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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 25, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because Figures 1-10 have text that is written on a shaded background, see 37 CFR 1.84(p)(3). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 13-15 and 17-20, the phrase "A system according to Claim 1” renders the claim indefinite because claim 1 is a method claim and not a system claim.
Regarding claim 16, the phrase “A system according to Claim 5” renders the claim indefinite because claim 5 is a method claim and not a system claim.
For the purposes of Examination, claims 13-15 and 17-20 will be interpreted as reciting “A system according to Claim 12” and claim 16 will be interpreted as reciting “A system according to Claim 15”.
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.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating a composition by combining two or more of the models”
“assembling components via a graph”
“performing simulation testing by mapping the composition into a simulation representative of the target system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“running input data through the assembly of components, wherein one of the components comprises two or more of the models”
“configuring the models and any associated computation services for the models”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtaining models each based on a technique of artificial intelligence and associated with a target function for which a goal is to be achieved”
“obtaining output data based on the running of input data through the composition components”
“employing control nodes to marshal the input data into or the output data out of the models”
“deploying the composition to the target system”
“obtaining information regarding actions to achieve the goal of the target system based on the deployed composition, wherein the actions are executed by the target system to achieve the goal or to control systems to achieve the goal”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the obtaining, employing, and deploying limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. 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). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. As an ordered whole, the claim is directed to a mentally performable process of generating a composition of models by combining them via a graph. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the graph comprises one of a directed acyclic graph, directed graph, or behavior tree”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“revising the models prior to inclusion in the composition”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the models are combined via chaining, parallel or interleaved configurations, or nested configurations”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“revising the composition based on the received edits”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“receiving edits to the composition prior to deployment”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. 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). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 5.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“running debugging tools on the composition to provide feedback for the revisions”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“running information from the target system in and out of the deployed composition via data connectors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“transforming the information regarding the actions based on language specific to the target system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate service code for the composition”
“generate a deployment definition for deploying”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“compile the composition into a set of container images”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“wherein the deployment is performed”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, deploying limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. 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). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the composition is displayed via a user interface”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method for deployment of composite artificial intelligence systems, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the goal to be achieved comprises one of a problem is to be solved, a system controlled, or data produced or transformed”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate a composition by combining two or more of the models”
“assemble components via a graph”
“perform simulation testing by mapping the composition into a simulation representative of the target system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A system for deployment of composite artificial intelligence systems, comprising: a database to store models each based on a technique of artificial intelligence and associated with a target function for which a goal is to be achieved; a server comprising a central processing unit, memory, an input port to receive the models, and an output port, wherein the central processing unit is configured to execute the program code to perform steps to:...”
“run input data through the assembly of components, wherein one of the components comprises two or more of the models”
“configuring the models and any associated computation services for the models”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“deploy the composition to the target system”
“obtain output data based on the running of input data through the composition components”
“employ control nodes to marshal the input data into or the output data out of the models”
“obtain information regarding actions to achieve the goal of the target system based on the deployed composition, wherein the actions are executed by the target system to achieve the goal or to control systems to achieve the goal”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the obtaining, employing, and deploying limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. 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). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. As an ordered whole, the claim is directed to a mentally performable process of generating a composition of models by combining them via a graph. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 12.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the graph comprises one of a directed acyclic graph, directed graph, or behavior tree”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 12.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the models are combined via chaining, parallel or interleaved configurations, or nested configurations”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“revises the composition based on the received edits”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the central processing unit receives edits to the composition prior to deployment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 15.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the central processing unit runs debugging tools on the composition to provide feedback for the revisions”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 12.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the central processing unit runs information from the target system in and out of the deployed composition via data connectors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“transforms the information regarding the actions based on language specific to the target system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the central processing unit…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating a service code for the composition”
“generating a deployment definition for deploying”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the central processing unit performs the deployment by…”
“compiling the composition into a set of container images”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system for deployment of composite artificial intelligence systems, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 12.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the composition is displayed via a user interface”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
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 the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. 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 (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.
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. Applicant is 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 1-4, 7-9, 11-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ashrafzadeh et al. (U.S. Patent Publication No. 2022/0414547) (“Ashrafzadeh”) in view of Tuli et al (MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems) (“Tuli”).
Regarding claim 1, Ashrafzadeh teaches a method for deployment of composite artificial intelligence systems, comprising: obtaining models each based on a technique of artificial intelligence and associated with a target function for which a goal is to be achieved (Ashrafzadeh [0020] “The MLS infrastructure determines, based on the tenant identifier and a type of the machine learning application, a first machine learning model that was generated based on a first training data set associated with the tenant identifier and a second machine learning model that was generated based on a second training data set associated with the tenant identifier [obtaining a first and second model based on training]. The MLS infrastructure executes, based on the type of the machine learning application, a flow of operations that includes running the first and second machine learning models with data related to the request to obtain a scoring result.”; [0065] “In some implementations, the function identifier in a node can identify a scoring service [a target function for which a goal is to be achieved] (which is accessed through a remote call such as webservice request, or an API call).” Ashrafzadeh obtaining a first and second machine learning model from training (a technique of artificial intelligence), which is associated with a scoring request and corresponding scoring service, wherein the functionality of the scoring service corresponds to the target function for which a goal is to be achieved.); generating a composition by combining two or more of the models (Ashrafzadeh [0036] “The machine learning application defines a flow of operations that includes the multiple machine learning models. In some implementations, the flow of operations can combine the machine learning models in sequence, where an output of a first model is fed to the next model in the sequence. Alternatively or additionally, the flow of operations can combine the machine learning models [combining two or more of the models] based on parallel independent executions of the machine learning models. In some implementations, the multi-model machine learning application implements an ensemble modeling process.” Ashrafzadeh teaches combing machine learning models using various implementations.), comprising: assembling components via a graph (Ashrafzadeh [0021] “Additionally or alternatively, the MLS infrastructure supports the definition of a flow of operations of a machine learning application based on a directed acyclic graph (DAG) structure.”; [0059] “The implementations described herein provide a flexible and dynamic structure for defining a machine learning application based on a directed acyclic graph structure.” Ashrafzadeh teaches defining the machine learning flow of operations based on a directed acyclic graph (DAG) structure.); and running input data through the assembly of components, wherein one of the components comprises two or more of the models (Ashrafzadeh [0038] “The first scoring service 220A receives the processed data as input and uses the first machine learning model to obtain a first scoring result. The third element 230A represents a second scoring service. The second scoring service is associated with a second type of machine learning models. The second scoring service 230A receives data based on the output of the first scoring service and uses a machine learning model of the second type to obtain a second scoring result.” Ashrafzadeh teachings processing input data through the first and second machine learning models.); and obtaining output data based on the running of input data through the composition components (Ashrafzadeh [0038] “The first scoring service 220A receives the processed data as input and uses the first machine learning model to obtain a first scoring result. The third element 230A represents a second scoring service. The second scoring service is associated with a second type of machine learning models. The second scoring service 230A receives data based on the output of the first scoring service and uses a machine learning model of the second type to obtain a second scoring result. The data that is fed to the second scoring service 230A can be the output of the first scoring service 220A or a modified version of this output. The modified version of the output of the first scoring service 220A includes features to be used by the second machine learning model to make a prediction.” Ashrafzadeh provides output data from running the input data through the first and second machine learning models.); employing control nodes to marshal the input data into or the output data out of the models (Ashrafzadeh [0021] “The MLS infrastructure executes nodes of the graph structure based on the configuration parameters to obtain a scoring result. The execution of the nodes includes executing a node, based on the configuration parameters, that causes a machine learning model generated for the first tenant to be applied to data related to the request.”; [0060] “FIG. 4A illustrates a block diagram of nodes that can be used in a DAG for defining a machine learning application, in accordance with some implementations. A DAG may include a node 400A of type Constant, a node 400B of type transform, a node 400C of type combine, a node 400D of type branch, a node 400E of type dynamic, a node 400F of type condition.” Ashrafzadeh teaches a plurality of control nodes, as shown in figure 4A.) and configuring the models and any associated computation services for the models (Ashrafzadeh [0070] “At operation 506, the router 130 determines, based on the tenant identifier and the type of the machine learning application, configuration parameters and a graph structure that defines a flow of operations for the machine learning application.”; [0073] “The router 130 is operative to execute operations of a node within the same process as the one handling the management of the execution of the remote service calls (e.g., remote call to a scoring service) and coordinate inputs and outputs between the nodes for execution of the flow of operations of the machine learning application.” Ashrafzadeh teaches a router for configuring models including the management of the execution of the remote service calls, corresponding to computation services for the models.); …deploying the composition to the target system (Ashrafzadeh [0042] “The flow of operations is executable. In some implementations, the flow of operations can be deployed through an API call (e.g., an HTTP POST request) that makes the flow of operations visible to a provisioning process. The provisioning process executes the flow of operations. In some implementations, the flow of operations is provisioned/executed in a containerized environment.” Ashrafzadeh teaches machine learning model deployment to a containerized environment, corresponding to deploying the composition to a target system.); and obtaining information regarding actions to achieve the goal of the target system based on the deployed composition (Ashrafzadeh [0070] “In some implementations, the configuration parameters can include tenant specific context (e.g., records, and/or history) that can be used when executing the nodes of the graph structure for responding to the request. In some implementations, the configuration parameters can further include tenant specific functions and/or models to be used during execution of the graph structure. In some implementations, the configuration parameters can be determined based on the tenant identifier and the type of machine learning application. In some implementations, the configuration parameters can be determined based on the identifier of the graph structure.” Ashrafzadeh teaches obtaining tenant specific context (e.g., records, and/or history) from executing nodes of the graph structure for the scoring request, which is information regarding actions to achieve a goal of the containerized environment, which is a target system.), wherein the actions are executed by the target system to achieve the goal or to control systems to achieve the goal (Ashrafzadeh [0034] “The router 130 makes decisions to load, rebalance, delete, distribute, and replicate the scoring services 131 in the serving containers 160A-N.”; [0071] “At operation 508, the router 130 executes nodes of the graph structure based on the configuration parameters to obtain a scoring result. The execution of the nodes includes executing a first node, based on the first configuration parameters, that causes a first machine learning model generated for the tenant to be applied to data related to the request.” Ashrafzadeh teaches the router 130 executes nodes of the graph structure based on the configuration parameters to obtain a scoring result, wherein the router 130 makes decisions in the serving containers, and is therefore part of the containerized environment, corresponding to the actions are executed by the target system to achieve a goal (i.e., scoring).).
Ashrafzadeh fails to explicitly teach …performing simulation testing by mapping the composition into a simulation representative of the target system.
However, Tuli teaches performing simulation testing by mapping the composition into a simulation representative of the target system (Tuli Section 4 Deep Surrogate Model “An overview of the deep neural network used as a surrogate model in the MCDS scheduler is shown in Fig. 2.. Fig. 2. Deep Surrogate Model used in MCDS Scheduler. The three inputs to the model are shown in red. Feed-forward, graph convolution and attention operations are shown in blue, purple and orange. [a composition comprising a feed-forward, graph convolution, and attention model combination]”; Section 6.1 Evaluation Setup “We use two-test beds to test the scheduling strategies: physical setup and a simulated platform... Simulated Testbed: We consider 50 host machines as a scaled up version of the physical setup [a simulation representative of the target system]. Here, each category has 5 times the instance count, i.e., 30 edge nodes and 20 cloud nodes. This is to test models in a larger-scale edge cloud environment as considered in prior art [30], [42]. We use a trace driven simulation model that emulates real execution of tasks, but without actual job execution, allowing us to perform experiments on scale. [performing simulation testing]” Tuli teaches a deep surrogate model, as shown in Figure 2, corresponding to the composition, wherein simulation testing of the model is performed using host machines representing a physical setup, corresponding to mapping the composition into a simulation representative of the target system.);
Ashrafzadeh and Tuli are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to machine learning model deployment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh with the above teachings of Tuli. Doing so would allow the models to be scalable and outperform other models in terms of energy consumption, response time, SLA violation rate and cost by up to 6.13%, 4.56%, 45.09% and 30.71% respectively in a heterogeneous mobile edge-cloud environment with real-world workloads (Section 7 Conclusion “All these contributions allow MCDS to be scalable and outperform other models in terms of energy consumption, response time, SLA violation rate and cost by up to 6.13%, 4.56%, 45.09% and 30.71% respectively in a heterogeneous mobile edge-cloud environment with real-world workloads”).
Regarding claim 2, Ashrafzadeh in view of Tuli teaches wherein the graph comprises one of a directed acyclic graph, directed graph, or behavior tree (Ashrafzadeh [0021] “Additionally or alternatively, the MLS infrastructure supports the definition of a flow of operations of a machine learning application based on a directed acyclic graph (DAG) structure.” Ashrafzadeh teaches a directed acyclic graph.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 3, Ashrafzadeh in view of Tuli teaches further comprising: revising the models prior to inclusion in the composition (Ashrafzadeh [0040] “FIG. 3A illustrates a flow diagram of exemplary operations that can be performed in an MLS infrastructure, in accordance with some implementations. The MLS infrastructure can support one or multiple types of machine learning models. Each type of machine learning model can be trained based on tenant data to obtain a machine learning model.”; [0041] “At operation 302, the MLS infrastructure 100 trains a first type of machine learning models based on first tenant training data to obtain a first machine learning model. The first machine learning model is associated with a unique identifier. At operation 304, the MLS infrastructure 100 trains a second type of machine learning models based on second tenant training data to obtain a second machine learning model.”; [0042] “At operation 306, a flow of operations that includes the first and second machine learning models is created.” Ashrafzadeh teaches training the first and second machine learning models (revising the models) prior to executing the flow of operations at operation 306 shown in Figure 3A, wherein the execution of the first and second models at operation 306 corresponds to the combined model composition comprising the first and second models.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 4, Ashrafzadeh in view of Tuli teaches wherein the models are combined via chaining, parallel or interleaved configurations, or nested configurations (Ashrafzadeh [0036] “The machine learning application defines a flow of operations that includes the multiple machine learning models. In some implementations, the flow of operations can combine the machine learning models in sequence, where an output of a first model is fed to the next model in the sequence. Alternatively or additionally, the flow of operations can combine the machine learning models based on parallel independent executions of the machine learning models.” Ashrafzadeh teaches sequential (chaining) or parallel model combination configurations.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 7, Ashrafzadeh in view of Tuli teaches running information from the target system in and out of the deployed composition via data connectors (Ashrafzadeh [0042] “In some implementations, the flow of operations can be deployed through an API call (e.g., an HTTP POST request) that makes the flow of operations visible to a provisioning process. The provisioning process executes the flow of operations. In some implementations, the flow of operations is provisioned/executed in a containerized environment.” Ashrafzadeh provides APIs corresponding to data connectors for running information from the target system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 8, Ashrafzadeh in view of Tuli teaches transforming the information regarding the actions based on language specific to the target system (Ashrafzadeh [0038] “A first element 210A represents a preprocessing operation. The preprocessing operation 210A receives data related to the request as input, performs one or more operations on the data, and outputs processed data. In some implementations, preprocessing the data includes preparing the data for the first scoring service 220A.”; [0061] “The constant node takes the request as input and outputs the next node that is to be performed in the graph. A transform node 400B is coupled with a first node and a second node. The transform node 400B receives an input from the first node, transforms the data according to a function that is defined for the node, and outputs the transformed data to the second node.” Ashrafzadeh discloses transform nodes for the request input data from a target system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 9, Ashrafzadeh in view of Tuli teaches wherein the deployment is performed (Ashrafzadeh [0042] “The flow of operations can be referred to as a pipeline. The flow of operations is executable. In some implementations, the flow of operations can be deployed through an API call (e.g., an HTTP POST request) that makes the flow of operations visible to a provisioning process. The provisioning process executes the flow of operations. In some implementations, the flow of operations is provisioned/executed in a containerized environment.” Ashrafzadeh teaches performing deployment via an API.), comprising: generate service code for the composition (Ashrafzadeh [0064] “A developer/data scientist can use the language and/or the DSL code of FIGS. 4B-C to define a custom machine learning application that is applicable to one or more tenants. In the serialization language case, the developer defines two or more nodes and connects them.” Ashrafzadeh provides generated service code for the model composition, as shown in FIG4B.); compile the composition into a set of container images (Ashrafzadeh [0042] “The provisioning process executes the flow of operations. In some implementations, the flow of operations is provisioned/executed in a containerized environment.”; [0066] “When a developer defines the machine learning application using data serialization language, the MLS infrastructure compiles data serialization language file into code, which is interpreted to obtain an executable version of the machine learning application.” Ashrafzadeh teaches compiling the models in a container.); and generate a deployment definition for deploying (Ashrafzadeh [0029] “Applications that are containerized can be quickly deployed to many target environments including data centers, cloud architectures, or individual workstations.”; [0030] “In some implementations, each of the serving containers registers with a service discovery system 150 by providing the serving container's registration information, such as the host, the port, functions, or similar information. When any of the serving containers is no longer available or becomes unavailable, the service discovery system 150 deletes the unavailable serving container's registration information.” Ashrafzadeh teaches deployment information for deploying containers.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 11, Ashrafzadeh in view of Tuli teaches wherein the goal to be achieved comprises one of a problem is to be solved, a system controlled, or data produced or transformed (Ashrafzadeh [0024] “The applications 110 can be any program or software to perform a set of tasks or operations. A ‘set,’ as used herein includes any positive whole number of items including a single item. The applications are operative to make requests to one or more machine learning applications of the MLS infrastructure 100 and receive scoring results for the requests.” Ashrafzadeh teaches a goal comprising receiving scoring results for requests, corresponding to the goal to be achieved comprises data produced.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 12, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further Ashrafzadeh teaches a system for deployment of composite artificial intelligence systems, comprising: a database to store models each based on a technique of artificial intelligence and associated with a target function for which a goal is to be achieved (Ashrafzadeh [0041] “Each of the machine learning models is associated with a unique identifier. The machine learning models are stored in a data storage 170. The machine learning models can be retrieved based on an identifier.” Ashrafzadeh provides data storage 170, wherein the models can be retrieved based on an identifier, corresponding to a database for storing the models.); a server comprising a central processing unit (Ashrafzadeh [0090] “FIG. 6B is a block diagram of a deployment environment according to some example implementations. A system 640 includes hardware (e.g., a set of one or more server devices) and software to provide service(s) 642” Ashrafzadeh teaches server devices corresponding to a server comprising a CPU.), memory ([0081] “For example, an electronic device may include a set of one or more processors coupled to one or more machine-readable storage media (e.g., non-volatile memory such as magnetic disks, optical disks, read only memory (ROM), Flash memory, phase change memory, solid state drives (SSDs)) to store code and optionally data.” Ashrafzadeh teaches memory), an input port to receive the models, and an output port (Ashrafzadeh [0030] “In some implementations, each of the serving containers registers with a service discovery system 150 by providing the serving container's registration information, such as the host, the port, functions, or similar information.”; [0042] “In some implementations, the flow of operations can be deployed through an API call (e.g., an HTTP POST request) that makes the flow of operations visible to a provisioning process. The provisioning process executes the flow of operations. In some implementations, the flow of operations is provisioned/executed in a containerized environment.” Ashrafzadeh provides containers with corresponding port information for receiving the machine learning models, corresponding to input and output ports.), wherein the central processing unit is configured to execute the program code (Ashrafzadeh [0083] “Software instructions (also referred to as instructions) are capable of causing (also referred to as operable to cause and configurable to cause) a set of processors to perform operations when the instructions are executed by the set of processors.” Ashrafzadeh teaches a CPU to execute program code.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 13, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 14, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 17, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 7.
Regarding claim 18, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 8.
Regarding claim 19, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli for the same reasons disclosed above in the rejection of claim 9.
Claims 5-6, 10, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ashrafzadeh et al. (U.S. Patent Publication No. 2022/0414547) (“Ashrafzadeh”) in view of Tuli et al. (MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems) (“Tuli”) in further view of Achin et al. (U.S. Patent Publication No. 2023/0083891) (“Achin”).
Regarding claim 5, Ashrafzadeh in view of Tuli teaches the method according to claim 1, as discussed above in the rejection of claim 1, but fails to teach receiving edits to the composition prior to deployment; and revising the composition based on the received edits.
However, Achin teaches receiving edits to the composition prior to deployment (Achin [0032] “Additionally, the system allows end users to visually generate and/or edit the workflow 200A without interacting with the underlying code. For instance, the system can transform and execute the workflow 200A into the workflow 200B depicted in FIG. 2B using the method 100.”; [0181] “User interface 1020 may include a variety of interface components that allow users to manage multiple modeling projects within an organization, create and modify elements of the modeling methodology hierarchy, conduct comprehensive searches for accurate predictive models, gain insights into the dataset and model results, and/or deploy completed models to produce predictions on new data.” Achin teaches users providing edits for a machine learning workflow.); and revising the composition based on the received edits (Achin [0034] “The end user operating the user device may access an electronic platform hosted by the system. The system may display the GUIs depicted in FIGS. 2B-3D via the platform. For instance, the system may display the GUI 200 that allows the end user to use various input elements to draw or revise the workflow 200B.” Achin teaches revising the machine learning workflow based on the edits.).
Ashrafzadeh, Tuli and Achin are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to machine learning model deployment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli with the above teachings of Achin. Doing so may improve the performance of a predictive modeling system, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system has not accurately estimated a modeling procedure's suitability for a prediction problem (Achin [0121] “Allowing the users to select modeling procedures for execution may improve the performance of predictive modeling system 600, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system 600 has not accurately estimated a modeling procedure's suitability for a prediction problem.”).
Regarding claim 6, Ashrafzadeh in view of Tuli in further view of Achin teaches further comprising: running debugging tools on the composition to provide feedback for the revisions (Achin [0126] “The user may investigate the cause of the poor performance and determine, for example, that the poor performance is caused by an error in the preparation of the dataset. The user can then fix the error and restart execution of the modeling procedures that were affected by the error.” Achin provides users investigating an error in data preparation and restarting execution of procedures that were affected by the error, corresponding to running debugging tools for feedback to revisions.).
Ashrafzadeh, Tuli and Achin are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to machine learning model deployment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli with the above teachings of Achin. Doing so may improve the performance of a predictive modeling system, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system has not accurately estimated a modeling procedure's suitability for a prediction problem (Achin [0121] “Allowing the users to select modeling procedures for execution may improve the performance of predictive modeling system 600, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system 600 has not accurately estimated a modeling procedure's suitability for a prediction problem.”).
Regarding claim 10, Ashrafzadeh in view of Tuli teaches the method according to claim 1, as discussed above in the rejection of claim 1, but fails to explicitly teach wherein the composition is displayed via a user interface.
However, Achin teaches wherein the composition is displayed via a user interface (Achin [0034] “Referring now to FIGS. 2B-3D, various GUIs and elements provided by the system are depicted in accordance with different embodiments. The end user operating the user device may access an electronic platform hosted by the system. The system may display the GUIs depicted in FIGS. 2B-3D via the platform. For instance, the system may display the GUI 200 that allows the end user to use various input elements to draw or revise the workflow 200B.” Achin teaches a user interface.).
Ashrafzadeh, Tuli and Achin are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to machine learning model deployment. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashrafzadeh in view of Tuli with the above teachings of Achin. Doing so may improve the performance of a predictive modeling system, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system has not accurately estimated a modeling procedure's suitability for a prediction problem (Achin [0121] “Allowing the users to select modeling procedures for execution may improve the performance of predictive modeling system 600, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system 600 has not accurately estimated a modeling procedure's suitability for a prediction problem.”).
Regarding claim 15, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli in further view of Achin for the same reasons disclosed above in the rejection of claim 5.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli in further view of Achin for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 20, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Ashrafzadeh in view of Tuli in further view of Achin for the same reasons disclosed above in the rejection of claim 10.
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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