DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 11/19/2023. Claims 1-20 are pending in this Office 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 .
Claim Objections
Claims 8 and 19 are objected to because of the following informalities: claim 8 recites “the predetermined policy head and the received variables”. There is insufficient antecedent basis for this limitation because predetermined policy and received variables have not been previously mentioned in the claim. It appears applicant intends to have claim 8 depending on claim 7, NOT claim 6. Claim 19 also has similar issue. Appropriate correction is required.
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.
2. Claims 1-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 9 and 12 and 20:
Step 1: Statutory Category? Yes. claim 1 and 9 recite a method (i.e., a “process”), claim 12 recites a non-transitory computer readable storage medium (i.e., an article of manufacture) and claim 20 recites a system (i.e., a “machine”) which are statutory categories.
Claim 1:
Step 2A-Prong 1: Judicial Exception Recited? Yes.
As to Claim 1, the limitation “fixing a gauge function by applying an auxiliary loss function on a latent activation for the specified task, during the training of the neural network model to minimize redundancy” is mathematical calculations that fall under mathematical concepts of the abstract idea groupings.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 further recites additional elements of “obtaining an input data and producing a human-interpretable representation of the latent representation of the neural network model, based on the application of the auxiliary loss function on the latent representation” which is simply data gathering step and pre/post solution activity and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 1 recites additional element of “training the neural network model based on the obtained input data” which amounts to mere instructions to apply the exception using generic computer.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering and post solution activity are well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The neural network is at best the equivalent of merely adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible.
Claim 2 recites additional element of “determining a loss quantity defining a non-linearity of a policy head for the specified task; obtaining a second derivative of the determined loss quantity to minimize the non-linearity of the policy head; taking an absolute value of the obtained second derivative; and adding the absolute value to the latent activation during the training” which is mathematical calculations that fall under mathematical concepts of the abstract idea groupings. Claim 2 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 2 is not patent eligible.
Claim 3 recites additional element of “wherein the auxiliary loss function is obtained via informed learning by: receiving a family of related predetermined variables needed for a policy head to conduct the latent activation for the specific task together with the input data, obtaining simultaneously a distance between a combination of the family of related predetermined variables and a latent representation associated with the family of the related predetermined variables” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III).
The additional limitation of “adding the obtained distance to a loss function to train the policy head and the latent representation, and encourage the informed learning” which is mathematical calculations that fall under mathematical concepts of the abstract idea groupings. Even when considered in combination, the additional elements do not provide an inventive concept, claim 3 therefore is ineligible.
Claim 4 recites additional element of “wherein the training of the policy head and the latent representation are performed simultaneously during the training of the neural network” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 4 therefore is ineligible.
Claim 5 recites additional element of “wherein the specified task includes positioning a vehicle at a center of a lane on which the vehicle travels” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 5 therefore is ineligible.
Claim 6 recites additional element of “ extracting a relevant quantity from the input data, the relevant quantity including: a boundary line of a lane on which a vehicle travels, a distance from the vehicle to the boundary line, a curvature of the boundary line, or information allowing for the vehicle to stay at a center of the lane” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 6 therefore is ineligible.
Claim 7 recites additional element of “wherein the auxiliary loss function is determined by: further receiving a predefined policy head with the received family of related predetermined variables” which is mathematical calculations that fall under mathematical concepts of the abstract idea groupings. Claim 2 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 7 is not patent eligible.
Claim 8 recites additional element of “wherein the predetermined policy head and the received variables are shared amongst neurons within the latent representation” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). Even when considered in combination, the additional elements do not provide an inventive concept, claim 8 therefore is ineligible.
Claim 9 does not recite any limitation that is directed to abstract idea. Accordingly, claim 9 is patent eligible.
Claim 10 recites the limitation of “wherein the neural network model is trained by visualizing the latent representation that includes a human-interpretable representation necessary for performing a specified task” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 10 depends on claim 9 which recites additional elements of “obtaining an input data; producing a human-interpretable representation during inference” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The claim 10 depends on claim 9 . Claim 9 further recites the limitation of “applying a neural network model trained based on the latent representation including a human-interpretable data representation necessary for performing a specified task, and based on the obtained input data, wherein the neural network has a gauge function that is fixed” which amounts to mere instructions to apply the exception using generic computer and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 10 therefore is ineligible.
Claim 11 recites the limitation of “wherein the latent representation is compared to a second latent representation during inference, and a measurement of how close a vehicle is to a center of a lane is determined” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 11 depends on claim 9 which recites additional elements of “obtaining an input data; producing a human-interpretable representation during inference” which is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The claim 11 depends on claim 9. Claim 9 further recites the limitation of “applying a neural network model trained based on the latent representation including a human-interpretable data representation necessary for performing a specified task, and based on the obtained input data, wherein the neural network has a gauge function that is fixed” which amounts to mere instructions to apply the exception using generic computer and is at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 11 therefore is ineligible.
Claim 12:
Step 2A-Prong 1: Judicial Exception Recited? Yes.
As to Claim 12, the limitation “fixing a gauge function by applying an auxiliary loss function on a latent activation for the specified task, during the training of the neural network model to minimize redundancy” is mathematical calculations that fall under mathematical concepts of the abstract idea groupings.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 12 further recites additional elements of “obtaining an input data and producing a human-interpretable representation of the latent representation of the neural network model, based on the application of the auxiliary loss function on the latent representation” which is simply data gathering step and pre/post solution activity and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
Claim 12 recites additional element of “training the neural network model based on the obtained input data” which amounts to mere instructions to apply the exception using generic computer. The additional element of “non-transitory computer readable medium” amounts no more than mere instructions to apply the exception using generic computer component.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 12 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, data gathering and post solution activity are well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The neural network and non-transitory computer readable storage medium are at best the equivalent of merely adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 12 therefore is ineligible.
Claims 13-19 recite similar features of claims 2-8 and therefore being rejected for the same rationale as indicates in the above rejection of claims 2-8 respectively.
Claim 20 merely recites a system to perform the method of claim 1 and therefore being rejected for the same rationale as indicates in the above rejection of claim 1. Furthermore, claim 20 recites additional element of “one or more memory and processors” which amounts to no more than mere instructions to apply the exception using generic computer components and at best the equivalent of merely adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 20 therefore is ineligible.
Allowable Subject Matter
Claims 3-4, 7 , 14-15 and 18 are 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. Although these claims are allowable over prior art, all other rejections and/or objections (if any) such as 101/112/claim objection must be overcome before the claims are allowed.
Claim Rejections - 35 USC § 103
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, 5,9-12,16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al., (US Patent Application Publication 2024/0149906 A1, hereinafter “Zhao”) and further in view of Chen et al., (US Patent Application Publication 2020/0302303 A1, hereinafter “Chen”)
As to claim 1, Zhao teaches a method of training a neural network model based on a latent representation including a human-interpretable data representation necessary for performing a specified task (Zhao par [003] teaches predicting the future trajectories of agents is a task required for motion planning ), comprising: obtaining an input data (Zhao par [0059] teaches target coordinates and context feature as input); training the neural network model based on the obtained input data (Zhao par [0059] the system can implement the Eq(1) as the target prediction neural network. The function f(.) and v(.) can be implemented with any appropriate trainable model such as with a multilayer perceptron neural network with target coordinates and context feature as input) ) ;
[fixing a gauge function by applying an auxiliary loss function on a latent activation for the specified task, during the training of the neural network model to minimize redundancy] (Zhao par [0060] teaches loss function for training model is given by equation (2) ; and
producing a human-interpretable representation of the latent representation of the neural network model, based on the application of the [auxiliary ] loss function on the latent representation. (Zhao par [0065] teaches the neural network outputs most likely future trajectory per target location)
Zhao fails to expressly teach fixing a gauge function by applying an auxiliary loss function on a latent activation for the specified task, during the training of the neural network model to minimize redundancy.
However, Chen teaches fixing a gauge function by applying an auxiliary loss function on a latent activation for the specified task, during the training of the neural network model to minimize redundancy. (Chen par [0058] teaches the loss function employed during the training process may be represented with a linear combination of path values effectively, thereby reducing redundancy)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Zhao and Chen to achieve the claimed invention. One would have been motivated to make such combination to reduce redundancy. (Chen par [0058])
As to claim 5, Zhao and Chen teach the method of claim 1 wherein the specified task includes positioning a vehicle at a center of a lane on which the vehicle travels. (Zhao par [0053] teaches for teach identified lane, the system can sample points on a lane center line of the identified lane)
As to claim 9, Zhao teaches a method of visualizing a latent representation of a neural network model, comprising: obtaining an input data; applying a neural network model trained based on the latent representation including a human-interpretable data representation necessary for performing a specified task (Zhao par [0059] the system can implement the Eq(1) as the target prediction neural network. The function f(.) and v(.) can be implemented with any appropriate trainable model such as with a multilayer perceptron neural network with target coordinates and context feature as input), and based on the obtained input data, [wherein the neural network has a gauge function that is fixed] ; producing a human-interpretable representation during inference. (Zhao par [0065] teaches the neural network outputs most likely future trajectory per target location)
Zhao fails to expressly teach wherein the neural network has a gauge function that is fixed.
However, Chen teaches wherein the neural network has a gauge function that is fixed. (Chen par [0058] teaches the loss function employed during the training process may be represented with a linear combination of path values effectively, thereby reducing redundancy.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Zhao and Chen to achieve the claimed invention. One would have been motivated to make such combination to reduce redundancy. (Chen par [0058])
As to claim 10, Zhao and Chen teach the method of claim 9, wherein the neural network model is trained by visualizing the latent representation that includes a human-interpretable representation necessary for performing a specified task. (Zhao par [0010] teaches the planning system of the vehicle can use the likely future trajectories for the agent to make planning decisions to plan a future trajectory of the vehicle)
As to claim 11, Zhao and Chen teach the method of claim 9, wherein the latent representation is compared to a second latent representation during inference, and a measurement of how close a vehicle is to a center of a lane is determined. (Zhao par [0015] teaches when identifying the plurality of initial target locations in the environment, the system obtains road graph data identifying lanes on one or more roads in the environment, and samples, as initial target locations, points from the identified lanes. In some implementations, when sampling points from the identified lanes, for each identified lane, the system samples points on a lane center line of the identified lane)
Claims 12 and 20 merely recite a non-transitory computer readable storage medium and a system to perform the method of claim 1. Accordingly, Zhao and Chen teach every limitation of claims 12 and 20 as indicates in the above rejection of claim 1.
As to claim 16, see the above rejection of claim 5.
Claims 2,6,8, 13,17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao , Chen and further in view of Sorakado et al., (US Patent Application Publication 2024/0144646A1, hereinafter “Sorakado”)
As to claim 2, Zhao and Chen teach the method of claim 1 but fail to teach wherein the auxiliary loss function obtained by: determining a loss quantity defining a non-linearity of a policy head for the specified task; obtaining a second derivative of the determined loss quantity to minimize the non-linearity of the policy head; taking an absolute value of the obtained second derivative; and adding the absolute value to the latent activation during the training.
However, Sorakado teaches determining a loss quantity defining a non-linearity of a policy head for the specified task; obtaining a second derivative of the determined loss quantity to minimize the non-linearity of the policy head; taking an absolute value of the obtained second derivative; and adding the absolute value to the latent activation during the training. (Sorakado par [0048] teaches backward propagation is used. Sorakado par [0072] teaches absolute value of second order derivative)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Zhao and Chen with the teaching of Sorakado to achieve the claimed invention. One would have been motivated to make such combination to improve accuracy for the machine learning model.
As to claim 6, Zhao , Chen and Sorakado teach the method of claim 2, further comprising extracting a relevant quantity from the input data, the relevant quantity including: a boundary line of a lane on which a vehicle travels, a distance from the vehicle to the boundary line, a curvature of the boundary line, or information allowing for the vehicle to stay at a center of the lane.(Zhao par [0045] teaches the scene context data can include road graph data that indicates the positions of lanes, cross-sections, traffic lights, road signs in the environment )
As to claim 8, Zhao, Chen and Sorakado teach the method of claim 6, wherein the predetermined policy head and the received variables are shared amongst neurons within the latent representation. (Zhao par [006] teaches each neuron receives one or more inputs and generates an output that is received by another neural network layer. Often, each neuron receives inputs from other neurons, and each neuron provides an output to one or more other neurons)
As to claims 13,17 and 19, see the above rejection of claims 2,6 and 8 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Niu et al.US PGPub. 2025/0363373, par [0052] teaches loss function is configured to reduce redundancy in a non-linear way. Humphreys et al., US PGPub 2025/0335439 A1, par [0108]-[0109] teaches providing the latent representation into the policy neural network and generate output value.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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