DETAILED ACTION
This action is responsive to the following communications: Original Application filed on August 24, 2022. All references to this application refer to the U.S. Patent Application Publication No. 2024/0037455 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-14 are pending in this case. Claims 1 and 11-13 are the independent claims. Claims 1-14 are rejected.
Priority
Applicants claim the benefit of Netherlands Patent Application No. NL 2032650, filed on August 1, 2022. However, as indicated in the Priority Document Exchange Failure Report, an attempt was made to retrieve the certified priority documents on January 1, 2024, but was unsuccessful. Accordingly, the priority has not been perfected because a certified copy of the Netherlands Patent Application No. NL 2032650, as required by 37 CFR 1.55, has not been entered into the file wrapper.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0047, the acronym “MTSL” first appears. This should be spelled out (e.g., “Multi-Task Structural Learning (MTSL) uses Centered Kernal Alignment (CKA) [21] to align neurons based on representation similarity.”)
In paragraph 0078, reference 1 is indicated as published by “anonymous.” However, the paper was published by Kaitlin Maile, Herve Luga, and Dennis G. Wilson. Paragraph 0078 should be updated with the authors names (or “Maile et al.” if preferred).
Appropriate corrections are required.
The use of the trademarks has been noted in this application. The term should be accompanied by the generic terminology, if appropriate; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The following were not properly marked:
Paragraph 0077 (JAVA, PYTHON, LINUX)
Appropriate corrections are 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.
Claim 11 is rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Independent claim 11 recites “a computer-readable storage.” During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Under the broadest reasonable interpretation, “computer-readable storage” encompasses transitory propagating signals, which are non-statutory subject matter. In re Nuijten, 500 F.3d 1346, 1356-57, 84 U.S.P.Q.2d 1495, 1502 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). See also Subject Matter Eligibility of Computer Readable Media, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). Thus, the broadest reasonable interpretation of “computer-readable storage” encompasses nonstatutory subject matter that is unpatentable under 35 U.S.C. 101.
Accordingly, independent claim 11 fails to recite statutory subject matter under 35 U.S.C. 101.
To expedite a complete examination of the instant application, the claims rejected above under 35 U.S.C. 101, as relating to non-statutory subject matter, are further rejected as set forth below in anticipation of amendments to these claims to place them within the four statutory categories of invention.
Claims 1-14 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “initially processing each task in a single network comprising a plurality of layers wherein the first layer comprises a task node, the last layer comprises a task prediction head, and the remaining consecutive layers specific to a task comprise a task branch” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “initially connecting all task nodes to an input image” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “initially connecting all task nodes to an input image” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “training all networks to minimize a multi-task loss” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “training all the task nodes to maximize similarity among the task nodes by aligning their learned concepts” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “locally increasing a similarity in said learned concepts by gauging a similarity between features of said task nodes, representing the local activity of a task, using a similarity metric” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “using a weighted sum of all individual task losses for representing the multi-task loss” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “using a regularization term included with a balancing factor and a negative sign for maximizing alignment between task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “minimizing the task branch, only on the corresponding task loss independently of other tasks” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “calculating similarity between all pairs of task node features” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “listing all possible groups of task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a set of groups that maximizes the total similarity” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “using the groups that satisfy a minimum required similarity for creating a group node” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “obtaining weights of the group node by averaging parameters of the concerned task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “distilling the knowledge learned by multiple task nodes into the group node using an attention- based feature amalgamation method” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “labeling the task nodes used for creating the group node as redundant task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “disconnecting the task branches connected to the redundant task nodes and removing the redundant task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “connecting the disconnected task branches to the corresponding group node” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “assigning the first layer of the task branches as new task nodes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the method comprises a fine-tuning phase wherein the network is only trained with the multi-task loss while skipping the step of aligning concepts learned by task nodes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the method comprises a fine-tuning phase wherein the network is only trained with the multi-task loss while skipping the step of aligning concepts learned by task nodes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the method comprises the step of alternating between the task learning phase and the structural learning phase for a plurality of times before starting the final fine-tuning phase” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the method comprises the step of alternating between the task learning phase and the structural learning phase for a plurality of times before starting the final fine-tuning phase” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture (computer-readable storage).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “initially processing each task in a single network comprising a plurality of layers wherein the first layer comprises a task node, the last layer comprises a task prediction head, and the remaining consecutive layers specific to a task comprise a task branch (from the method of claim 1)” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “computer-readable storage provided with a computer program wherein when said computer program is loaded and executed by a computer, said computer program causes the computer to carry out the steps of” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “computer-readable storage provided with a computer program wherein when said computer program is loaded and executed by a computer, said computer program causes the computer to carry out the steps of” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “initially processing each task in a single network comprising a plurality of layers wherein the first layer comprises a task node, the last layer comprises a task prediction head, and the remaining consecutive layers specific to a task comprise a task branch (from the method of claim 1)” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “autonomous driving awareness method incorporating the computer implemented method of claim 1” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “autonomous driving awareness method incorporating the computer implemented method of claim 1” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
As to claim 13:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine (autonomous vehicle).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “initially processing each task in a single network comprising a plurality of layers wherein the first layer comprises a task node, the last layer comprises a task prediction head, and the remaining consecutive layers specific to a task comprise a task branch (from the method of claim 1)” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “An autonomous vehicle comprising a computer loaded with a computer program wherein said program is arranged for causing the computer to carry out the steps of the computer-implemented method according to claim 1” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “initially connecting all task nodes to an input image (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity (from the method of claim 1)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “An autonomous vehicle comprising a computer loaded with a computer program wherein said program is arranged for causing the computer to carry out the steps of the computer-implemented method according to claim 1” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
As to claim 14:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process (computer-implemented method).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the similarity metric is a Centered Kernel Alignment” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
To expedite a complete examination of the instant application, the claims rejected above under 35 U.S.C. 101, as relating to a judicial exception without significantly more, are further rejected as set forth below in anticipation of amendments to these claims to place them within the four statutory categories of invention.
Examiner’s Note
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature Reference entitled “Branched Multi-Task Networks: Deciding What Layers to Share,” by Vandenhende et al., published on August 13, 2020 (hereinafter Vandenhende).
With respect to independent claim 1, Vandenhende teaches a computer-implemented method for learning of a plurality of tasks in artificial neural networks, wherein the method comprises the steps of:
Initially processing each task in a single network comprising a plurality of layers wherein the first layer comprises a task node, the last layer comprises a task prediction head, and the remaining consecutive layers specific to a task comprise a task branch; Vandenhende teaches initially processing each task in a single network comprising a plurality of layers (see Vandenhende, Figs. 1-2; see also, Vandenhende, section 3.1 [as a first step, train single task models for each task, where the model comprises a plurality of layers]).
Initially connecting all task nodes to an input image; Vandenhende teaches connecting all task nodes to an input image (see Fig. 1).
Wherein the method comprises a task learning phase and a structural learning phase, wherein the structural learning phase comprises the steps of creating and removing neurons based on local task similarity; Vandenhende teaches a task learning phase and a structural learning phase, with the structural phase comprising creation and removal of neurons (e.g., nodes) based on local task similarity (see Vandenhende, section 3.2 [constructing a branched multi-task network using tree structures]; see also, Vandenhende, section 3.1, described supra).
With respect to dependent claim 2, Vandenhende teaches the computer-implemented method of claim 1, as described above.
Vandenhende further teaches the method wherein the task learning phase comprises the steps of:
Training all networks to minimize a multi-task loss; Vandenhende further teaches training all networks to minimize multi-task loss (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Training all the task nodes to maximize similarity among the task nodes by aligning their learned concepts; Vandenhende further teaches maximizing similarity using affinity scores (see Vandenhende, section 3.1, described supra, claim 1).
With respect to dependent claim 3, Vandenhende teaches the computer-implemented method of claim 2, as described above.
Vandenhende further teaches the method wherein the step of maximizing similarity among task nodes by aligning learned concepts of the task nodes comprises the step of locally increasing a similarity in said learned concepts by gauging a similarity between features of said task nodes, representing the local activity of a task, using a similarity metric.
Vandenhende further teaches maximizing similarity using affinity scores (e.g., similarity metric) (see Vandenhende, section 3.1, described supra, claim 1).
With respect to dependent claim 4, Vandenhende teaches the computer-implemented method of claim 1, as described above.
Vandenhende further teaches the method wherein the task learning phase comprises the steps of:
Using a weighted sum of all individual task losses for representing the multi-task loss; Vandenhende further teaches using a weighted sum of individual task losses to represent the multi-task loss (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Using a regularization term included with a balancing factor and a negative sign for maximizing alignment between task nodes; Vandenhende further teaches using a regularization term with a balancing factor and a negative sign (e.g., dissimilarity) for maximizing alignment (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
With respect to dependent claim 5, Vandenhende teaches the computer-implemented method of claim 2, as described above.
Vandenhende further teaches the method wherein the step of training all networks to minimize a multi-task loss comprises the step of minimizing the task branch, only on the corresponding task loss independently of other tasks.
Vandenhende further teaches training each single task model independently by minimizing each task branch (see Vandenhende, Figs. 1-2; see also, Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
With respect to dependent claim 6, Vandenhende teaches the computer-implemented method of claim 1, as described above.
Vandenhende further teaches the method wherein the step of creating neurons comprises the steps of:
Calculating similarity between all pairs of task node features; Vandenhende further teaches calculating similarity between all pairs of task node features (see Vandenhende, section 3 [method aims to find effective task grouping for sharable layers by grouping related tasks together in the same branches of the tree]).
Listing all possible groups of task nodes; Vandenhende further teaches listing all possible groups of task nodes (e.g., all trees) (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Selecting a set of groups that maximizes the total similarity; Vandenhende further teaches getting max matched groups (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Using the groups that satisfy a minimum required similarity for creating a group node; Vandenhende further teaches the similarity score must exceed a threshold for matching (see Vandenhende, section 3.1, described supra, claim 1).
With respect to dependent claim 8, Vandenhende teaches the computer-implemented method of claim 6, as described above.
Vandenhende further wherein the step of removing neurons comprises the steps of:
Labeling the task nodes used for creating the group node as redundant task nodes; Vandenhende further teaches labeling grouped task nodes as redundant (see Vandenhende, section 3.2, described supra, claim 1).
Disconnecting the task branches connected to the redundant task nodes and removing the redundant task nodes; Vandenhende further teaches disconnecting redundant task branches (see Vandenhende, section 3.2, described supra, claim 1).
Connecting the disconnected task branches to the corresponding group node; Vandenhende further teaches connecting (e.g., grouping) similar task branches (see Vandenhende, section 3.2, described supra, claim 1).
Assigning the first layer of the task branches as new task nodes; Vandenhende further teaches assigning the first layer as new task nodes (see Vandenhende, Figs. 1-2; see also, Vandenhende, section 3.1, described supra, claim 1).
Independent claim 11 recites a computer-readable storage provided with a computer program wherein when said computer program is loaded and executed by a computer, said computer program causes the computer to carry out the steps of the computer-implemented method of independent claim 1. Accordingly, independent claim 11, is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
Independent claim 12 recites an autonomous driving awareness method incorporating the computer implemented method of independent claim 1. Accordingly, independent claim 12, is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
Claims 7 is rejected under 35 U.S.C. 103 as being unpatentable over Vandenhende, in view of Non-Patent Literature Reference entitled “PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing,” by Xu et al., published on May 11, 2018 (hereinafter Xu).
With respect to dependent claim 7, Vandenhende teaches the computer-implemented method of claim 6, as described above.
Vandenhende further teaches the method wherein the step of creating neurons comprises the step of using knowledge learned in the task nodes for initializing the created group node using a two-step process:
Obtaining weights of the group node by averaging parameters of the concerned task nodes; Vandenhende further teaches obtaining weights of the group node by averaging parameters of concerned task nodes (see Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Vandenhende fails to further teach distilling the knowledge learned by multiple task nodes into the group node using an attention-based feature amalgamation method.
However, Xu teaches using attention-based feature amalgamation methods (see Xu, section 3.4 [deep multi-modal distillation module fuses information from intermediate predictions for each specific task, where the distillation module is attention based]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Vandenhende and Xu before him before the effective filing date of the claimed invention, to modify the method of Vandenhende to incorporate attention-based distillation as taught by Xu. One would have been motivated to make such a combination because attention has been successfully applied to multiple machine learning and NN model training, as taught by Xu (see Xu, section 3.4, described supra).
Claims 9, 10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Vandenhende, in view of U.S. Patent Application Publication No. 2020/0103909 A1, filed by Feinson et al., on September 26, 2019, and published on April 2, 2020 (hereinafter Feinson).
With respect to dependent claim 9, Vandenhende teaches the computer-implemented method of claim 1, as described above.
Vandenhende fails to further teach the method wherein the method comprises a fine-tuning phase wherein the network is only trained with the multi-task loss while skipping the step of aligning concepts learned by task nodes.
However, Feinson teaches fine-tuning trained machine learning models for use in autonomous driving vehicles (see Feinson, paragraph 0049 [fine-tuning a trained model for using in identifying objects during autonomous driving])
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Vandenhende and Feinson before him before the effective filing date of the claimed invention, to modify the method of Vandenhende to incorporate fine-tuning a trained model as taught by Feinson. One would have been motivated to make such a combination because efficient fine-tuning of machine learning models is a well-known technique with multiple applications, such as with autonomous driving, as taught by Feinson (see Feinson, paragraph 0003 [“Therefore, in order for autonomous and semi-autonomous vehicles to maintain near perfect safety, they must often account for these vehicles and obstacles as well.”]).
With respect to dependent claim 10, Vandenhende, as modified by Feinson, teaches the computer-implemented method of claim 9, as described above.
Vandenhende further teaches the method wherein the method comprises the step of alternating between the task learning phase and the structural learning phase for a plurality of times before starting the final fine-tuning phase.
Vandenhende further teaches alternating between learning phases while building the branching multi-task network (see Vandenhende, section 3.2, described supra, claim 1).
With respect to independent claim 13, Vandenhende discloses … comprising a computer loaded with a computer program wherein said program is arranged for causing the computer to carry out the steps of the computer-implemented method according to claim 1. See Vandenhende, Figs. 1-2; see also, Vandenhende, sections 3.1 and 3.2, described supra, claim 1).
Vandenhende fails to further teach an autonomous vehicle.
However, Feinson teaches autonomous vehicles (see Feinson, Fig. 1; see also, Feinson, paragraphs 0023-0029 [describing the autonomous vehicle of Fig. 1]; see also, Feinson, paragraph 0049, described supra, claim 9).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Vandenhende and Feinson before him before the effective filing date of the claimed invention, to modify the method of Vandenhende to incorporate a trained model into an autonomous vehicle as taught by Feinson. One would have been motivated to make such a combination because trained machine learning models are well known as being used as the vision systems of autonomous vehicles to identify objects, obstructions, and hazards, as taught by Feinson (see Feinson, paragraph 0003, described supra, claim 9).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Vandenhende, in view of Non-Patent Literature Reference entitled “Universal Representation Learning from Multiple Domains for Few-Show Classification,” by Li et al., published on March 25, 2021 (hereinafter Li).
With respect to dependent claim 14, Vandenhende teaches the computer-implemented method of claim 3, as described above.
Vandenhende fails to further teach the method wherein the similarity metric is a Centered Kernel Alignment.
However, Li teaches using CKA as a similarity metric for training multi-domain (e.g., multi-task) networks (see Li, section 3.2 [the method adopts the CKA similarity index for determining similarity between domains]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Vandenhende and Li before him before the effective filing date of the claimed invention, to modify the method of Vandenhende to incorporate using CKA as a similarity metric as taught by Li. One would have been motivated to make such a combination because CKA is a well-known similarity metric used in training neural network models, as taught by Li (see Li, section 3.2, described supra).
Conclusion
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicants are encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KIEU D. VU can be reached on (571) 272-4057. The fax phone number for the organization where this application or