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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-13 recite “the AI system including a neural network having an autoencoder architecture adapted for dynamic transformation of time series input data from an input state space indicative of the state of the device into an output state space indicative of a state trajectory of the device, comprising: at least one processor; and a memory having instructions stored thereon that cause the at least one processor to execute the neural network, train the neural network, or both, the autoencoder architecture comprising”. It is unclear whether the limitations of “execute the neural network, train the neural network, or both” limits the structure of the claim such that they be treated as a claim limitation as the “processor” is merely part of the “AI system” itself but the “AI system” comprises the “autoencoder architecture” which performs the functionality of the claim. See MPEP § 2111.02.
Allowable Subject Matter
Claims 1-20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 1-13 recite an AI system including a neural network having an autoencoder architecture adapted for dynamic transformation of time series input data from an input state space indicative of the state of the device into an output state space indicative of a state trajectory of the device, comprising: at least one processor; and a memory having instructions stored thereon that cause the at least one processor to execute the neural network, train the neural network, or both, the autoencoder architecture comprising: multiple neural ordinary differential equation (ODE) subnetworks, each of the neural ODE subnetworks includes a neural ODE implemented as a recurrent neural network (RNN) architecture transforming unsynchronized time-series input data into time-series latent representations synchronized in time with the time-series latent representations produced by others of the multiple neural ODE subnetworks; a post ODE fusion module configured to fuse the synchronized time-series latent representations of the multiple ODE-RNN subnetworks; and a decoder configured to decode changes in the state of the device from the fused synchronized time-series latent representations to form the state trajectory of the device.
Claims 14-20 similarly recite a method for tracking a state of a device with continuous-time latent dynamics, the method utilizing an artificial intelligence (AI) system including a neural network having an autoencoder architecture adapted for dynamic transformation of time series input data from an input state space indicative of the state of the device into an output state space indicative of a state trajectory of the device; and a non-transitory computer readable storage medium embodied thereon a program executable by a processor for performing a method for tracking a state of a device with continuous-time latent dynamics, the method utilizing an artificial intelligence (AI) system including a neural network having an autoencoder architecture adapted for dynamic transformation of time series input data from an input state space indicative of the state of the device into an output state space indicative of a state trajectory of the device that comprise the steps/functionality of transforming by each subnetwork of a plurality of neural ordinary differential equation (ODE) subnetworks, unsynchronized time-series input data into time-series latent representations synchronized in time with the time-series latent representations produced by other neural ODE subnetworks; fusing the synchronized time-series latent representations of the plurality of neural ODE subnetworks; and decoding changes in the state of the device from the fused synchronized time-series latent representations to form the state trajectory of the device.
These limitations, after search and consideration, are found to be distinguished from the cited prior art. The closest prior art of record is “StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation” by Yining Shi et al. which taught the use of N-ODE networks in conjunction with temporal sensor data fusion, however, it fails to teach or reasonably suggest the entirety of the claimed invention when considered as a whole.
This indication of allowable subject matter is contingent upon the anticipated resolution of the remaining issues detailed in this action.
In the event that any amendment made to the claims changes the scope of the indicated allowable subject matter, further reconsideration of whether the claims continue to distinguish from the prior art and/or are subject to further rejection under applicable statutes may be deemed necessary.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR § 1.111(b) and MPEP § 707.07(a).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to G. C. Neurauter, Jr. whose telephone number is (571)272-3918. The examiner can normally be reached Monday-Friday 9am-5pm Eastern Time.
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/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459
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