DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application was filed on 12/06/2021. This action is in response to amendments and remarks filed on 12/09/2025. In the current amendments claims 1, 6, 8, 13, 15 and 20 have been amended and no claims were added or canceled. Thus, claims 1-20 are pending and have been examined. Claims 1, 8 and 15 are independent claims.
Response to Amendment
In response to amendments and remarks filed on 12/09/2025, the objections to the specification and drawings, set forth in the previous Office Action, have been withdrawn. Claims 15-20 are no longer being rejected under 35 U.S.C. as being directed to software per se in light of Applicant’s claim amendments and remarks.
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 1-20 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: Claim 1 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
generating, for a plurality of actor agents, a first set of instructions for performing a first set of manufacturing tasks, wherein the actor agents comprise one or more of: an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, or a non-autonomous actor having a third sensor - In the context of the claim limitation, this encompasses a mental process of evaluating tasks for an actor agent based on observed instructions and selected actor agent.
based at least on the first set of instructions and the observation data, generating…a second set of instructions for performing a second set of manufacturing tasks, wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “by the control agent for the plurality of actor agents” – this is a mere instruction to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the first set of manufacturing tasks, wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitation of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2:
Step 1: Claim 2 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
based at least on the second set of instructions and the second observation data, generating, by the control agent for the plurality of actor agents, a third set of instructions for performing a third set of manufacturing tasks - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “receiving, by the control agent from at least the plurality of actor agents, second observation data regarding performance of the actor agents on the second set of manufacturing tasks”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3:
Step 1: Claim 3 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “an encoder portion comprising a plurality of input-specific LSTMs and an attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 4:
Step 1: Claim 4 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of claim 3.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the input-specific LSTMs”, “the decoder LSTM”, “the input-specific LSTMs and attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receive at least a portion of the observation data…receives at least a portion of the observation data”, which recites the insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 5:
Step 1: Claim 5 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
constraining instructions using actor masks - In the context of the claim limitation, this encompasses a mental process of evaluating instructions by observing actor masks.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 6:
Step 1: Claim 6 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “wherein receiving the observation data comprises receiving the observation data from a sensor agent comprising a fourth sensor”, which recites the insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 7:
Step 1: Claim 7 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. This judicial exception is not integrated into a practical application. The claim further recites “training the control agent with synthetic training data” – this is a mere instruction to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 8:
Step 1: Claim 8 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
generating, for a plurality of actor agents, a first set of instructions for performing a first set of manufacturing tasks, wherein the actor agents comprise one or more of: an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, or a non-autonomous actor having a third sensor - In the context of the claim limitation, this encompasses a mental process of evaluating tasks for an actor agent based on observed instructions and selected actor agent.
based at least on the first set of instructions and the observation data, generating…a second set of instructions for performing a second set of manufacturing tasks, wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “one or more processors”; “a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations”, “by the control agent for the plurality of actor agents” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the first set of manufacturing tasks, wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 9:
Step 1: Claim 9 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
based at least on the second set of instructions and the second observation data, generating, by the control agent for the plurality of actor agents, a third set of instructions for performing a third set of manufacturing tasks - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “receiving, by the control agent from at least the plurality of actor agents, second observation data regarding performance of the actor agents on the second set of manufacturing tasks”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 10:
Step 1: Claim 10 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 8.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “an encoder portion comprising a plurality of input-specific LSTMs and an attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 11:
Step 1: Claim 11 is directed to a method of multi-agent instruction generation, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of claim 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the input-specific LSTMs”, “the decoder LSTM”, “the input-specific LSTMs and attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receive at least a portion of the observation data…receives at least a portion of the observation data”, which recites the insignificant extra-solution activities of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 12:
Step 1: Claim 12 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
constraining instructions using actor masks - In the context of the claim limitation, this encompasses a mental process of evaluating instructions by observing actor masks.
Step 2A Prong 2: Please see analysis of the independent claim 8.
Step 2B Analysis: Please see analysis of the independent claim 8.
Claim 13:
Step 1: Claim 13 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 8.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “wherein receiving the observation data comprises receiving the observation data from a sensor agent comprising a fourth sensor”, which recites insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 14:
Step 1: Claim 14 is directed to a system of multi-agent instruction generation, which is directed to a machine, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 8.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. This judicial exception is not integrated into a practical application. The claim further recites “training the control agent with synthetic training data” – this is a mere instruction to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 15:
Step 1: Claim 15 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
generating, for a plurality of actor agents, a first set of instructions for performing a first set of manufacturing tasks, wherein the actor agents comprises one or more of: an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, or a non-autonomous actor having a third sensor - In the context of the claim limitation, this encompasses a mental process of evaluating tasks for an actor agent based on observed instructions and selected actor agent.
based at least on the first set of instructions and the observation data, generating…a second set of instructions for performing a second set of manufacturing tasks, wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “by the control agent for the plurality of actor agents” – this is a mere instruction to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the first set of manufacturing tasks, wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network”, which recites insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 16:
Step 1: Claim 16 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
based at least on the second set of instructions and the second observation data, generating, by the control agent for the plurality of actor agents, a third set of instructions for performing a third set of manufacturing tasks - In the context of the claim limitation, this encompasses a mental process of observing data and instructions to evaluate tasks.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “receiving, by the control agent from at least the plurality of actor agents, second observation data regarding performance of the actor agents on the second set of manufacturing tasks”, which recites insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “receiving…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 17:
Step 1: Claim 17 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “an encoder portion comprising a plurality of input-specific LSTMs and an attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 18:
Step 1: Claim 18 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: Please see analysis of claim 17.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the input-specific LSTMs”, “the decoder LSTM”, “the input-specific LSTMs and attention network”; “a decoder portion comprising a decoder LSTM” – these are mere instructions to apply merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f). The claim recites “receive at least a portion of the observation data…receives at least a portion of the observation data”, which recites insignificant extra-solution activities of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 19:
Step 1: Claim 19 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
constraining instructions using actor masks - In the context of the claim limitation, this encompasses a mental process of evaluating instructions by observing actor mask.
Step 2A Prong 2: Please see analysis of the independent claim 15.
Step 2B Analysis: Please see analysis of the independent claim 15.
Claim 20:
Step 1: Claim 20 is directed to a computer program product, comprising non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim recites “wherein receiving the observation data comprises receiving the observation data from a sensor agent comprising a fourth sensor”, which recites insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “wherein…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
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 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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Grant (US11221897B2) in view of Ramadas (US 20220199073 A1) further in view of Aradi (“Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles”).
Claim 1.
Grant teaches a method of multi-agent instruction generation, the method comprising (Column 1 & line 55-61 “The at least one artificial intelligence algorithm optionally includes at least one clustering algorithm that generates at least one output reflecting failure trends with respect to the plurality of devices. Furthermore, the machine learning knowledge model optionally is a multi-agent reinforcement learning model” and Column 1 & lines 36-43 “The method further includes analyzing, via the machine learning knowledge model, the device sensor data in view of the environmental sensor data to determine a failure risk value for each of the plurality of devices. Responsive to the failure risk value for any device among the plurality of devices exceeding a predefined failure risk threshold, the method further includes facilitating activation of the device for a designated duration of time” and Figure 3 teaches multi-agent generation of instruction because system output reflecting failure value and in response to failure value system facilitating activation of device for a designated duration time):
generating, for a plurality of actor agents, a first set of instructions for performing a first set of manufacturing tasks (Column 6 & lines 28-30 “I/O device interface 111 is communicatively coupled to client I/O device(s) 125 (e.g., touchscreen console, trackpad, joystick, microphone, speaker, etc.). The client(s) may interact with client application interface(s) 121 via client I/O device(s) 125” teaches I/O device communicatively couple to I/O devices which suggests that the system allows for agent interaction with an interface through input and output and Column 2 & lines 3-11 “the step of facilitating activation of the device includes accessing a dataset enumerating at least one maintenance action with respect to the device, selecting one or more actions among the at least one maintenance action in the dataset based upon the failure risk value and the analysis of the device sensor data in view of the environmental sensor data” teaches for client devices (corresponds to actor agents) selecting one or more actions (first set of tasks) among the maintenance action in view of failure value, in response to failure value system facilitating activation of device for a designated duration (corresponds to instructions)),
wherein the actor agents comprise one or more of: a human actor accessing a user interface (UI), an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, and a non-autonomous actor having a third sensor (Column 6 & lines 41-42 “A device or one or more components thereof optionally include autonomous capabilities, i.e., -capable of functioning without user intervention, or semi-autonomous capabilities, i.e., capable of functioning with limited user intervention. A device optionally includes software components and/or one or more digital interfaces to facilitate device operation and/or to facilitate interaction with any entity associated with a client ecosystem or another environment” and Column 6 & lines 28-30 “I/O device interface 111 is communicatively coupled to client I/O device(s) 125 (e.g., touchscreen console, trackpad, joystick, microphone, speaker, etc.). The client(s) may interact with client application interface(s) 121 via client I/O device(s) 125” and Column 2 & lines 8-10 “in view of the environmental sensor data, and facilitating initiation of the selected one or more actions” teaches client device comprising human actor, autonomous capabilities, or semi-autonomous capabilities);
receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the first set of manufacturing tasks (Column 9 & lines 60-63 “In an embodiment, as reflected in the methods described herein, device management application 149 serves as a centralized virtual agent that captures sensor data related to device 205, device 215, device 225, device 235, and/or device 245 in client ecosystem 200” and column 13 & lines 14-19 “the device management application may collect moisture-related data that pertains to both device wear and environmental exposure, in which case the device management application may log or otherwise store the moisture-related data for both device wear analysis and environmental exposure analysis” teaches device management application (control agent) collect sensor data (observation data) regarding performance of the client ecosystem, the moisture related data helps monitor both wear and environmental exposure, so the system can maintain good performance in the manufacturing process).
Grant does not explicitly teach wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network.
However, in the same field, analogous art Ramadas teaches wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network (Para [0075] “Translatotron 804 may pass these hidden states through an attention-based alignment mechanism (i.e., attention units 812) to condition an autoregressive decoder (e.g., spectrogram decoder 816 or decoder 814A, 814B). Encoder stack 808 may be implemented as a stack of bidirectional LSTM layers (e.g., a stack of 8 bidirectional LSTM layers or another number of bidirectional LSTM layers). Concatenation unit 810 may concatenate the output of speaker-encoder 806 with the output of encoder stack 808. Attention is a type of input processing technique for neural networks. Attention enables neural networks (such as neural networks implemented in decoders 814 and spectrogram decoder 816) to focus on a subset of a complex input dataset or features. Attention mechanisms may be helpful in alignment of sequential data such as speech, text, etc.” teaches the autoregressive decoder use output to condition the generation of future outputs (task instructions), the LSTM bidirectional attention network to process sequential data).
Grant and Ramadas are analogous art because they are both directed to using LSTM to make the prediction based on provided input.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ramadas to the disclosed invention of Grant.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, using autoregressive bidirectional LSTM “ improve the prediction of occurrences and/or temporal durations of sensitive-information utterances”, as suggested in Ramadas (Ramadas, Para [0061]).
Grant in view of Ramadas does not explicitly teach based at least on the first set of instructions and the observation data, generating, by the control agent for the plurality of actor agents, a second set of instructions for performing a second set of…tasks, wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization.
However, in the same field, analogous art Aradi teaches based at least on the first set of instructions and the observation data, generating, by the control agent for the plurality of actor agents, a second set of instructions for performing a second set of…tasks (C. Action Space & Page 4-5 “Though two main levels of control can be found: one is the direct control of the vehicle by steering braking and accelerating commands, and the other acts on the behavioral layer and defines choices on strategic levels, such as lane change, lane keeping, setting ACC reference point, etc. At this level, the agent gives a command to low-level controllers, which calculate the actual trajectory” teaches the first control (corresponds to first set of instruction), second control defines choice on strategic levels (corresponds to second set of instruction)),
wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization (C. Action Space & Page 4-5 “Though two main levels of control can be found: one is the direct control of the vehicle by steering braking and accelerating commands, and the other acts on the behavioral layer and defines choices on strategic levels, such as lane change, lane keeping, setting ACC reference point, etc. At this level, the agent gives a command to low-level controllers, which calculate the actual trajectory” teaches the first control (corresponds to first set of instruction), second control defines choice on strategic levels (corresponds to second set of instruction) each includes control commands for the agent and command comprises to vehicle controller (platform control)).
Grant, Ramadas and Aradi are analogous art because they are all directed to using Artificial Intelligence and Machine Learning to analyze data and make decisions in complex systems.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Aradi to the disclosed invention of Grant in view of Ramadas.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, in the autonomous vehicles shows that artificial intelligence and machine learning improve motion, planning, decision making, and control in task such as lane keeping and merging, as suggested in Aradi (Aradi, Abstract, Page 1).
Claim 2.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 1, further comprising:
Grant further teaches receiving, by the control agent from at least the plurality of actor agents, second observation data regarding performance of the actor agents on the second set of manufacturing tasks (Column 9 & lines 60-63 “In an embodiment, as reflected in the methods described herein, device management application 149 serves as a centralized virtual agent that captures sensor data related to device 205, device 215, device 225, device 235, and/or device 245 in client ecosystem 200” and column 13 & lines 14-19 “the device management application may collect moisture-related data that pertains to both device wear and environmental exposure, in which case the device management application may log or otherwise store the moisture-related data for both device wear analysis and environmental exposure analysis” column 15 & lines 15-21 “the device management application iteratively compares each device among the plurality of devices to the predefined failure risk threshold (either a single predefined failure risk threshold defined for all device types or a predefined failure risk threshold defined for the applicable device type)” teaches device management application (control agent) collect sensor data (observation data) regarding performance of the client ecosystem, device management application iteratively performs for each device);
and based at least on the second set of instructions and the second observation data, generating, by the control agent for the plurality of actor agents, a third set of instructions for performing a third set of manufacturing tasks (Column 20 & lines 51-58 “Based upon a negative adjustment to a reward function for a respective device associated with an agent (i.e., a negative maintenance state change for the respective device) and/or based upon a positive adjustment to the reward function for the respective device (i.e., a positive maintenance state change for the respective device), the device management application correspondingly updates a maintenance policy associated with the agent” and column 15 & lines 15-21 “the device management application iteratively compares each device among the plurality of devices to the predefined failure risk threshold (either a single predefined failure risk threshold defined for all device types or a predefined failure risk threshold defined for the applicable device type)” teaches the device management application update a policy associated with the agent corresponding to a third set of instruction, device management application iteratively performs for each device).
Claim 3.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 1,
However, in the same field, analogous art Ramadas further teaches wherein the control agent comprises: an encoder portion comprising a plurality of input-specific LSTMs and an attention network; and a decoder portion comprising a decoder LSTM (Para [0075]-[0076] “Translatotron 804 may pass these hidden states through an attention-based alignment mechanism (i.e., attention units 812) to condition an autoregressive decoder (e.g., spectrogram decoder 816 or decoder 814A, 814B). Encoder stack 808 may be implemented as a stack of bidirectional LSTM layers (e.g., a stack of 8 bidirectional LSTM layers or another number of bidirectional LSTM layers). Concatenation unit 810 may concatenate the output of speaker-encoder 806 with the output of encoder stack 808. Attention is a type of input processing technique for neural networks. Attention enables neural networks (such as neural networks implemented in decoders 814 and spectrogram decoder 816) to focus on a subset of a complex input dataset or features. Attention mechanisms may be helpful in alignment of sequential data such as speech, text, etc. Spectrogram decoder 816 may be an autoregressive decoder that takes, as input for each time-step, attention data, hidden states from encoder stack 808 (i.e., an encoded spectrogram) for the time-step, output of speaker-encoder 806, and output of spectrogram decoder 816 for a previous time-step” teaches the decoder LSTM in the claim can be mapped to the autoregressive decoder in Translatotron 804, the encoder processes input sequence, and decoder generate output sequence for each timestamp differently, model process specific part of the input for each timestep at a time).
Grant and Ramadas are analogous art because they are both directed to using LSTM to make the prediction based on provided input.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ramadas to the disclosed invention of Grant.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, using autoregressive bidirectional LSTM “ improve the prediction of occurrences and/or temporal durations of sensitive-information utterances” (Ramadas, Para [0061]).
Claim 4.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 3,
Ramadas further teaches wherein the input-specific LSTMs receive at least a portion of the observation data, and wherein the decoder LSTM receives at least a portion of the observation data from the input-specific LSTMs and attention network (Para [0075] “Translatotron 804 may pass these hidden states through an attention-based alignment mechanism (i.e., attention units 812) to condition an autoregressive decoder (e.g., spectrogram decoder 816 or decoder 814A, 814B). Encoder stack 808 may be implemented as a stack of bidirectional LSTM layers (e.g., a stack of 8 bidirectional LSTM layers or another number of bidirectional LSTM layers). Concatenation unit 810 may concatenate the output of speaker-encoder 806 with the output of encoder stack 808. Attention is a type of input processing technique for neural networks. Attention enables neural networks (such as neural networks implemented in decoders 814 and spectrogram decoder 816) to focus on a subset of a complex input dataset or features. Attention mechanisms may be helpful in alignment of sequential data such as speech, text, etc. Spectrogram decoder 816 may be an autoregressive decoder that takes, as input for each time-step, attention data, hidden states from encoder stack 808 (i.e., an encoded spectrogram) for the time-step, output of speaker-encoder 806, and output of spectrogram decoder 816 for a previous time-step. The output of spectrogram decoder 816 may refer to the output of spectrogram decoder 816 as a “target spectrogram.” The target spectrogram represents sounds of an obfuscated sensitive-information utterance for the current time-step. Because spectrogram decoder 816 uses the output of speaker-encoder 816 as input, the obfuscated sensitive-information utterance may have vocal characteristics of user 106” teaches the input data could include spectrograms, which processed by the encoder stack, the LSTM bidirectional attention network to process sequential data. teaches the encoder processes input sequence (corresponding to observation data), and decoder (corresponding to control agent) generate output sequence (instruction) for each timestamp differently, model process specific part of the input for each timestep at a time).
Grant and Ramadas are analogous art because they are both directed to using LSTM to make the prediction based on provided input.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ramadas to the disclosed invention of Grant.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, using autoregressive bidirectional LSTM “ improve the prediction of occurrences and/or temporal durations of sensitive-information utterances” (Ramadas, Para [0061]).
Claim 5.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 1, further comprising:
Grant further teaches constraining instructions using actor masks (Column 10 & line 45-51 “the device management application provides, or is capable of providing, any entity associated with the client ecosystem or the plurality of devices therein advance notice of any personal data collection, including data collection via sensors, monitoring device(s), and/or autonomous/semi-autonomous device(s). The device management application further provides, or is capable of providing, any affected entity an option to opt in or opt out of any such personal data collection at any time” teaches opt out personal data collection (corresponds to actor mask)).
Claim 6.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 1,
Grant further teaches wherein receiving observation data comprises receiving observation data from a sensor agent comprising a fourth sensor (Fig. 2 teaches more than 4 sensor which send data to device management application Column 9 & lines 60-63 “In an embodiment, as reflected in the methods described herein, device management application 149 serves as a centralized virtual agent that captures sensor data related to device 205, device 215, device 225, device 235, and/or device 245 in client ecosystem 200” teaches device management application (control agent) collect sensor data (observation data) regarding performance of the client ecosystem).
Claim 7.
As discussed above, Grant in view of Ramadas further in view of Aradi teaches the method of claim 1, further comprising:
Grant further teaches training the control agent with synthetic training data (Column 3 & lines 1-6 “A server system configured to implement techniques associated with the various embodiments includes a device management application configured to analyze device sensor data in view of environmental sensor data via a machine learning knowledge model in order to determine a failure risk value for each of a plurality of devices” and Column 1 & lines 51-56“The step of constructing the machine learning knowledge model further includes applying at least one artificial intelligence algorithm to train the machine learning knowledge model based upon the stored operational information and the stored environmental information” teaches a train device management application (control agent) using collected environmental sensor data. The environmental sensor data corresponds to synthetic training data because unusual environmental conditions or failure case are synthetic cases).
Claim 8.
With respect to independent claim 8, claim 8 is substantially similar to claim 1 and therefore is rejected on the same ground as claim 1, discussed above. In particular, claim 8 is a system for multi-agent instruction generation that perform operations of claim 1.
Grant teaches a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (Column 3 & lines 52-56 “The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”).
Claim 9.
Claim 9 recites analogous limitations to claim 2. Therefore, claim 9 is rejected based on the same rationale as claim 2.
Claim 10.
Claim 10 recites analogous limitations to claim 3. Therefore, claim 10 is rejected based on the same rationale as claim 3.
Claim 11.
Claim 11 recites analogous limitations to claim 4. Therefore, claim 11 is rejected based on the same rationale as claim 4.
Claim 12.
Claim 12 recites analogous limitations to claim 5. Therefore, claim 12 is rejected based on the same rationale as claim 5.
Claim 13.
Claim 13 recites analogous limitations to claim 6. Therefore, claim 13 is rejected based on the same rationale as claim 6.
Claim 14.
Claim 14 recites analogous limitations to claim 7. Therefore, claim 14 is rejected based on the same rationale as claim 7.
Claim 15.
With respect to independent claim 15, claim 15 is substantially similar to claim 1 and therefore is rejected on the same ground as claim 1, discussed above. In particular, claim 15 is a computer program product that perform operations of claim 1.
Grant teaches a non-transmission computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of multi-agent instruction generation (Column 4 & lines 8-14 “A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire”).
Claim 16.
Claim 16 recites analogous limitations to claim 2. Therefore, claim 16 is rejected based on the same rationale as claim 2.
Claim 17.
Claim 17 recites analogous limitations to claim 3. Therefore, claim 17 is rejected based on the same rationale as claim 3.
Claim 18.
Claim 18 recites analogous limitations to claim 4. Therefore, claim 18 is rejected based on the same rationale as claim 4.
Claim 19.
Claim 19 recites analogous limitations to claim 5. Therefore, claim 19 is rejected based on the same rationale as claim 5.
Claim 20.
Claim 20 recites analogous limitations to claim 6. Therefore, claim 20 is rejected based on the same rationale as claim 6.
Response to Arguments
Applicant's arguments filed on 12/09/2025 with respect to 35 U.S.C. 101 rejections of claims 1-20 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 101 rejection of claims 1-20, applicant asserts, “These technical details go beyond mere mental steps or generic data processing. The claimed invention leverages a specialized deep learning architecture (autoregressive bidirectional LSTM attention network) tailored for multi-agent control in manufacturing tasks, allowing for dynamic, context-sensitive instruction generation that adapts to sensor data and the operational status of multiple agent types (human, autonomous, semi-autonomous, non-autonomous). This is not a generic computer implementation…The claims address specific technological challenges in manufacturing automation, including coordination of heterogeneous agents, adaptation to environmental changes, and real- time optimization based on feedback and constraints. This is a practical application that improves the functioning of manufacturing technology and multi-agent collaboration, not merely an abstract idea performed on a computer…The claims address specific technological challenges in manufacturing automation, including coordination of heterogeneous agents, adaptation to environmental changes, and real- time optimization based on feedback and constraints. This is a practical application that improves the functioning of manufacturing technology and multi-agent collaboration, not merely an abstract idea performed on a computer. Additionally, these elements are not routine or conventional. The Specification explains that prior solutions "require manual oversight...demonstrate limited flexibility in new environments and circumstances not explicitly programmed into the original design; and do not adapt well to unplanned processes and procedures...." See Specification paragraph 0002. The claimed methods, systems, and computer program products for multi-agent instruction generation overcome these deficiencies through a novel architecture and methodology” (Remarks Pg. 11-13).
Examiner Response:
The examiner respectfully disagrees with Applicant’s arguments. The applicant asserts that the claimed architecture improves autoregressive bidirectional LSTM attention network. However, the purported improvement is not reflected in the claim language itself. Specifically, the claims describe high level functional steps like generating the actor agents with receiving observation data and performing the manufacturing tasks by the control agent. However, the claims do not explain how these steps are implemented, nor do they describe any technical mechanism by which improvement to performance, efficiency, or accuracy are achieved. The claimed features constitute a general organizational or procedural concept rather than a concrete technical invention. Furthermore, the mechanism by how agents coordinate and how adaptation to environmental changes occurs is not recited with any specificity in the claims. The claim does not describe a specific way to solve these problems. The claims do not describe how the architecture addresses these challenges in a specific or technical way. Therefore, the rejections under 35 U.S.C. 101 are maintained for claims 1-20.
Applicant's arguments filed on 12/09/2025 with respect to 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but they are moot.
With respect to the 35 U.S.C. 103 rejection of claims 1-20, applicant asserts, “That is, GRANT discloses a management application that may determine an excessive failure risk, and specifically a duration of time to failure. As described in the Abstract, GRANT is generally directed to an approach for predicting equipment failures and scheduling maintenance activities based on real-time data and historical performance, not performing manufacturing tasks. GRANT does not disclose "based at least on the first set of instructions and the observation data, generating, by the control agent for the plurality of actor agents, a second set of instructions for performing a second set of manufacturing tasks, wherein the first set of instructions and the second set of instructions each includes control commands for the plurality of actor agents and at least one instruction comprises one or more of: a role assignment, a platform control, a tool selection, or a tool utilization," as recited in claim 1, as amended (emphasis added).
The Office Action does not rely on RAMADAS for allegedly disclosing the above- discussed features of amended claim 1, nor does RAMADAS disclose these features” (remarks Pages 15).
Examiner Response:
This argument has been considered but is moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in this argument. A newly cited prior art, (Aradi (“Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles”)) has been applied to teach the limitations referred to in this argument. Therefore, the claims are now rejected under 35 U.S.C. 103 using the newly-cited Aradi reference, as detailed above.
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lokesha Patel whose telephone number is (571)272-6267. The examiner can normally be reached 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LOKESHA PATEL/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125