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 .
Response to Amendment
The amendment filed on 11/4/2025 has been entered. Claims 1-6, 8-16, 18-19, and 21-23 remain pending in the present application. The 35 U.S.C. 101 rejection has been withdrawn in light of the applicant’s arguments and remarks filed on 11/4/2025. Claims 7, 17, and 20 have been canceled and claims 21-23 are new claims.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-6, 12-13, 15-19, and 21-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cohen et al. (US PGPUB 20190311273).
Regarding Claims 1, 15, and 18; Cohen teaches; A method comprising: receiving data corresponding to operation of a plurality of elements, wherein the plurality of elements comprise at least one of a plurality of devices and a plurality of device components, and wherein the data corresponding to the operation of the plurality of elements comprises one or more operational states for respective ones of the plurality of elements; (Cohen; at least Fig. 8; paragraph [0131]; disclose receiving current state data from industrial machines that will be utilized by a prediction model to determine future state operations of the industrial machines)
predicting, using one or more machine learning algorithms, one or more future operational states of one or more elements of the plurality of elements based, at least in part, on the data corresponding to the operation of the plurality of elements, wherein the predicting further comprises using a conformal prediction model to predict the one or more future operational states of the one or more elements based on respective probabilities of the one or more future operational states; and (Cohen; at least paragraph [0027], [0042], and [0139]; disclose using the current operational data and applying it to a prediction model that can then determine a future operational state of the industrial machine and further, wherein the current operational data is used in conjunction with a state sequence model (i.e. conformal prediction model) to predict the future operational states based on probabilities of state transitions resulting in the future states)
identifying, using the one or more machine learning algorithms, one or more actions to prevent the one or more elements from transitioning to the future operational state; (Cohen; at least paragraphs [0047] and [0139]; disclose wherein the system updates/provides controls to the machine to prevent it from entering a future operational state (i.e. stopping the machine))
wherein the steps of the method are executed by a processing device operatively coupled to a memory. (Cohen; at least paragraphs [0010] and [0027]).
Regarding Claim 2; Cohen teaches; The method of claim 1 wherein: the data corresponding to the operation of the plurality of elements comprises a plurality of log entries; and (Cohen; at least paragraph [0010])
the method further comprises collating data in the plurality of log entries based, at least in part, on one or more of changes in the one or more operational states, durations of the one or more operational states before changing to a different operational state and whether the changes in the one or more operational states resulted in failure of one or more of the plurality of elements. (Cohen; at least paragraphs [0010] and [0110]-[0111]).
Regarding Claim 3; Cohen teaches; The method of claim 2 further comprising collating the data in the plurality of log entries based, at least in part, on one or more of device type, device component type, alerts indicating one or more issues with the plurality of elements and severity of the alerts. (Cohen; at least paragraph [0158]).
Regarding Claim 4; Cohen teaches; The method of claim 2, further comprising training the one or more machine learning algorithms with at least a portion of the data corresponding to the operation of the plurality of elements. (Cohen; at least paragraphs [0039]-[0040]).
Regarding Claims 5, 16, and 19; Cohen teaches; The method of claim 1 wherein predicting the one or more future operational state of the one or more elements comprises using a stochastic model to predict respective probabilities of one or more future operational states of the one or more elements based on a most recent known operational state of the one or more elements. (Cohen; at least paragraphs [0010], [0024], [0091], and [0112]).
Regarding Claim 6; Cohen teaches; The method of claim 5 wherein predicting the future one or more operational state of the one or more elements further comprises generating a matrix in accordance with the stochastic model, wherein the matrix comprises one or more rows corresponding to the most recent known operational state of the one or more elements and one or more columns corresponding to the one or more future operational states of the one or more elements. (Cohen; at least paragraphs [0066]-[0069] and [0109]).
Regarding Claims 12 and 21; Cohen teaches; The method of claim 1 further comprising identifying, using the one or more machine learning algorithms, a time period in which to perform the one or more actions. (Cohen; at least paragraphs [0043] and [0139]).
Regarding Claims 13 and 22; Cohen teaches; The method of claim 12 wherein the one or more actions and the time period in which to perform the one or more actions are transmitted to at least one user device. (Cohen; at least paragraph [0047]).
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.
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 8-9, 14, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (US PGPUB 20190311273) in view of Morris, II et al. (US PGPUB 20160350671).
Regarding Claim 8; Cohen teaches; The method of claim 7 wherein predicting the future one or more operational state of the one or more elements further comprises using a random forest model to compute a conformity value of the respective probabilities of the one or more future operational states. (Cohen; at least paragraphs [0119]-[0120] and [0125]).
Cohen appears to be silent on; The method of claim 7 wherein predicting the future operational state of the one or more elements further comprises using a random forest model to compute a conformity value of the respective probabilities of the one or more future operational states.
However, Morris teaches; The method of claim 7 wherein predicting the future operational state of the one or more elements further comprises using a random forest model to compute a conformity value of the respective probabilities of the one or more future operational states. (Morris; at least paragraphs [0008] and [0084]-[0085]; disclose a system and method for predicting future operational states of machines/components wherein the system and method includes developing prediction models based on predicted probabilities of occurrences of events, and wherein the system and method utilizes random forest algorithms for developing these models).
Cohen and Morris are analogous art because they are from the same field of endeavor or problem solving area of, future operation and intervention control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have incorporated the known method of using random forest algorithms as taught by Morris with the known system of an operation prediction and control system of Cohen in order provide a means for more efficiently analyzing the huge amounts of data made available and using the random forest techniques to make the predictive model more accurate as taught by Morris (paragraph [0006]).
Regarding Claim 9; the combination of Cohen and Morris teach; The method of claim 1 wherein identifying the one or more actions comprises using the data corresponding to the operation of the plurality of elements as training data to generate a decision tree. (Morris; at least paragraphs [0085] and [0106]).
Regarding Claims 14 and 23; the combination of Cohen and Morris teach; The method of claim 1 wherein the one or more machine learning algorithms comprise a random forest machine learning algorithm. (Morris; at least paragraphs [0085] and [0106]).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (US PGPUB 20190311273) in view of Morris, II et al. (US PGPUB 20160350671) in further view of Chattopadhyay et al. (US PGPUB 20210097449).
Regarding Claim 10; the combination of Cohen and Morris appear to be silent on; The method of claim 9 wherein generating the decision tree comprises: computing respective Gini indexes for respective ones of a plurality of features in the data corresponding to the operation of the plurality of elements; and identifying a root node of the decision tree based, at least in part, on the respective Gini indexes.
However, Chattopadhyay teaches; The method of claim 9 wherein generating the decision tree comprises: computing respective Gini indexes for respective ones of a plurality of features in the data corresponding to the operation of the plurality of elements; and identifying a root node of the decision tree based, at least in part, on the respective Gini indexes. (Chattopadhyay; at least paragraphs [0101]-[0112]; disclose a system and method in which generating root nodes includes computing respective Gini index for each data set and identifying the root node based on the respective features having the best Gini index).
Cohen, Morris, and Chattopadhyay are analogous art because they are from the same field of endeavor or problem solving area of, machine learning optimization control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have incorporated the known method of using Gini index computation for determining root nodes as taught by Chattopadhyay with the known system of an operation prediction and control system of Cohen and Morris in order provide a means for assisting in determination of optimal solutions while requiring significantly less computational resources as taught by Chattopadhyay (paragraph [0033]).
Regarding Claim 11; the combination of Cohen, Morris, and Chattopadhyay teach; The method of claim 10 wherein, for at least one feature of the plurality of features, a computed Gini index is based, at least in part, on a plurality of actions that were performed to prevent at least a portion of the plurality of elements from transitioning to a plurality of future operational states, and a number of times respective ones of the plurality of actions were performed. (Chattopadhyay; at least paragraphs [0102]-[0110]; disclose computing the Gini index for a plurality of datasets and wherein Cohen discloses wherein the datasets includes a number times a plurality of actions were performed).
Response to Arguments
Applicant’s arguments, see pages 7-11, filed 11/4/2025, with respect to the 35 U.S.C. 101 rejection have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-6, 8-16, 18-19, and 21-23 has been withdrawn.
Applicant's arguments filed on 11/4/2025 with respect to the 35 U.S.C. 102 rejection have been fully considered but they are not persuasive.
In particular, the applicant asserts that the reference of Cohen does not explicitly teach the limitations of, “wherein the predicting further comprises using a conformal prediction model to predict the one or more future operational states of the one or more elements based on respective probabilities of the one or more future operational states;”. The applicant expands by referring to page 12, lines 10-15 further supporting the limitation as presented with additional details as to how the conformal prediction model is utilized for predicting future operational states.
The office appreciates the applicant’s explanation of the matter, however, the office would like to first point out, that the description in the specification is provided as an example and not a formal definition as to what clearly defines what the conformal prediction model entails within the context of the claims. As such, the conformal prediction model is being treated as any model that is utilized for prediction of future states, which is clearly taught in Cohen (see paragraphs [0027], [0042], and [0139]) as explained in the rejection section above. Further, the limitation requires that the future operational states be based upon respective probabilities of occurrence, which is also explicitly taught in Cohen (see paragraph [0139], step 3e-3g).
In order to further prosecution, the office recommends providing more of the details as to how the conformal prediction model functions, such as in the cited sections of the specification, to help distinguish the present application over the current prior art. This will make it clear in the claims as to the differences in the functionality of the cited reference compared to the present application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Morris, II et al. (US PGPUB 20160350671): disclose a system and method for predicting future outcomes in a process system such that mitigating actions can be taken to prevent damage to a particular process.
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.
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/CHRISTOPHER W CARTER/Examiner, Art Unit 2117