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 .
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.
For claims 1, 11, and 20:
Step 2A prong 1: the claims are considered an exception because they recite concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion) and/or using pen and paper, and they do not necessarily require generic or any computer components to be completed. The claims recite computer components that execute the operations/steps performed (“computer, automatically, computing system, computer ,memory, processors, computer instructions, computer program product, medium, and storage”), however that does not change the fact that the operations/steps can still be performed in the mind only, and that the computer components are not necessary. The operations of “receiving notification, obtaining/providing/displaying recommendations, capturing actions/timestamps, and generating a report”, and fall under evaluation and judgement; “xxx” falls under observation.
Step 2A prong 2: The recited additional elements do not integrate the judicial exception into a practical application because (i) the generic elements recited do not add a meaningful limitation to the abstract idea as they amount to simply implementing the abstract idea utilizing a computer; (ii) Also, the object of the observing and evaluating in the claims, is very vague (operational events and their responses), and could be anything including human activity or an output, possibly in the form of a printout or view screen. If the object is a system, itis is not positively claimed nor specified, and as such the claim is not practical, but rather preemptive of all systems and applications; (iii) the objects of the determining and evaluating operations are not sufficiently complex and/or laborious that would mandate the use of computing components.
Step 2B: The claim recites the additional activity of capturing timestamps. Officially note that this activity is well-understood, routine, and conventional.
For claims 2-7, 9, 12-17, and 19, the claim further specifies descriptions of the information exchanged, from independent claims, the utilization of a trained machine learning model, and information exchanged with the model. This addition does not change the fact that the operations can be performed in the mind only (Step 2A prong 1), that the machine learning model is neither positively claimed, nor that it’s necessary. Using a third-party machine learning model does not integrate the invention into a practical application because the general operation is not claimed as complex or laborious which requires the model (Step 2A prong 2), and that machine learning model is well-understood, routine, and conventional (Step 2B).
For claims 8 and 18, the claim further specifies that the information exchanged is screen shot of a user interface of the computer system and telemetry data. Reading information from a screen and getting telemetry can be performed in the mind only (Step 2A prong 1), this not integrate the invention into a practical application because complex or laborious which requires the model (Step 2A prong 2), and that screen shots and unspecified telemetry is well-understood, routine, and conventional (Step 2B).
For claim 10, the claim further specifies recommendation actions includes an indication of an expected duration for performing each of the first set of one or more recommendation actions and a likelihood that performing each of the first set of one or more recommendation actions will resolve the operational event. This amounts to estimating duration and likelihood, which is a formula and still can be performed in the mind only (Step 2A prong 1), this not integrate the invention into a practical application because it is not complex or laborious enough to require computer components (Step 2A prong 2), and that estimating likelihood and duration is well-understood, routine, and conventional (Step 2B).
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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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Smith (US 2923/0375991 A1).
For claim 1, Smith teaches a computer-implemented method for responding to an operational event, the computer-implemented method comprising: receiving a notification of the operational event; (see abstract and other locations: view anomalous operation as said operational event) obtaining a first set of one or more recommendation actions for responding to the operational event; (see paragraph [0005], [0081], view suggested or recommended as said) providing the first set of one or more recommendation actions to a user responding to the operational event; (see [0035]: user/technician) capturing a first response action performed during responding to the operational event; obtaining, based at least in part on the first response action, a second set of one or more recommendation actions for responding to the operational event; (see [0083]: past remedial actions that were takento address issue suggests back and forth response actions until resolution to find underlying cause) displaying the second set of one or more recommendation actions based to the user responding to the operational event; (see [0075], [0079], and other locations) capturing a second response action performed during responding to the operational event; (see locations pointed to above) and generating a final outcome report of the operational event including the first response action, the second response action, and an automatically captured timestamp of the first response action and the second response action (see [0083]: reporting includes said).
For claim 2, Smith teaches the limitations of claim 1 for the reasons above and further teaches the first set of one or more recommendation actions are obtained by inputting one or more tools available for responding to the operational event, information regarding the operational event, and a profile of the user responding to the operational event into a trained machine learning model (dee [0007], [0055])
For claim 3, Smith teaches the limitations of claim 2 for the reasons above and further teaches the trained machine learning model is trained based on final outcome reports obtained for previous operational events (see [0055]: root/cause remedial actions based on problem actual remediated, i.e. final).
For claim 4, Smith teaches the limitations of claim 2 for the reasons above and further teaches the second set of one or more recommendation actions are obtained by inputting the one or more tools available for responding to the operational event, the information regarding the operational event, the profile of the user responding to the operational event, and the first response action into the trained machine learning model (see locations pointed to above, [0032], [0035], [0052]: information about use including interface devices and whether on site; view that as said profile).
For claim 5, Smith teaches the limitations of claim 2 for the reasons above and further teaches updating the trained machine learning model based on the final outcome report (see [0093]).
For claim 6, Smith teaches the limitations of claim 2 for the reasons above and further teaches the information regarding the operational event includes an identification of a computer system experiencing the operational event and an operational log of the computer system (see abstract, [0081]: machine identifier; acoustic data and other information is continuous and thus is log).
For claim 7, Smith teaches the limitations of claim 6 for the reasons above and further teaches the first response action includes one of the first set of one or more recommendation actions that was performed by the user and a response of the computer system to the one of the first set of one or more recommendation actions (see locations pointed to above: all root cause actions are available to view).
For claim 8, Smith teaches the limitations of claim 7 for the reasons above and further teaches the response of the computer system to the one of the first set of one or more recommendation actions includes one or more of a screen shot of a user interface of the computer system and telemetry data generated by the computer system (see [0080]: when viewing a screen, one views a screen shot).
For claim 9, Smith teaches the limitations of claim 6 for the reasons above and further teaches the second response action includes one of the second set of one or more recommendation actions that was performed by the user and a response by the computer system to the one of the second set of one or more recommendation actions (see locations pointed to above: root/cause analysis in general includes multiple steps and results until the root cause is remediated, as opposed to just the events).
For claim 10, Smith teaches the limitations of claim 1 for the reasons above and further teaches the first set of one or more recommendation actions includes an indication of an expected duration for performing each of the first set of one or more recommendation actions and a likelihood that performing each of the first set of one or more recommendation actions will resolve the operational event (see [0008] and other locations).
For claims 11-19, the claims recite essentially similar limitations as claims 1-9 respectively. Claims 11-19 are a system.
For claim 20, the claim recites essentially similar limitations as claim 1. Claims 20 is a computer program product.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YAIR LEIBOVICH whose telephone number is (571)270-3796. The examiner can normally be reached 8:00am-5:00pm.
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/YAIR LEIBOVICH/Primary Examiner, Art Unit 2114
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