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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/13/2024, 11/29/2024, 05/25/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,094,192 B2.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,100,414 B2.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-29 of U.S. Patent No. 10,977,566 B2.
Claims 1-18 are allowed if they are overcome the Double Patenting rejection.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,094,192 B2.
For claim 1, although this claim is not identical to Claim 1 of U.S. Patent No. 12,094,192 B2, this claim is not patentably distinct from Claim 1 of U.S. Patent No. 12,094,192 B2 because Claim 1 is broader than and fully encompassed by Claim 1 of U.S. Patent No. 12,094,192 B2.
Application 18/380,639 (US-20240404269 A1)
U.S. Patent No. 12,094,192 B2
A computer-implemented method of learning for inference, the method comprising: processing, by a first input processor of a first inference system, first inputs indicating detection of a first feature from a first sensor associated with the first inference system; selecting, at a first output processor of the first inference system coupled to the first input processor, a set of output elements for activation, the activated set of output elements representing an object; associating a subset of the activated output elements in the first output processor with each other by updating first lateral connections between the subset of the activated output elements; generating, by the first input processor, a first input representation by selecting a set of input elements for activation, the first input representation representing a first set of pairs indicating detection of the first feature of the object at a first location associated with the first feature; and associating a subset of the activated input elements in the first input processor with one or more of the activated output elements in the first output processor by updating feedforward connections.
A computer-implemented method of performing inference using a plurality of multi-layer systems corresponding to a plurality of nodes, the method comprising: generating, by a first inference system of a first multi-layer system of a first node, a first input representation by at least processing first sensory input indicating a first set of candidate pairs, the first set of candidate pairs indicating detection of a first feature of an unidentified object at first candidate locations on the unidentified object associated with the first feature; determining, by the first inference system, a first output representation corresponding to the first input representation by processing the first input representation, the first output representation indicating a first set of candidate objects associated with the first set of candidate pairs; sending an inter-node signal representing the first output representation from the first inference system to a second inference system; generating, by the second inference system of a second multi-layer system of a second node, a second input representation by processing second sensory input indicating a second set of candidate pairs, the second set of candidate pairs indicating detection of a second feature of the unidentified object at second candidate locations on the unidentified object associated with the second feature; and determining, by the second inference system, a second output representation indicating a second set of candidate objects corresponding to the second input representation and consistent with the first output representation based on the inter-node signal from the first inference system.
As for claims 2-18, although this claim is not identical to Claims 2-20 of U.S. Patent No. 12,094,192 B2, this claim is not patentably distinct from Claims 2-20 of U.S. Patent No. 12,094,192 B2 because Claims 2-18 is broader than and fully encompassed by Claims 2-20 of U.S. Patent No. 12,094,192 B2.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,100,414 B2.
For claim 1, although this claim is not identical to Claim 1 of U.S. Patent No. 11,100,414 B2, this claim is not patentably distinct from Claim 1 of U.S. Patent No. 11,100,414 B2 because Claim 1 is broader than and fully encompassed by Claim 1 of U.S. Patent No. 11,100,414 B2.
Application 18/380,639 (US-20240404269 A1)
U.S. Patent No. 11,100,414 B2
A computer-implemented method of learning for inference, the method comprising: processing, by a first input processor of a first inference system, first inputs indicating detection of a first feature from a first sensor associated with the first inference system; selecting, at a first output processor of the first inference system coupled to the first input processor, a set of output elements for activation, the activated set of output elements representing an object; associating a subset of the activated output elements in the first output processor with each other by updating first lateral connections between the subset of the activated output elements; generating, by the first input processor, a first input representation by selecting a set of input elements for activation, the first input representation representing a first set of pairs indicating detection of the first feature of the object at a first location associated with the first feature; and associating a subset of the activated input elements in the first input processor with one or more of the activated output elements in the first output processor by updating feedforward connections.
A computer-implemented method of performing inference using a plurality of inference systems, each inference system including a respective input processor and a respective output processor coupled to the respective input processor, the method comprising:
generating, by a first input processor in a first inference system at a first layer of a multi-layer system, a first input representation by processing first sensory input indicating a first set of candidate pairs, the first set of candidate pairs indicating detection of a first feature of an unidentified object at first candidate locations on the unidentified object associated with the first feature;
determining, by a first output processor in the first inference system, a first output representation corresponding to the first input representation by processing the first input representation, the first output representation indicating a first set of candidate objects associated with the first set of candidate pairs;
generating, by a second input processor in a second inference system at a second layer of the multi-layer system higher than the first layer, a second input representation corresponding to at least the first output representation of the first output processor by processing the first output representation; and
determining, by a second output processor in the second inference system, a second output representation corresponding to the second input representation by processing the second input representation, the second output representation indicating a second set of candidate objects associated with at least the first set of candidate pairs.
As for claims 2-18, although this claim is not identical to Claims 2-20 of U.S. Patent No. 11,100,414 B2, this claim is not patentably distinct from Claims 2-20 of U.S. Patent No. 11,100,414 B2 because Claims 2-18 is broader than and fully encompassed by Claims 2-20 of U.S. Patent No. 11,100,414 B2.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-29 of U.S. Patent No. 10,977,566 B2.
For claim 1, although this claim is not identical to Claim 1 of U.S. Patent No. 10,977,566 B2, this claim is not patentably distinct from Claim 1 of U.S. Patent No. 10,977,566 B2 because Claim 2 is broader than and fully encompassed by Claim 1 of U.S. Patent No. 10,977,566 B2.
Application 17/380,639 (US- 20210374578A1)
U.S. Patent No. 10,977,566 B2
A computer-implemented method of learning for inference, the method comprising: processing, by a first input processor of a first inference system, first inputs indicating detection of a first feature from a first sensor associated with the first inference system; selecting, at a first output processor of the first inference system coupled to the first input processor, a set of output elements for activation, the activated set of output elements representing an object; associating a subset of the activated output elements in the first output processor with each other by updating first lateral connections between the subset of the activated output elements; generating, by the first input processor, a first input representation by selecting a set of input elements for activation, the first input representation representing a first set of pairs indicating detection of the first feature of the object at a first location associated with the first feature; and associating a subset of the activated input elements in the first input processor with one or more of the activated output elements in the first output processor by updating feedforward connections.
A computer-implemented method of performing inference, comprising:
generating, by a first input processor, a first input representation indicating a first set of candidate pairs, the first set of candidate pairs indicating detection of a first feature of an unidentified object at first candidate locations associated with the first feature;
determining, by a first output processor, a first output representation corresponding to the first input representation, the first output representation indicating one or more candidate objects associated with the first set of pairs and including the unidentified object;
generating, by the first input processor, a second input representation indicating a second set of candidate pairs subsequent to generating the first input representation, the second set of candidate pairs indicating detection of a second feature of the unidentified object at second candidate locations associated with the second feature;
determining, by the first output processor, a second output representation corresponding to the first input representation and the second input representation, the second output representation indicating one or more of the candidate objects associated with the first set of pairs and the second set of pairs; and determining one of the candidate objects as an identified object based on the first output representation and the second output representation.
As for claims 2-18, although this claim is not identical to Claims 2-29 of U.S. Patent No. 10,977,566 B2, this claim is not patentably distinct from Claims 2-29 of U.S. Patent No. 10,977,566 B2 because Claims 2-18 is broader than and fully encompassed by Claims 2-29 of U.S. Patent No. 10,977,566 B2.
Allowable Subject Matter
Claims 1-18 are allowed if they are overcome the Double Patenting rejection.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance: The present invention comprises a multi-layer system may correspond to a node that receives a set of sensory input data for hierarchical processing, and may be grouped to perform processing for sensory input data. Inference systems at lower layers of a multi-layer system pass representation of objects to inference systems at higher layers. Each inference system can perform inference and form their own versions of representations of objects, regardless of the level and layer of the inference systems. The set of candidate objects for each inference system is updated to those consistent with feature-location representations for the sensors as well as object representations at lower layers. The set of candidate objects is also updated to those consistent with candidate objects from other inference systems, such as inference systems at other layers of the hierarchy or inference systems included in other multi-layer systems. The closest prior art, Chalasani et al (U.S. 20150324655 A1; Chalasani) shows a similar system which also includes the processing circuitry is configured to process a sequence of images to recognize an object in the images, the recognition of the object based upon a hierarchical model includes determining input data from a plurality of overlapping pixel patches of a video image; determining a plurality of corresponding states based at least in part upon the input data and an over-complete dictionary of filters; and determining a cause based at least in part upon the plurality of corresponding states. The cause may be used to identify an object in the video image.
However, Chalasani fails to disclose “ processing, by a first input processor of a first inference system, first inputs indicating detection of a first feature from a first sensor associated with the first inference system; selecting, at a first output processor of the first inference system coupled to the first input processor, a set of output elements for activation, the activated set of output elements representing an object; associating a subset of the activated output elements in the first output processor with each other by updating first lateral connections between the subset of the activated output elements; generating, by the first input processor, a first input representation by selecting a set of input elements for activation, the first input representation representing a first set of pairs indicating detection of the first feature of the object at a first location associated with the first feature; and associating a subset of the activated input elements in the first input processor with one or more of the activated output elements in the first output processor by updating feedforward connections” These features have been added to independent claim(s) 1 and 10; therefore, rendering it/them allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nevatia et al (U.S. 20110085702 A1), “Object Tracking By Hierarchical Association of Detection Responses”, teaches about systems and methods for providing object tracking utilizing associations that are made in several levels and the affinity measure is refined at each level based on the knowledge obtained at the previous level. It also teaches about (1) a novel three-level hierarchical framework to progressively associate detection responses, in which different methods and models are adopted to improve tracking robustness; (2) a modified transition matrix for the Hungarian algorithm to solve the association problem that considers not only initialization, termination and transition of tracklets but also false alarm hypotheses; and/or (3) a Bayesian inference approach to automatically estimate a scene structure model as the high-level knowledge for the long-range trajectory association.
Chalasani et al (U.S. 20150324655 A1), “Distributive Hierarchical Model For Object Recognition In Video”, teaches about a system includes processing circuitry including a processor. The processing circuitry is configured to process a sequence of images to recognize an object in the images, the recognition of the object based upon a hierarchical model. In another example, a method includes determining input data from a plurality of overlapping pixel patches of a video image; determining a plurality of corresponding states based at least in part upon the input data and an over-complete dictionary of filters; and determining a cause based at least in part upon the plurality of corresponding states. The cause may be used to identify an object in the video image.
Ibarz Gabardos et al (U.S. 20160096270 A1), “Feature Detection Apparatus and Methods for Training of Robotic Navigation”, teaches about adaptive control and training of robotic devices. It also teaches about a method of operating a robotic device by a learning controller causing the robotic device to execute an action along a first trajectory in accordance with a first control signal determined based on a sensory input; determining, a performance measure based on an evaluation of the first trajectory and an indication related to a target trajectory provided by a trainer; conveying information related to the performance measure to the learning controller; and updating one or more learning parameters of the learning controller. Furthermore, the method also causing the robotic device to execute the action along a second trajectory in accordance with a second control signal determined based on the sensory input; wherein: the execution of the action along the second trajectory is characterized by a second performance measure; and the updating is configured to displace the second trajectory closer towards the target trajectory relative to the first trajectory.
Sung J et al, “Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding”, teaches about an algorithm that allows a robot to infer a manipulation trajectory when it is introduced to a new object or appliance and its natural language instruction manual. It also teaches a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista.
Song D et al, “Task-Based Robot Grasp Planning Using Probabilistic Inference”, teaches
about a probabilistic frame work for the representation and modeling of robot-grasping tasks.
The framework consists of Gaussian mixture models for generic data discretization, and discrete
Bayesian networks for encoding the probabilistic relations among various task-relevant
variables, including object and action features as well as task constraints. It also teaches about
the graphical model framework provides insights into dependencies between variables and
features relevant for object grasping.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Duy A Tran whose telephone number is (571)272-4887. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm.
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/DUY TRAN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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