DETAILED ACTION
This action is written in response to the RCE filed 3/3/26. 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 Arguments
The Examiner is persuaded by the Applicant’s argument pertaining to §112(a) enablement as well as §102 anticipation. In view of these arguments—as well as the Applicant’s amendments to the claims—these rejections are withdrawn. All pending claims are allowable over the prior art. Additional arguments are addressed below.
§101 – The Applicant argues that “Claims 1-21 (now claims 1-7, 9, and 11-21) stand rejected under 35 U.S.C. § 101 because the claimed invention is allegedly directed to a mental process.”
The Examiner identifies no mental process in claim 1. Instead, the recited ‘mapping’ step of claim 1 is identified as a mathematical calculation. See §101 rejection infra.
§101 – The Applicant argues that “the practical application of claim 1 is directed toward defining and using an improved embedding space, enabling neural networks to form more accurate, deeper connections in various tasks, such as for classifying objects.”
The Examiner is not persuaded. Performing an “inferencing task” is too vague to constitute a practical application. Many practical applications do involve inferencing tasks—eg an autonomous vehicle can infer the presence of a pedestrian in the road using camera data, and subsequently apply the brakes. However, no such practical application is recited in claim 1. Dependent claim 6 recites several wildly different and unrelated applications (“classification, image generation, motion prediction, or animation”). The variety and diversity of these applications seems to support the Examiner’s finding that claim 1 is not directed to solving any particular real-world problem. The same is true of dependent claims 16 and 21.
For the foregoing reasons, the examiner maintains the outstanding rejections under §101.
Claim Rejections - 35 USC § 101
Claims 1-7, 9 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention lacks a specific utility.
A "specific utility" is specific to the subject matter claimed and can "provide a well-defined and particular benefit to the public." In re Fisher, 421 F.3d 1365, 1371, 76 USPQ2d 1225, 1230 (Fed. Cir. 2005). This contrasts with a general utility that would be applicable to the broad class of the invention. Office personnel should distinguish between situations where an applicant has disclosed a specific use for or application of the invention and situations where the applicant merely indicates that the invention may prove useful without identifying with specificity why it is considered useful.” (MPEP 2107.01(I)(A))
Claim 1 recites “mapping, suing one or more first layers of a neural network, one or more nodes of a graph”. There are no limitations in the claim regarding what the information in the graph might represent. Likewise, the claim recites “performing… an inferencing task using the one or more embeddings”. However, there are no limitations on what task is being performed. No particular real-world problem is addressed by the claim.
Dependent claims 16 and 21 each recite a litany of potential applications.1 The variety and diversity of these potential applications are further evidence of the lack of specific utility for each of independent claims 1/9/17, as well as the lack of specific utility for these dependent claims.
Independent claims 9 and 17 each lack a specific utility for the same reasons as claim 1. Every pending dependent claims inherits this deficiency from their respective parent claim, and does not correct this deficiency by addressing a particular real-world problem.
Additionally, as a separate grounds of rejection, claims 1-7, 9 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In determining whether the claims are subject matter eligible, the Examiner guidance from MPEP § 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claim recites a mathematical calculation, as illustrated in the table below.
Claim limitation
Examiner analysis
1. A method comprising:
mapping, using one or more first layers of a neural network, one or more nodes of a graph to corresponding positions of a non-Riemannian manifold having an indefinite metric tensor and configured as a representation in which each point in the non-Riemannian manifold is equivalent to a respective antipodal point, to generate one or more embeddings; and
Performing a mapping of graph data to a non-Riemannian manifold is a mathematical calculation, because such an operation will always necessarily proceed according to a defined algorithm. In this, it is similar to both examples (iii) and (v) of “mathematical calculations” in MPEP 2106.04(a)(2)(I)(C):“iii. using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential); …
v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979);”
performing, using one or more second layers of the neural network, an inferencing task using one or more embeddings.
Fundamentally, all neural networks perform inferencing tasks, ie they each determine a prediction or conclusion based on a set of input data. Thus, performing inferencing tasks using a neural network is a well-understood/routine/conventional activity within the field of machine learning. See eg Grattarola, p. 5, fig. 1, illustrating an autoencoder neural network model (Grattarola, Daniele, Lorenzo Livi, and Cesare Alippi. "Adversarial autoencoders with constant-curvature latent manifolds." Applied Soft Computing 81 (2019): 105511. arXiv:1812.04314v2. 11 Apr 2019.); Russell, p. 736 et seq., sec. 20.5, giving an overview of neural networks. (S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd Ed., 2003, chapt 18-21, pp. 649-789.)
Because the claim recites a mathematical calculation, it recites an abstract idea.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the claim lacks a practical application because it lacks specific utility, as outlined supra. Incidentally, the claim does not recite even generic computer hardware.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond those discussed above.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 9 and 17, which recite a system and a processor, respectively, as well as to all pending dependent claims. The additional limitations of the dependent claims are addressed briefly below. Taken alone, the additional elements of the dependent claims above do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other particular technology. Their collective functions merely provide conventional computer implementation.
Claim limitation
Examiner analysis
2, 10. The method of claim 1, wherein the non-Riemannian manifold corresponds to a pseudo-Riemannian manifold of constant non-zero curvature.
This is merely additional information about the previously identified mathematical calculation.
3, 11. The method of claim 1, wherein the non-Riemannian manifold corresponds to at least one of a hyperbolic geometry or an elliptical geometry.
This is merely additional information about the previously identified mathematical calculation.
4. The method of claim 1, wherein the graph comprises the one or more nodes and an adjacency matrix.
This is merely additional information about the previously identified mathematical calculation.
5. The method of claim 1, wherein the one or more nodes correspond to one or more feature vectors generated using an encoder neural network to process input data corresponding to the inferencing task.
This is a mere field-of-use limitation, ie specifying that the data being mapped and used in the neural network model pertains to particular information.
6. The method of claim 1, wherein the inferencing task relates to at least one of classification, image generation, motion prediction, or animation.
This is a mere field-of-use limitation, ie limiting the application of the recited methods to one of the listed problems.
7. The method of claim 1, wherein the non-Riemannian manifold is associated with at least one of: one or more temporal constraints, one or more causal constraints, or one or more spatial constraints.
This is merely additional information about the previously identified mathematical calculation.
9. A system, comprising: one or more processing units to:
map, using a neural network, one or more nodes of a graph to one or more corresponding positions on a non-Riemannian manifold representation corresponding to a pseudo-Riemannian manifold of constant non-zero curvature having an indefinite metric tensor configured such that any point in the pseudo-Riemannian manifold is equivalent to a respective antipodal point in the pseudo-Riemannian manifold; and
perform, using the neural network, an inferencing task based at least in part on the one or more corresponding positions on the non-Riemannian manifold representation.
Performing a mapping of graph data to a non-Riemannian manifold is a mathematical calculation, because such an operation will always necessarily proceed according to a defined algorithm. In this, it is similar to both examples (iii) and (v) of “mathematical calculations” in MPEP 2106.04(a)(2)(I)(C).
Fundamentally, all neural networks perform inferencing tasks, ie they each determine a prediction or conclusion based on a set of input data. Thus, performing inferencing tasks using a neural network is a well-understood/routine/conventional activity within the field of machine learning. See citations to Grattarola and Russell, supra.
16. The system of claim 9, wherein the system comprises at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
This is a mere field-of-use limitation, ie limiting the application of the recited methods to one of the listed problems or problem solving areas.
17. A processor comprising: one or more processing units to:
embed, using a neural network, one or more nodes of a hierarchical graph with cycles of one or more datasets into a non-parametric embedding space to generate one or more ultrahyperbolic embeddings, the non-parametric embedding space corresponding to a pseudo-Riemannian manifold including at least one pair of points that cannot be joined by an unbroken geodesic;
compute, using the neural network and based at least in part on the one or more ultrahyperbolic embeddings, one or more outputs; and
perform one or more operations based at least in part on the one or more outputs.
Performing a mapping (ie an embedding) of hierarchical graph data to a particular manifold is a mathematical calculation, because such an operation will always necessarily proceed according to a defined algorithm. In this, it is similar to both examples (iii) and (v) of “mathematical calculations” in MPEP 2106.04(a)(2)(I)(C).
Fundamentally, all neural networks perform computing tasks, ie they each determine a prediction or conclusion based on a set of input data. Thus, performing inferencing tasks using a neural network is a well-understood/routine/conventional activity within the field of machine learning. See citations to Grattarola and Russell, supra.
This is insignificant (unspecified) post-solution activity.
22. The method of claim 1, wherein a negative of a horizontal lift of a parallel translate of one or more gradients is not a descent direction.
This is merely additional information about the previously identified mathematical calculation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vincent Gonzales whose telephone number is (571) 270-3837. The examiner can normally be reached on Monday-Friday 7 a.m. to 4 p.m. MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092.
Information regarding the status of an application may be obtained from the USPTO Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov.
/Vincent Gonzales/Primary Examiner, Art Unit 2124
1 For example, claim 16: The system of claim 9, wherein the system comprises at least one of:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Prosecution Insights