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 .
Preliminary Amendment
The preliminary amendment filed 8/04/2025 has been entered. Claims 1-3, 9-12, 16-18, 20 have been amended.
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 Judicial Exceptions without significantly more. The claims recite mathematical relationships, mathematical formulas or equations, mathematical calculation and a mental process. This judicial exception is not integrated into a practical application because the recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims only recites generic computer components, which are well-understood, routine, and conventional.
Revised Patent Subject Matter Eligibility Guidance
The USPTO has published revised guidance on the application of § 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”). Under the Guidance, the Examiner first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Guidance, Step 2A, prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure (MPEP) § 2106.05(a)-(c), (e)-(h) (9th Ed., Rev. 08.2017, 2018)) (Guidance, Step 2A, prong 2).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do the Examiner then look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field (see MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. (Guidance (Step 2B)).
Evaluate Step 2A Prong One
(a) identify the specific limitation(s) in the claim that recites an abstract idea;
(b) determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
In TABLE 1 below, the Examiner identifies in italics the specific claim limitations that recite an abstract idea.
TABLE 1
Independent Claim 1
Analysis Under Revised Guidance
(a) A method of computing kinematic metrics for a node comprising steps of:
(b) receiving, at a cluster kinematics analysis system, a temporal sequence of data samples associated with the node, each data sample in the temporal sequence comprising a vector of N dimensions, the temporal sequence of data samples comprising at least a first data sample corresponding to a first time and a second data sample corresponding to a second time
“receiving…a temporal sequence of data samples” is an abstract idea, i.e., “mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes”. A person can receive a temporal sequence of data samples by observing, in the human mind, or with the aid of pen/paper.
(c) projecting, by the cluster kinematics analysis system, the first data sample into a projection space of a clustering model, the clustering model defining one or more clusters in the N dimensions and trained on data samples comprising vectors of the same N dimensions
“projecting… the data sample in to a projection space…” is an abstract idea, i.e., “a mathematical concept” or “mathematical calculation”. A set of data samples can be grouped and visualized in in the human mind or with the aid of pen/paper.
(d) calculating, by the cluster kinematics analysis system, a distance value associated with each of the one or more clusters in the clustering model for the first data sample
“calculating… a distance value …” is an abstract idea, i.e., a “mathematical calculation” or “mathematical formula”, to measure a distance between two objects. A person can calculate a distance in the human mind or with the aid of pen/paper.
(e) projecting, by the cluster kinematics analysis system, the second data sample into the projection space of the clustering model
“projecting… the data sample in to a projection space…” is an abstract idea, i.e., “a mathematical concept” or “mathematical calculation”. A set of data samples can be grouped and visualized in in the human mind or with the aid of pen/paper.
(f) calculating, by the cluster kinematics analysis system, a distance value associated with each of the one or more clusters in the clustering model for the second data sample
“calculating… a distance value …” is an abstract idea, i.e., a “mathematical calculation” or “mathematical formula”, to measure a distance between two objects. A person can calculate a distance in the human mind or with the aid of pen/paper.
(g) and calculating for the node, by the cluster kinematics analysis system, a first velocity associated with at least one cluster of the one or more clusters in the clustering model from the distance values associated with the at least one cluster calculated for the first data sample and the second data sample and a difference between the first time and the second time
“calculating …velocity …from the distance value… and a difference between the first time and the second time…” is an abstract idea, i.e., a “mathematical calculation”, “mathematical formula”, based on the well-known formular Speed = Distance / Time. A person can perform the calculating of velocity in the human mind or with the aid of pen/paper.
In view of the above analysis, Claim 1 recites an abstract idea under the Revised Guidance because the limitations (b) – (g) each recite mathematical relationship, mathematical calculation and/or a mental process. Independent claims 11 and 16 also recite an abstract idea because it includes similar limitations (b) – (g). Dependent claims 2-10, 12-15, 17-20 also recite abstract idea because they include limitations (b) – (g) by virtue of their dependencies to claims 1, 11 and 16, respectively.
Dependent claims 2-10, 12-15, 17-20 further recites additional limitations. However, these limitations also recite abstract idea, i.e., “mathematical concept – mathematical formulas or equations, mathematical calculations” similar to the limitations of claims 1, 11 and 16 discussed above.
Evaluate Step 2A Prong Two:
Evaluate whether the claim as a whole integrated the recited Judicial exception into a Practical Application of the exception.
Having determined that the claims recite a judicial exception, the analysis under the Guidance turns now to determining whether there are “additional element that integrate the judicial exception into a practical application”. The examiner determines whether the recited judicial exception is integrated into a practical application that exception by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions; and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
Independent claim 1 does not recite any additional element that integrate the judicial exception into a practical application. Independent claim 11 recites limitations “a non-transitory computer-readable medium”, “a data store”, “a processor”, which are simply a generic computer component to store and execute computer instructions, which causes a generic computer system to perform the operations recited in limitations (b)-(h). Independent Claim 16 further recites “a non-transitory computer readable medium”, which is a generic computer component such as a data storage. The “non-transitory medium” “processor” recited in the claims are so generically that is represents no more than mere generic computer component to apply the judicial exception on a computer. The recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. Guidance at 52 n.14 (“Performance of a claim limitation using generic computer components does not necessarily preclude the claim limitation from being in the mathematical concepts grouping.”)
Evaluate Step 2B:
Evaluate whether the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. See MPEP 2106.05(g). The claim does not add any specific limitations beyond what is well-understood, routine, and conventional. Here, claims 1, 11, 16 recite “non-transitory computer-readable medium”, “processor”, which are mere generic computer components that are recited at a high level of generality, and, as disclosed in the specification, is also well-understood, routine, conventional activity when expressed at this high level of generality. Mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims do not provide an inventive concept (significantly more than the abstract idea) and is not eligible.
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.
Claims 1-8, 10-19 are rejected under 35 U.S.C. 103 as being unpatentable over Arashanipalai (US 2020/0409339 A1), hereinafter “Arashanipalai”, and in view of Sripada (US 2015/0325000 A1), hereinafter “Sripada”.
As per claim 1, Arashanipalai teaches a method of computing kinetic metrics for a node comprising steps of:
“receiving, at a cluster kinematics analysis system, a temporal sequence of data samples associated with the node, each data sample in the temporal sequence comprising a vector of N dimensions, the temporal sequence of data samples comprising at least a first data sample corresponding to a first time and a second data sample corresponding to a second time” at [0105], [0132]-[0133] and Fig. 10;
(Arashanipalai teaches receiving a plurality of data records sequentially from a data stream, each of the plurality of data records having an associated timestamp)
“projecting, by the cluster kinematics analysis system, the first data sample into a projection space of a clustering model, the clustering model defining one or more cluster in the N dimensions and trained on data samples comprising vectors of the same N dimension” at [0105]-[0110], [0132]-[0136] and Figs. 10-11;
(Arashanipalai teaches building a cluster pattern for a plurality of time period by placing each data record of the plurality of data records into a corresponding cluster of a particular time period based on the associated timestamp of each data record using a machine learning model. The machine learning model is trained using a training data set to apply labels to the input data. Arashanipalai teaches at Fig. 10 the first data sample C collected between t0 and t1 is projected into a projection space 1004 of the clustering model)
“calculating, by the cluster kinematics analysis system, a distance within the projection space associated with each of the one or more clusters in the clustering model for the first data sample” at [0134]-[0136] and Fig. 11;
(Arashanipalai teaches calculating the distance between a cluster to other clusters for each of the time period)
“projecting, by the clustering kinematics analysis system, the second data sample into the projection space of the clustering model” at [0132]-[0133] and Figs. 10-11;
(Arashanipalai teaches at Fig. 10 the second data sample C’ is projected into a projection space 1004 of the clustering mode
“calculating, by the clustering kinematics analysis system, a distance within the projection space associated with each of the one or more cluster in the clustering model for the second data sample” at [0134]-[0136] and Fig. 11;
(Arashanipalai teaches calculating the distance between a cluster, such as cluster Ctriangle, to other clusters for each of the time period)
Arashanipalai does not teach “calculating for the node, by the cluster kinematics analysis system, a first velocity within the projection space associated with at least one cluster of the one or more clusters in the clustering model from the distances associated with the at least one cluster calculated for the first data sample and the second data sample and a different between the first time and the second time” as claimed. However, calculating velocity/speed based on distance and time is a well-known mathematic formular. Sripada teaches at [0072]-[0076] and Fig.8 a similar method for clustering time series data into a plurality of clusters, calculating distances between the clusters. Sripada teaches at [0076] that “the speed of the cluster may be determined by comparing a time value associated with the first frame to a time value associated with the second frame, and computing the distance between the two points, such that the rate equals the distance divided by the change in time”. Thus, it would have been obvious to one of ordinary skill in the art to combine Sripada with Arashanipalai’s teaching to calculate the speed of the cluster based on distance and time, as suggested by Sripada.
As per claim 2, Arashanipalai and Sripada teach the method of claim 1 discussed above. Arashanipalai also teaches:
“the temporal sequence of data samples further comprises a third data sample corresponding to a third time” at [0105]-[0110], [0132]-[0136] and Figs. 10-11;
“and the method further comprises steps of: projecting, by the cluster kinematics analysis system, the third data sample into the projection space of the clustering model” at [0105]-[0110], [0132]-[0136] and Figs. 10-11;
“calculating, by the cluster kinematics analysis system, a distance within the projection space associated with each of the one or more clusters in the clustering model for the third data sample” at [0105]-[0110], [0132]-[0136] and Figs. 10-11;
“calculating for the node, by the cluster kinematics analysis system, a second velocity within the projection space associated with the at least one cluster from the distance within the projection space associated with the at least one cluster calculated for the second data sample and the third data sample and a difference between the second time and the third time” at [0105]-[0110], [0132]-[0136] and Figs. 10-11; and
Sripada also teaches: “calculating for the node, by the cluster kinematics analysis system, an acceleration within the projection space associated with the at least one cluster from the first velocity and the second velocity calculated for the node and the difference between the second time and the third time” at [0076].
As per claim 3, Arashanipalai and Sripada teach the method of claim 2 discussed above. Arashanipalai also teaches: receiving, by the cluster kinematics analysis system, further data samples associated with the node, each of the further data samples corresponding with a subsequent time value; and upon receiving each of the further data samples, calculating new velocity and acceleration within the projection space associated with the at least one cluster for the node based at least in part on each subsequent time value” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 4, Arashanipalai and Sripada teach the method of claim 2 discussed above. Arashanipalai also teaches: “the kinematic metrics computed for the node are utilized by the cluster kinematics analysis system to predict future cluster assignments within the clustering model for the node” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 5, Arashanipalai and Sripada teach the method of claim 4 discussed above. Arashanipalai also teaches: “predicting future cluster assignments within the clustering model for the node comprises determining, by the cluster kinematics analysis system, that the node is moving towards the at least one cluster in the projection space of the clustering model” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 6, Arashanipalai and Sripada teach the method of claim 4 discussed above. Arashanipalai also teaches: “predicting future cluster assignments within the clustering model for the node comprises determining, by the cluster kinematics analysis system, that the node is moving away from the at least one cluster in the projection space of the clustering model” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 7, Arashanipalai and Sripada teach the method of claim 4 discussed above. Arashanipalai also teaches: wherein “the data samples in the temporal sequence associated with the node comprise data from sensors monitoring a state of an object device corresponding to the node” at [0105].
As per claim 8, Arashanipalai and Sripada teach the method of claim 7 discussed above. Arashanipalai also teaches: “the data samples associated with the node are received by the cluster kinematics analysis system in real-time over a network connecting the object device to the cluster kinematics analysis system, and the future cluster assignment predictions for the node are updated upon receipt of each of the data samples to provide substantially real-time anomaly detection and failure prediction for the object device” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 10, Arashanipalai and Sripada teach the method of claim 1 discussed above. Arashanipalai also teaches: the distances associated with each of the one or more clusters in the clustering model calculated by the cluster kinematics analysis system upon receipt of each data sample associated with the node are stored in a datastore connected to the cluster kinematics analysis system for retrieval and computation of new kinematic metrics for the node upon receipt of subsequent data samples associated with the node” at [0105]-[0110], [0132]-[0136] and Figs. 10-11.
As per claim 16, Arashanipalai teaches a cluster kinematics analysis system comprising:
“a datastore containing a clustering model defining one or more clusters in an N- dimensional projection space, and a processor operably connected to the datastore and configured to, upon receiving a data sample from a temporal sequence of data samples associated with a node, wherein each data sample is associated with a time value; apply feature encoding to the data sample to encode the sample into a vector of N dimensions and project the data sample into the N-dimensional projection space” at [0105]-[0110], [0132]-[0136] and Figs. 10-11;
(Arashanipalai teaches building a cluster pattern for a plurality of time period by placing each data record of the plurality of data records into a corresponding cluster of a particular time period based on the associated timestamp of each data record using a machine learning model. The machine learning model is trained using a training data set to apply labels to the input data. Arashanipalai teaches at Fig. 10 the first data sample C collected between t0 and t1 is projected into a projection space 1004 of the clustering model)
“calculate a distance value-within the N-dimensional projection space associated with each of the one or more clusters in the clustering model for the data sample, store the calculated distances in the datastore associated with the node and the associated time value” at [0134]-[0136] and Fig. 11;
(Arashanipalai teaches calculating the distance between a cluster, such as cluster Ctriangle, to other clusters for each of the time period)
Arashanipalai does not teach “calculate one or kinematic metrics associated with at least one cluster of the one or more clusters in the clustering model for the node from the calculated distance values, the associated time value, and previously calculated distance values -distances and kinematic metrics for the node retrieved from the datastore, the one or more kinematic metrics representing a trajectory of the node in relation to the at least one cluster in the N- dimensional projection space of the clustering model” as claimed. However, Sripada teaches at [0072]-[0076] and Fig.8 a similar method for clustering time series data into a plurality of clusters, calculating distances between the clusters. Sripada teaches at [0076] that the speed and direction (i.e., “trajectory”) of the cluster from the first frame to the second frame is computed using the point locations determined at action 808. The speed of the cluster may be determined by comparing a time value associated with the first frame to a time value associated with the second frame, and computing the distance between the two points, such that the rate equals the distance divided by the change in time”. Thus, it would have been obvious to one of ordinary skill in the art to combine Sripada with Arashanipalai’s teaching to calculate the trajectory of the cluster based on distance and time, as suggested by Sripada.
Claims 11-15, 17-19 recite similar limitations as in claims 1-8, 10 above, and are therefore rejected by the same reasons.
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Arashanipalai and Sripada as applied to claims 1-8, 10-19 above, and further in view of Segal et al. (US 2019/0180527 A1), hereinafter “Segal”.
As per claim 9, Arashanipalai and Sripada teach the method of claim 1 discussed above. Arashanipalai does not explicitly teach: “calculating a distance associated with each of the one or more clusters in the clustering model for a data sample comprises calculating a Euclidean distance between the N-dimensional vector comprising the data sample projected into the projection space and an N-dimensional center defined for each of the one or more clusters in the projection space by the clustering model”. However, Segal teaches a similar method for calculating distance between kinematic clusters of a dataset using the well-known Euclidean distance at [0069]. Thus, it would have been obvious to one of ordinary skill in the art to combine Segal with Arashanipalai’s teaching by calculating the distance between the clusters using the well-known Euclidean distance, as suggested by Segal.
Claim 20 recites similar limitations as in claim 9 and are therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
January 28, 2026