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 .
This office action is in response to amendment filed 9/8/2025. Claims 1, 3-10, 12-16 and 18 are pending. Priority date: 2/17/2021
Prosecution is hereby re-opened. The USC 101 rejection on claim eligibility is set forth below.
Response to Argument
Applicant's arguments filed 9/8 have been fully considered but they are not persuasive.
USC 103 rejection: withdrawn
USC 101 statutory rejection: withdrawn
USC 101 claim eligibility rejection
Regarding the amendment “determining a plurality of edge embeddings based on the set of node embeddings and based on the coordinate embeddings associated with the set of node embeddings; determining, via a velocity operation, a weighting factor associated with a velocity embedding of the previous layer based on the associated node embedding of the previous layer; determining a velocity embedding of the at least one hidden layer based on the weighting factor and based on a velocity embedding of the previous layer; and determining a coordinate embedding of the at least one hidden layer based on the determined velocity embedding and based on a coordinate embedding of the previous layer” overcomes the claim eligibility rejection in amended claim 1 (similarly, claims 16 and 18)
In response. The amendment appears to fall under mental processing. Each embedding can be merely represented as a vector of numbers. In the determining steps, each determination of an embedding, as broadly recited, may only involve vector operations that can be carried out by human with pencil and paper.
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, 3-10, 12-14, 16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1. The claim recites a process.
Step 2a, prong 1. The claim recites an abstract idea. The determining step, “an output tensor of at least one hidden layer of the trained graph neural network is determined, at least partly, based on a set of node embeddings of a previous layer and based on coordinate embeddings associated with the node embeddings of the previous layer” and “determining a plurality of edge embeddings based on the set of node embeddings and based on the coordinate embeddings associated with the set of node embeddings; determining, via a velocity operation, a weighting factor associated with a velocity embedding of the previous layer based on the associated node embedding of the previous layer; determining a velocity embedding of the at least one hidden layer based on the weighting factor and based on a velocity embedding of the previous layer; and determining a coordinate embedding of the at least one hidden layer based on the determined velocity embedding and based on a coordinate embedding of the previous layer” fall under mental processing.
As broadly recited, determining an output tensor amounts to comparing and evaluating values that can be carried out by human with pen and paper. An embedding can be represented as a vector of numbers. In the determining steps, each determination of an embedding, as broadly recited, may only involve vector operations. Such determination can be performed by human in mind.
Step 2a, prong 2. This judicial exception is not integrated into a practical application. The additional elements include “receiving an input graph that includes nodes and associated multi-dimensional coordinates”, “propagating the input graph through a trained graph neural network”, “the input graph being provided as input to an input section of the trained graph neural network”, “hidden layer of the trained graph neural network” and “providing an output graph in an output section of the trained graph neural network”. The receiving inputs, propagating the input graph, providing inputs and providing outputs are mere data collection or transmission at a high level of generality and thus are insignificant extra solution activity. The graph neural network, the trained graph neural network, the input section and the output section are described at a high level such that these terms amount to using a computer with a generic neural network to apply the abstract idea.
Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The functions, receiving, propagating and providing input and providing output, amount to receiving or transmitting data over a network, which is well understood, routine, conventional activity and thus, remain insignificant extra-solution activity. The trained graph neural network is merely used as a tool to perform the abstract idea. Applying operations over the hidden or previous layers of a neural network is well understood or WURC activity (e.g., Scarselli). Even when considered in combination, the additional elements represent mere instructions to apply an exception, data receiving and transmission and insignificant extra-solution activity, which cannot provide an inventive concept. Claim 1 is not eligible.
Claim 16. The claim is directed to an apparatus to perform the process of claim 1. The above discussion is incorporated herein. The additional element a hardware processor merely used as a tool to perform the judicial exception. Claim 16 is not eligible.
Claim 18. The claim is directed to method. The discussion of claim 1 is incorporated herein. The additional element providing an apparatus and using the device are at best equivalent of merely adding the word “apply it” to the judicial exception. The additional elements the receiving device, the device and the propagator are merely used as a tool to perform the judicial exception. Claim 18 is not eligible.
Dependent claims 3-10, 12-14 recite further claim limitations in claim 3, determining at least one metric indicative of a relationship between the coordinate embeddings associated of the set of node embeddings; wherein the determining of the respective one of the plurality of edge embeddings is based on the at least one metric (mental processing), claim 4, the metric is a scalar value indicative of a squared relative distance between the coordinate embeddings of the set (step 2a, prong 2/ step 2b, field of use), claim 5, aggregating the plurality of edge embeddings to an aggregated edge embedding (mental processing); and determining at least a part of the output tensor of the hidden layer based on the aggregated edge embedding and based on the associated node embedding of the previous layer (mental processing), claim 6, determining at least one weighting factor based on each respective edge embedding; wherein the aggregating of the plurality of edge embeddings to the aggregated edge embedding is based on the weighting factors (mental processing), claim 7, determining at least one weighting factor based on outputs of a sigmoid function, which has each respective edge embedding as an input (mental processing); wherein the aggregating of the plurality of edge embeddings to the aggregated edge embedding is based on the weighting factors (mental processing), claim 8, determining a coordinate embedding of at least one node of the at least one hidden layer based on the set of node embeddings of the previous layer and based on the edge embedding associated with the set of node embeddings (mental processing), claim 9, the determining of the coordinate embedding is based on a distance between the set of coordinate embeddings of the previous layer, the distance being weighted by a weight of a coordinate operation that depends on the edge embedding associated with the set of coordinate embeddings of the previous layer (mental processing), claim 10, the coordinate embeddings remain constant throughout the propagating (step 2a, prong 2 / step 2b field of use), claim 12, an encoder section of an autoencoder includes the trained graph neural network (the use of an autoencoder is a WURC activity, e.g., reference Do), in claim 13, determining the input graph based on received sensor data representing at least sensor measurement from at least one sensor associated with a state of a physical system (determining based on measurements falls under mental processing and obtaining measurement from sensor associated with a state of a physical system is WURC, e.g., Casas), claim 14, the received sensor data include digital images, and wherein the physical system is a robot or a vehicle or a domestic appliance or a power tool or a manufacturing machine or a personal assistant or an access control system (filed of use). Therefore, claims 3-10, 12-14 are not eligible.
Claim Objections
(1) Claims 1, 16 and 18 are objected to. The claims may be amended to clarify the meaning of the term “velocity”.
(2) Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure, e.g., reference Tran et al. teaches supply chain management based on anomaly detection using neural networks and refence Chen et al. teaches resource capacity management for multiple users with multiple host controllers. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LiWu Chang whose telephone number is (571)270-3809 and email: li-wu.chang@uspto.gov. The examiner can normally be reached on M-F. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LI WU CHANG/ Primary Examiner, Art Unit 2124 December 12, 2025