated because by using artificial intelligence the system can narrow down the root cause of the anomaly and determine how to resolve/remediate the anomaly (Tiwari – Abstract, Paragraphs 0006, 0041, 0046).
Regarding Claim 2, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses further comprising:
determining multiple different underlying issues that are a potential cause of the anomaly based on the analysis of the anomaly and data related to the anomaly (Tiwari, Paragraph 0046, determine a root cause of an anomaly based on the anomalous behavior of each parameter or group of parameters in the particular zone. Paragraph 0047, detecting root causes of anomalies);
and based on the collected data, identifying an actual underlying issue of the multiple different underlying issues that is the cause of the anomaly, wherein the additional information includes data used to identify which of the multiple different underlying issues is the actual underlying issue that is the cause of the anomaly (Tiwari, Paragraph 0046, determining, using the artificial intelligence, a root cause of an anomaly and perform a set of one or more action steps to pre-empt, prevent, or remediate the anomaly).
Regarding Claim 3, the combination of Zaron and Tiwari disclose the method of claim 2 above, where Tiwari further discloses wherein the anomaly is indicative of a malfunctioning device or a suboptimal interaction by a user with one of the managed devices (Tiwari, Paragraph 0064, examples of types of anomalies, e.g., CPU behavior).
Regarding Claim 4, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Zaron further discloses wherein the AI engine is trained on data of the managed network that is indicative of normal operation of the managed devices, optimal interaction of users relative to the managed devices, and optimal behavior of the managed devices (Zaron, Paragraph 0033, train a prediction model using artificial intelligence (e.g., machine learning) for future anomaly detection and/or for specifying new forms of “relevant” information. Paragraph 0058, train a prediction model for anomaly detection and for monitoring network traffic using the prediction model to execute anomaly detection).
Regarding Claim 5, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses wherein the discovering the anomaly in the data includes:
identifying a pattern of operations in one or more of the managed devices; identifying a pattern of operations in a software that is running on one or more of the managed devices; or identifying a pattern of interoperability data related to a product update (Tiwari, Paragraph 0023, detect anomalies within time series data received from registered client devices based on identifying zonal parameter behavior characteristics and generating non-linear scores for artificial intelligence models processing the zonal parameter behavior characteristics of the time series data. The time series data may be any type of time series data, such as, for example, Internet of Things data, Information Technology data, sensor data, financial data, transaction data, streaming data, and the like. Paragraph 0042, identify relationships between parameters within defined beginning and ending points of the given time period).
Regarding Claim 7, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses further comprising submitting to an administrator the data gathering mechanism, wherein the distributing the data gathering mechanism is performed responsive to an indication of an approval received from the administrator (Tiwari, Paragraph 0046, sending a notification to a system or security analyst regarding the root cause of the anomaly and recommending the set of action steps to be taken to pre-empt or remediate the anomaly).
Regarding Claim 8, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses further wherein the data gathering mechanism includes:
an identification of one or more users or one or more of the managed devices that are directly affected by the anomaly (Tiwari, Paragraph 0060, computer receives time series data including univariate and multivariate parameters from a client device via a network);
and an identification of at least one request direct to the additional information (Tiwari, Paragraph 0060, computer identifies behavioral characteristics of the univariate and multivariate parameters during the specified time interval within the time series data based on the analysis. Behavioral characteristics for an Information Technology system, for example, may be CPU versus time and rate of change of CPU with time, as well as criticality of application. Paragraph 0062, computer identifies anomalous behavior characteristics of the univariate and multivariate parameters using the optimal artificial intelligence model. Paragraph 0064, determination of root cause of the anomalies based on the corresponding anomaly category).
Regarding Claim 9, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses wherein:
the anomaly is discovered prior to submission of a ticket in a service management system (Tiwari, Paragraph 0023, server provides anomaly detection services for registered client devices. For example, server detects anomalies within time series data received from registered client devices based on identifying zonal parameter behavior characteristics and generating non-linear scores for artificial intelligence models processing the zonal parameter behavior characteristics of the time series data);
and the alteration is implemented proactively (Tiwari, Paragraph 0046, based on the anomaly category, determining a root cause of an anomaly and automatically perform a set of one or more action steps to pre-empt, prevent, or remediate the anomaly).
Regarding Claim 10, the combination of Zaron and Tiwari disclose the method of claim 1 above, where Tiwari further discloses wherein the anomaly includes:
non-use of a licensed software; a first user of a new hardware; a modification of a role of a user in the managed network; a new staff member; a change in location of a user; a repeated disabling of a firewall; or a repeated malfunction of a device such as a periodic and repetitive low battery warning (Tiwari, Paragraph 0060, behavioral characteristics for an Information Technology system, for example, may be CPU versus time and rate of change of CPU with time, as well as criticality of application. Thus, in this example, the parameter is CPU, time, impact, business key performance indicator, and application, as well as attached application criticality score. Paragraph 0064, if CPU behavior for a zone appears anomalous, then the computer looks at the application running, also looks at predicted future behavior, as well past behavior, for the same system versus time and applications and business key performance indicators as response time).
Claim 11 carries similar limitations as discussed with regards to Claim 1 above and therefore is rejected for the same reason.
Regarding Claim 12, this claimed limitation is the same as the limitation addressed to Claim 2 above. Therefore, it is rejected under the same rationale.
Regarding Claim 13, this claimed limitation is the same as the limitation addressed to Claim 3 above. Therefore, it is rejected under the same rationale.
Regarding Claim 14, this claimed limitation is the same as the limitation addressed to Claim 4 above. Therefore, it is rejected under the same rationale.
Regarding Claim 15, this claimed limitation is the same as the limitation addressed to Claim 5 above. Therefore, it is rejected under the same rationale.
Regarding Claim 17, this claimed limitation is the same as the limitation addressed to Claim 7 above. Therefore, it is rejected under the same rationale.
Regarding Claim 18, this claimed limitation is the same as the limitation addressed to Claim 8 above. Therefore, it is rejected under the same rationale.
Regarding Claim 19, this claimed limitation is the same as the limitation addressed to Claim 9 above. Therefore, it is rejected under the same rationale.
Regarding Claim 20, this claimed limitation is the same as the limitation addressed to Claim 10 above. Therefore, it is rejected under the same rationale.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zaron and in view of Tiwari, as applied to claims 1 and 11 above, and further in view of Pavan Kumar Penugonda et al (US 20240119492 A1), hereinafter “Penugonda”.
Regarding Claim 6, the combination of Zaron and Tiwari disclose the method of claim 1 above.
However, the combination of Zaron and Tiwari fail to explicitly disclose wherein the data gathering mechanism includes: a survey directed to one or more users who are associated with the anomaly; and questions in the survey are directed to collection of the additional information.
Penugonda, from the same or similar field of endeavor, discloses wherein the data gathering mechanism includes:
a survey directed to one or more users who are associated with the anomaly (Penugonda, Paragraphs 0057-0058, transmitting a survey or other solicitation for feedback from a product to the affected user/consumer);
and questions in the survey are directed to collection of the additional information (Penugonda, Paragraphs 0057-0058,bBased on obtaining this event (this lack of use over this period of time), the program code can generate and send a survey to the user to determine why the user is not using the device).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Zaron in view of Tiwari and in further view of Penugonda in order to further modify the method of anomaly detection in a networked control system from the teachings of Zaron and the method of anomaly detection using artificial intelligence from the teachings of Tiwari with the method of continuous granular reviews and ratings from the teachings of Penugonda.
One of ordinary skill in the art would have been motivated because by the user providing feedback on a device/product the system will be able to determine anything else that is wrong/damaged in the device/product (Penugonda – Abstract, Paragraphs 0057-0060).
Regarding Claim 16, this claimed limitation is the same as the limitation addressed to Claim 6 above. Therefore, it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. All the references listed on 892 are related to the subject matter of identify anomalies in a system and implementing alterations.
Some of the prior art include:
US 12184480 B1, which discloses a method of detecting and mitigating network operation validation anomalies.
US 20220103591 A1, which discloses a method of detecting anomalies in network communication.
US 10027553 B2, which discloses a method of analytics for system monitoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAVIER O GUZMAN whose telephone number is (571)270-0588. The examiner can normally be reached Monday - Friday 8 am to 4 pm EST.
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, Jorge L. Ortiz-Criado can be reached at (571)272-7624. 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.
/JAVIER O GUZMAN/ Primary Examiner, Art Unit 2496
