METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR GENERATING MACHINE LEARNING MODEL

Application No. 18/484,731

• Filed: Oct 11, 2023
• Priority: Sep 15, 2023 — CN 202311198552.3
• Examiner: LAI, DYLAN HONG
• Art Unit: 2144
• Tech Center: 2100 — Computer Architecture & Software
• Assignee: Dell Products L.P.
• OA Round: 1 (Non-Final)

Office Action

Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Specification
The disclosure is objected to because of the following informalities: on page 6, line 8, "At block 206, ... using the machine learning mode." should be "At block 206, ... using the machine learning model", i.e. change mode to model.
Appropriate correction is required.

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.

Claim 1 recites “generating a machine learning model” in the preamble and also recites the limitation, “inputting the encoded decision tree into a machine learning model” in line 5. There is conflicting antecedent basis for this limitation in the claim.
Claims rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are: the relationship between inputting and using a machine learning model, then after generating the machine learning model. Normally, a machine learning model must be generated before the machine learning model can be used or have information input into it. Thus, it is unclear if the machine learning model in the limitations, “inputting the encoded decision tree into a machine learning model”, “generating a question corresponding to each node of a root node and internal nodes of the decision tree using the machine learning model”, and “inputting a natural language text to the machine learning model” is the same machine learning in the limitation, “generating the machine learning model using the inputted natural language text and the generated question” or if it is a new machine learning model.

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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines(“2019 PEG”).
Step 1: Independent claims 1 (A method for generating a machine learning model, comprising), 10 (An electronic device, comprising), and 19 (A computer program product tangibly stored on a non-transitory computer-readable storage medium…) are directed towards a method, a machine, and a manufacture respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine, manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
generating a question corresponding to each node of a root node and internal nodes of the decision tree
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a question that corresponds to each node of the decision tree. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 2
Step 2A, Prong 1: The claim recites, inter alia:
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select a question based on the answer. See MPEP 2106.04(a)(2)(III);
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text …
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
acquiring an answer to the first internal node question from the natural language text;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 3
Step 2A, Prong 1: The claim recites, inter alia:
constructing a decision chain using the answers acquired from the natural language text and the corresponding questions;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to mentally link nodes into a path based on the answers acquired. See MPEP 2106.04(a)(2)(III)
comparing the decision chain with the decision tree; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to compare the mental path with the decision tree. See MPEP 2106.04(a)(2)(III)
determining whether a path consistent with the decision chain exists in the decision tree.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide if the mental path matches a path in the decision tree. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 4
Step 2A, Prong 1: The claim recites, inter alia:
adjusting, in the case where no path consistent with the decision chain exists in the decision tree, the machine learning model using a loss function so as to minimize a path deviation between the decision tree and the decision chain.
This limitation recites a mathematical concept of utilizing a loss function to calculate loss to minimize error. See MPEP 2106.04(a)(2)(I).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 5
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic failure message output. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic failure message output. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 6
Step 2A, Prong 1: The claim recites, inter alia:
converting the decision tree into a directional acyclic graph form; and
This limitation recites a mathematical concept of conversion of a decision tree into the mathematical directional acyclic graph form . See MPEP 2106.04(a)(2)(I).
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
This limitation recites a mathematical concept of encoding a graph and embedding encoding into a machine learning model. See MPEP 2106.04(a)(2)(I).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 7
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output generic answers acquired by the machine learning model. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output generic answers acquired by the machine learning model. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 8
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
inputting a target natural language text to the machine learning model; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic machine learning model. See MPEP 2106.05(g);
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to cause a generic machine learning model to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
inputting a target natural language text to the machine learning model; and
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to cause a generic machine learning model to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 9
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic additional question. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic additional question. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 10
Step 2A, Prong 1: The claim recites, inter alia:
generating a question corresponding to each node of a root node and internal nodes of the decision tree
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a question that corresponds to each node of the decision tree. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
a processor; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
a memory, the memory being coupled to the processor and storing instructions, wherein the instructions, when executed by the processor, cause the electronic device to perform the following actions:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
a processor; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
a memory, the memory being coupled to the processor and storing instructions, wherein the instructions, when executed by the processor, cause the electronic device to perform the following actions:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 11
Step 2A, Prong 1: The claim recites, inter alia:
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select a question based on the answer. See MPEP 2106.04(a)(2)(III);
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text …
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
acquiring an answer to the first internal node question from the natural language text;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 12
Step 2A, Prong 1: The claim recites, inter alia:
constructing a decision chain using the answers acquired from the natural language text and the corresponding questions;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to mentally link nodes into a path based on the answers acquired. See MPEP 2106.04(a)(2)(III)
comparing the decision chain with the decision tree; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to compare the mental path with the decision tree. See MPEP 2106.04(a)(2)(III)
determining whether a path consistent with the decision chain exists in the decision tree.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide if the mental path matches a path in the decision tree. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 13
Step 2A, Prong 1: The claim recites, inter alia:
adjusting, in the case where no path consistent with the decision chain exists in the decision tree, the machine learning model using a loss function so as to minimize a path deviation between the decision tree and the decision chain.
This limitation recites a mathematical concept of utilizing a loss function to calculate loss to minimize error. See MPEP 2106.04(a)(2)(I).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 14
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic failure message output. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic failure message output. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 15
Step 2A, Prong 1: The claim recites, inter alia:
converting the decision tree into a directional acyclic graph form; and
This limitation recites a mathematical concept of conversion of a decision tree into the mathematical directional acyclic graph form . See MPEP 2106.04(a)(2)(I).
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
This limitation recites a mathematical concept of encoding a graph and embedding encoding into a machine learning model. See MPEP 2106.04(a)(2)(I).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 16
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output generic answers acquired by the machine learning model. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output generic answers acquired by the machine learning model. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 17
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
inputting a target natural language text to the machine learning model; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic machine learning model. See MPEP 2106.05(g);
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to cause a generic machine learning model to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
inputting a target natural language text to the machine learning model; and
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to cause a generic machine learning model to output a generic scheme of a leaf node of a generic decision tree. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 18
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic additional question. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to output a generic additional question. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 19
Step 2A, Prong 1: The claim recites, inter alia:
generating a question corresponding to each node of a root node and internal nodes of the decision tree
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a question that corresponds to each node of the decision tree. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A computer program product tangibly stored on a non-transitory computer-readable storage medium and comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a machine, cause the machine to perform the following actions:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A computer program product tangibly stored on a non-transitory computer-readable storage medium and comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a machine, cause the machine to perform the following actions:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
encoding a decision tree using a graph neural network;
This limitation is recited at a high level of generality and recites use of a generic graph neural network to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic graph neural network in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting the encoded decision tree into a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
inputting a natural language text to the machine learning model; and
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
generating the machine learning model using the inputted natural language text and the generated question.
This limitation is recited at a high level of generality and recites application of the abstract idea in order to generate a generic machine learning model. Mere recitation that a judicial exception is to be performed using an unspecified algorithm to create a generic machine learning model cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 20
Step 2A, Prong 1: The claim recites, inter alia:
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select a question based on the answer. See MPEP 2106.04(a)(2)(III);
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text …
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
acquiring an answer to the first internal node question from the natural language text;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge and decide an answer to a question. See MPEP 2106.04(a)(2)(III);
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
… using a machine learning model;
This limitation is recited at a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text.
This limitation is recited at a high level of generality and recites repeated application of an abstract idea. Mere recitation that a judicial exception is to be performed repetitively cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);

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.

Claim(s) 1, 10, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230032963 A1 by Dahlgren et al., hereafter Dahlgren in view of US 20180373986 A1 by Rainwater, hereafter Rainwater, and US 20210294781 A1 by Fernandez Musoles et al., hereafter Fernandez Musoles.

Regarding claim 1, Dahlgren teaches:
A method for generating a machine learning model, comprising:
inputting the encoded decision tree into a machine learning model; ((Dahlgren) Paragraph [0093], “The data gathered from the questions, including answers from the decision trees, is used to train the model 154a” Model 154a is a machine learning model and being used to train the model means that the decision tree is being input into the model.)
generating a question corresponding to each node of a root node and internal nodes of the decision tree using the machine learning model; ((Dahlgren) Paragraph [0054], “Once the user 130 begins the interaction (e.g., electronically) with the module 153, for example, the question and answer template submodule 153a creates various questions for the user by operating using a dynamic decision tree…”)
inputting a natural language text to the machine learning model; ((Dahlgren) Paragraph [0097], “The user 130 answers to questions from the questionnaires, decision trees, and other questions posed to the users, are, for example, augmented with Boolean values that are fed into the AI engine and derived from the answer to the question and/or third party data when relevant.” A user answering questions that are fed into an AI engine is inputting natural language text to a machine learning model.)
Dahlgren does not explicitly disclose:
encoding a decision tree using a graph neural network;
generating the machine learning model using the inputted natural language text and the generated question.
Rainwater teaches:
Encoding a decision tree using a graph neural network ((Rainwater) Paragraph [0093], “For example, subject data may be source code to be analyzed or maybe a decision tree reflecting a decision process over business domain or troubleshooting domain.”; Paragraph [0094], “At step 920, process 900 transforms the subject data access at step 910 into a directed acyclic graph… Then, at step 930, process 900 generates a network graph based on the directed acyclic graph for the subject data that was created at step 920.”, Paragraph [0095], “To determine to perform the task, at step 940, process 900 may apply a trained weight set to the DMLP network graph generated at step 930.” Process 900 transforms subject data which may be a decision tree into a directed acyclic graph, which is used to generate a DMLP network graph which is a graph neural network. This process encodes the decision tree by generating the DMLP network graph.) and inputting a decision tree into a machine learning model ((Rainwater) Paragraph [0071], “FIG. 7 shows a flowchart representing training process 700 for training a machine learning system using DMLPs.”; Paragraph [0073], “At step 705, process 700 access training data sets having an input and an expected output. The input of the training data sets may be inherently non-sequential or conditional as opposed to sequential. As described herein, one example of inherently non-sequential data is source code. Other examples may include decision tree datasets for conducting troubleshooting, customer service, or performing some other task where performance of the task may rely on the presence or absence of conditions or categorization and analysis of collections of non-sequential documents such as websites.” Process 700 uses input data that may include a decision tree for a machine learning system which is a machine learning model.).
Rainwater and Dahlgren are in the same area of invention, that being the use of machine learning models and including the use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the encoding a decision tree by generating a graph neural network, as taught by Rainwater, and using the decision tree as input, as taught by Rainwater, into the question and answer submodule machine learning model creating questions using a dynamic decision tree as taught by Dahlgren in order to more accurately train the machine learning model in cases such as customer service. This application of a known technique to a known structure would result in a predictable result of an encoded decision tree input into a machine learning model to be used to generate questions.
Dahlgren, in view of Rainwater, does not explicitly disclose:
generating the machine learning model using the inputted natural language text and the generated question.
Fernandez Musoles teaches:
Generating a machine learning model using questions and natural language text. ((Fernandez Musoles) Paragraph [0105], “Some embodiments may generate a pre-trained machine learning model with training data having a set of training questions and set training documents from a corpus.” A set of training questions is equivalent to the generated question. Documents from a corpus are equivalent to the inputted natural language text.)
Fernandez Musoles is in the same area of invention as Dahlgren, that being the generating of questions and answers involving the use of machine learning models.
Thus, it would have been obvious to a person having ordinary skill in the art to have added the generating a machine learning model using questions and natural language text, as taught by Fernandez Musoles, as a final step in the method of generating a machine learning model as taught by Dahlgren in order to have a useful tool to retrieve meaningful information for a query under limiting conditions. This combination of combining generation of a machine learning model with the generation of specific inputs to the model does not change the functionality of either invention and would produce a predictable result.

Regarding claim 10, Dahlgren teaches:
A decision tree (Paragraph [0054], “…by operating using a dynamic decision tree…”)
An electronic device, comprising:
a processor; and
a memory, the memory being coupled to the processor and storing instructions, wherein the instructions, when executed by the processor, cause the electronic device to perform the following actions: ((Dahlgren) Paragraph [0025], “The computer system 120, which includes the platform 150, includes components, such as various processors, storage/memory, modules, engines, models, interfaces and the like.”)
inputting the encoded decision tree into a machine learning model; ((Dahlgren) Paragraph [0093], “The data gathered from the questions, including answers from the decision trees, is used to train the model 154a” Model 154a is a machine learning model and being used to train the model means that the decision tree is being input into the model.)
generating a question corresponding to each node of a root node and internal nodes of the decision tree using the machine learning model; ((Dahlgren) Paragraph [0054], “Once the user 130 begins the interaction (e.g., electronically) with the module 153, for example, the question and answer template submodule 153a creates various questions for the user by operating using a dynamic decision tree…”)
inputting a natural language text to the machine learning model; ((Dahlgren) Paragraph [0097], “The user 130 answers to questions from the questionnaires, decision trees, and other questions posed to the users, are, for example, augmented with Boolean values that are fed into the AI engine and derived from the answer to the question and/or third party data when relevant.” A user answering questions that are fed into an AI engine is inputting natural language text to a machine learning model.)
Dahlgren does not explicitly disclose:
encoding a decision tree using a graph neural network;
generating the machine learning model using the inputted natural language text and the generated question.
Rainwater teaches:
Encoding a decision tree using a graph neural network ((Rainwater) Paragraph [0093], “For example, subject data may be source code to be analyzed or maybe a decision tree reflecting a decision process over business domain or troubleshooting domain.”; Paragraph [0094], “At step 920, process 900 transforms the subject data access at step 910 into a directed acyclic graph… Then, at step 930, process 900 generates a network graph based on the directed acyclic graph for the subject data that was created at step 920.”, Paragraph [0095], “To determine to perform the task, at step 940, process 900 may apply a trained weight set to the DMLP network graph generated at step 930.” Process 900 transforms subject data which may be a decision tree into a directed acyclic graph, which is used to generate a DMLP network graph which is a graph neural network. This process encodes the decision tree by generating the DMLP network graph.) and inputting a decision tree into a machine learning model ((Rainwater) Paragraph [0071], “FIG. 7 shows a flowchart representing training process 700 for training a machine learning system using DMLPs.”; Paragraph [0073], “At step 705, process 700 access training data sets having an input and an expected output. The input of the training data sets may be inherently non-sequential or conditional as opposed to sequential. As described herein, one example of inherently non-sequential data is source code. Other examples may include decision tree datasets for conducting troubleshooting, customer service, or performing some other task where performance of the task may rely on the presence or absence of conditions or categorization and analysis of collections of non-sequential documents such as websites.” Process 700 uses input data that may include a decision tree for a machine learning system which is a machine learning model.).
Rainwater and Dahlgren are in the same area of invention, that being the use of machine learning models and including the use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the encoding a decision tree by generating a graph neural network, as taught by Rainwater, and using the decision tree as input, as taught by Rainwater, into the question and answer submodule machine learning model creating questions using a dynamic decision tree as taught by Dahlgren in order to more accurately train the machine learning model in cases such as customer service. This application of a known technique to a known structure would result in a predictable result of an encoded decision tree input into a machine learning model to be used to generate questions.
Dahlgren, in view of Rainwater, does not explicitly disclose:
generating the machine learning model using the inputted natural language text and the generated question.
Fernandez Musoles teaches:
Generating a machine learning model using questions and natural language text. ((Fernandez Musoles) Paragraph [0105], “Some embodiments may generate a pre-trained machine learning model with training data having a set of training questions and set training documents from a corpus.” A set of training questions is equivalent to the generated question. Documents from a corpus are equivalent to the inputted natural language text.)
Fernandez Musoles is in the same area of invention as Dahlgren, that being the generating of questions and answers involving the use of machine learning models.
Thus, it would have been obvious to a person having ordinary skill in the art to have added the generating a machine learning model using questions and natural language text, as taught by Fernandez Musoles, as a final step in the method of generating a machine learning model as taught by Dahlgren in order to have a useful tool to retrieve meaningful information for a query under limiting conditions. This combination of combining generation of a machine learning model with the generation of specific inputs to the model does not change the functionality of either invention and would produce a predictable result.

Regarding claim 19, Dahlgren teaches:
A decision tree (Paragraph [0054], “…by operating using a dynamic decision tree…”)
A computer program product tangibly stored on a non-transitory computer-readable storage medium and comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a machine, cause the machine to perform the following actions: ((Dahlgren) Paragraph [0025], “The computer system 120, which includes the platform 150, includes components, such as various processors, storage/memory, modules, engines, models, interfaces and the like.”)
inputting the encoded decision tree into a machine learning model; ((Dahlgren) Paragraph [0093], “The data gathered from the questions, including answers from the decision trees, is used to train the model 154a” Model 154a is a machine learning model and being used to train the model means that the decision tree is being input into the model.)
generating a question corresponding to each node of a root node and internal nodes of the decision tree using the machine learning model; ((Dahlgren) Paragraph [0054], “Once the user 130 begins the interaction (e.g., electronically) with the module 153, for example, the question and answer template submodule 153a creates various questions for the user by operating using a dynamic decision tree…”)
inputting a natural language text to the machine learning model; ((Dahlgren) Paragraph [0097], “The user 130 answers to questions from the questionnaires, decision trees, and other questions posed to the users, are, for example, augmented with Boolean values that are fed into the AI engine and derived from the answer to the question and/or third party data when relevant.” A user answering questions that are fed into an AI engine is inputting natural language text to a machine learning model.)
Dahlgren does not explicitly disclose:
encoding a decision tree using a graph neural network;
generating the machine learning model using the inputted natural language text and the generated question.
Rainwater teaches:
Encoding a decision tree using a graph neural network ((Rainwater) Paragraph [0093], “For example, subject data may be source code to be analyzed or maybe a decision tree reflecting a decision process over business domain or troubleshooting domain.”; Paragraph [0094], “At step 920, process 900 transforms the subject data access at step 910 into a directed acyclic graph… Then, at step 930, process 900 generates a network graph based on the directed acyclic graph for the subject data that was created at step 920.”, Paragraph [0095], “To determine to perform the task, at step 940, process 900 may apply a trained weight set to the DMLP network graph generated at step 930.” Process 900 transforms subject data which may be a decision tree into a directed acyclic graph, which is used to generate a DMLP network graph which is a graph neural network. This process encodes the decision tree by generating the DMLP network graph.) and inputting a decision tree into a machine learning model ((Rainwater) Paragraph [0071], “FIG. 7 shows a flowchart representing training process 700 for training a machine learning system using DMLPs.”; Paragraph [0073], “At step 705, process 700 access training data sets having an input and an expected output. The input of the training data sets may be inherently non-sequential or conditional as opposed to sequential. As described herein, one example of inherently non-sequential data is source code. Other examples may include decision tree datasets for conducting troubleshooting, customer service, or performing some other task where performance of the task may rely on the presence or absence of conditions or categorization and analysis of collections of non-sequential documents such as websites.” Process 700 uses input data that may include a decision tree for a machine learning system which is a machine learning model.).
Rainwater and Dahlgren are in the same area of invention, that being the use of machine learning models and including the use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the encoding a decision tree by generating a graph neural network, as taught by Rainwater, and using the decision tree as input, as taught by Rainwater, into the question and answer submodule machine learning model creating questions using a dynamic decision tree as taught by Dahlgren in order to more accurately train the machine learning model in cases such as customer service. This application of a known technique to a known structure would result in a predictable result of an encoded decision tree input into a machine learning model to be used to generate questions.
Dahlgren, in view of Rainwater, does not explicitly disclose:
generating the machine learning model using the inputted natural language text and the generated question.
Fernandez Musoles teaches:
Generating a machine learning model using questions and natural language text. ((Fernandez Musoles) Paragraph [0105], “Some embodiments may generate a pre-trained machine learning model with training data having a set of training questions and set training documents from a corpus.” A set of training questions is equivalent to the generated question. Documents from a corpus are equivalent to the inputted natural language text.)
Fernandez Musoles is in the same area of invention as Dahlgren, that being the generating of questions and answers involving the use of machine learning models.
Thus, it would have been obvious to a person having ordinary skill in the art to have added the generating a machine learning model using questions and natural language text, as taught by Fernandez Musoles, as a final step in the method of generating a machine learning model as taught by Dahlgren in order to have a useful tool to retrieve meaningful information for a query under limiting conditions. This combination of combining generation of a machine learning model with the generation of specific inputs to the model does not change the functionality of either invention and would produce a predictable result.

Claim(s) 2, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dahlgren, in view of Rainwater, and Fernandez Musoles, and in further view of US 20210382607 A1 by Smith et al., hereafter Smith.

Regarding claim 2, Dahlgren, in view of Rainwater, and Fernandez Musoles, teaches the material disclosed in claim 1, and additionally Dahlgren teaches:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text. ((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question. The answers to the questions are collected by a data collection submodule 153b.” )
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node, and acquiring an answer to the first internal node question from the natural language text; and
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text. (((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question.” A set of questions implies a chain of questions that are each determined based on the answer and each have a step of acquiring an answer.)
Dahlgren, in view of Rainwater, and Fernandez Musoles, does not explicitly disclose, but together with Smith does teach:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text using the machine learning model; ((Smith) Paragraph [0015], “Digital analytics systems utilize machine learning and/or statistical modeling to process large amounts of data and generate predictions based on the processed data. These predictions can be generated in the form of natural language answers to natural language questions about representation of the data defined by user inputs.”)
Smith and Dahlgren are in the same area of invention, that being question and answer machine learning including using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the predicted answer acquisition, as taught by Smith, into the method as disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, as a response to the questions generated by the method in order to generate conclusions without having to wait for additional human input. This application of a known technique into a known process would produce the predictable result that is equivalent to the invention disclosed in claim 2.

Regarding claim 11, Dahlgren, in view of Rainwater, and Fernandez Musoles, teaches the material disclosed in claim 10, and additionally Dahlgren teaches:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text. ((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question. The answers to the questions are collected by a data collection submodule 153b.” )
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node, and acquiring an answer to the first internal node question from the natural language text; and
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text. (((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question.” A set of questions implies a chain of questions that are each determined based on the answer and each have a step of acquiring an answer.)
Dahlgren, in view of Rainwater, and Fernandez Musoles, does not explicitly disclose, but together with Smith does teach:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text using the machine learning model; ((Smith) Paragraph [0015], “Digital analytics systems utilize machine learning and/or statistical modeling to process large amounts of data and generate predictions based on the processed data. These predictions can be generated in the form of natural language answers to natural language questions about representation of the data defined by user inputs.”)
Smith and Dahlgren are in the same area of invention, that being question and answer machine learning including using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the predicted answer acquisition, as taught by Smith, into the method as disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, as a response to the questions generated by the method in order to generate conclusions without having to wait for additional human input. This application of a known technique into a known process would produce the predictable result that is equivalent to the invention disclosed in claim 11.

Regarding claim 20, Dahlgren, in view of Rainwater, and Fernandez Musoles, teaches the material disclosed in claim 20, and additionally Dahlgren teaches:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text. ((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question. The answers to the questions are collected by a data collection submodule 153b.” )
determining, based on the answer to the question corresponding to the root node, a first internal node question corresponding to a first internal node that is a node at a next depth of the root node, and acquiring an answer to the first internal node question from the natural language text; and
repeating steps of acquiring an answer to a question corresponding to a current node from the natural language text and of determining a question corresponding to a node at a next depth based on the answer, until an answer to a question corresponding to a node at a previous depth of a leaf node of the decision tree is acquired from the natural language text, or an answer to a current question fails to be acquired from the natural language text. (((Dahlgren) Paragraph [0053], “The first submodule 153a, includes a question and answer template, which creates a set of questions, either a complete set, or for a decision tree, where the answer to the previous question or questions causes dynamic generation of the next question.” A set of questions implies a chain of questions that are each determined based on the answer and each have a step of acquiring an answer.)
Dahlgren, in view of Rainwater, and Fernandez Musoles, does not explicitly disclose, but together with Smith does teach:
acquiring an answer to a question corresponding to the root node of the decision tree from the natural language text using the machine learning model; ((Smith) Paragraph [0015], “Digital analytics systems utilize machine learning and/or statistical modeling to process large amounts of data and generate predictions based on the processed data. These predictions can be generated in the form of natural language answers to natural language questions about representation of the data defined by user inputs.”)
Smith and Dahlgren are in the same area of invention, that being question and answer machine learning including using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the predicted answer acquisition, as taught by Smith, into the method as disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, as a response to the questions generated by the method in order to generate conclusions without having to wait for additional human input. This application of a known technique into a known process would produce the predictable result that is equivalent to the invention disclosed in claim 20.

Claim(s) 3, 8, 9, 12, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, in further view of US 20190057773 A1 by Li, hereafter Li.

Regarding claim 3, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teach the material disclosed in claim 2.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Li does teach:
constructing a decision chain using the answers acquired from the natural language text and the corresponding questions; ((Li) Paragraph [0080], “When the symptom information determined in all of the descriptive statements obtained from the dialogue box during the current dialogue process is “cough, no fever . . . ” the corresponding process of querying the decision tree for example includes a->d->. . . ” The process of querying the decision tree is constructing a decision chain. The symptom information is equivalent to the answers.)
comparing the decision chain with the decision tree; and
determining whether a path consistent with the decision chain exists in the decision tree. ((Li) Paragraph [0065], “At S23, the predefined class information is extracted and whether query condition is met is determined on the basis of this.” Extracting predefined class information is constructing the decision chain. A query condition is if the target being compared matches a specific part of a pre-constructed Determining whether a query condition is met based on the determination of predefined class information involves comparing the decision chain with the pre-constructed decision tree and determining whether they match.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the processes of constructing a decision chain, and comparing the decision chain with a decision tree to determine if it meets a query condition, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, by linking answers generated by the method and using that for comparison and determination in order to gain the benefit of being able to evaluate if the model is working so that one could decide if changes needed to be made. This use of a known process to improve the method disclosed in by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, would produce the predictable result that is equivalent to the invention disclosed in claim 3.

Regarding claim 8, Dahlgren, in view of Rainwater, and Fernandez Musoles, teach the material disclosed in claim 1.
Dahlgren, in view of Rainwater, and Fernandez Musoles, does not explicitly disclose, but together with Li does teach:
inputting a target natural language text to the machine learning model; and ((Li) Paragraph [0059], “At S21, a descriptive statement inputted by the user is obtained.” A descriptive statement is natural language)
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text. ((Li) Paragraph [0067], “At S24, query is performed based on the extracted predefined class information to obtain the triage information, and the triage information obtained from the query is displayed.” Triage information is equivalent to a scheme of a leaf node.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the process of inputting a natural language description and causing display of information, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, in order to have the benefit of human readability of the output so that a human might be able to make a judgment. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 8.

Regarding claim 9, Dahlgren, in view of Rainwater, Fernandez Musoles, and Li, teach the material disclosed in claim 8.
Li additionally teaches:
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text. ((Li) Paragraph [0070], “At S25, if it is determined that the descriptive statement does not include any predefined class information or does not meet the query conditions, an inquiry statement is generated and displayed, so that a next descriptive statement inputted by the user may be obtained.” Not meeting the query conditions is failing to find the scheme matching the target natural language text. An inquiry statement is an additional question required to find the scheme matching the target natural language text.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the process of displaying a request for additional input, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles and Li, in order to have the benefit of further narrowing the search area so one could find the target. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Li, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 9.

Regarding claim 12, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teach the material disclosed in claim 11.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Li does teach:
constructing a decision chain using the answers acquired from the natural language text and the corresponding questions; ((Li) Paragraph [0080], “When the symptom information determined in all of the descriptive statements obtained from the dialogue box during the current dialogue process is “cough, no fever . . . ” the corresponding process of querying the decision tree for example includes a->d->. . . ” The process of querying the decision tree is constructing a decision chain. The symptom information is equivalent to the answers.)
comparing the decision chain with the decision tree; and
determining whether a path consistent with the decision chain exists in the decision tree. ((Li) Paragraph [0065], “At S23, the predefined class information is extracted and whether query condition is met is determined on the basis of this.” Extracting predefined class information is constructing the decision chain. A query condition is if the target being compared matches a specific part of a pre-constructed Determining whether a query condition is met based on the determination of predefined class information involves comparing the decision chain with the pre-constructed decision tree and determining whether they match.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the processes of constructing a decision chain, and comparing the decision chain with a decision tree to determine if it meets a query condition, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, by linking answers generated by the method and using that for comparison and determination in order to gain the benefit of being able to evaluate if the model is working so that one could decide if changes needed to be made. This use of a known process to improve the method disclosed in by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, would produce the predictable result that is equivalent to the invention disclosed in claim 12.

Regarding claim 17, Dahlgren, in view of Rainwater, and Fernandez Musoles, teach the material disclosed in claim 10.
Dahlgren, in view of Rainwater, and Fernandez Musoles, does not explicitly disclose, but together with Li does teach:
inputting a target natural language text to the machine learning model; and ((Li) Paragraph [0059], “At S21, a descriptive statement inputted by the user is obtained.” A descriptive statement is natural language)
causing the machine learning model to output a scheme of a leaf node in the decision tree that matches the target natural language text. ((Li) Paragraph [0067], “At S24, query is performed based on the extracted predefined class information to obtain the triage information, and the triage information obtained from the query is displayed.” Triage information is equivalent to a scheme of a leaf node.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the process of inputting a natural language description and causing display of information, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, in order to have the benefit of human readability of the output so that a human might be able to make a judgment. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, and Fernandez Musoles, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 17.

Regarding claim 18, Dahlgren, in view of Rainwater, Fernandez Musoles, and Li, teach the material disclosed in claim 17.
Li additionally teaches:
outputting, in the case where the machine learning model fails to find the scheme matching the target natural language text, an additional question required to find the scheme matching the target natural language text. ((Li) Paragraph [0070], “At S25, if it is determined that the descriptive statement does not include any predefined class information or does not meet the query conditions, an inquiry statement is generated and displayed, so that a next descriptive statement inputted by the user may be obtained.” Not meeting the query conditions is failing to find the scheme matching the target natural language text. An inquiry statement is an additional question required to find the scheme matching the target natural language text.)
Li and Dahlgren are in the same area of invention, that being use of decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the process of displaying a request for additional input, as taught by Li, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles and Li, in order to have the benefit of further narrowing the search area so one could find the target. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Li, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 18.

Claim(s) 4, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, and in further view of Optimization Problems for Machine Learning: A Survey by Gambella et al., hereafter Gambella.

Regarding claim 4, Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, teaches the material disclosed in claim 3.
Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, does not explicitly disclose, but together with Gambella does teach:
adjusting, in the case where no path consistent with the decision chain exists in the decision tree, the machine learning model using a loss function so as to minimize a path deviation between the decision tree and the decision chain. ((Gambella) Page 9 Section 3.3 Decision Trees under equation 38, “The objective function (22) minimizes the normalized total misclassification loss … The splitting of the data points at each of the branch nodes is enforced by constraints (31)(32)…” The splitting of the data points at each of the branch nodes is adjusting the machine learning model. The objective function that minimizes misclassification loss is equivalent to a loss function that minimizes a path deviation between the decision tree and the decision chain.)
Gambella and Dahlgren are in the same area of invention, that being machine learning including decision trees:
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented adjusting the machine learning model by splitting data points at nodes based on an objective function, as taught by Gambella, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, in order to minimize path error. This use of a known technique to allow adjustment of the machine learning model in the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, Smith and Li, would produce the predictable result that is equivalent to the invention disclosed in claim 4.

Regarding claim 13, Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, teaches the material disclosed in claim 12.
Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, does not explicitly disclose, but together with Gambella does teach:
adjusting, in the case where no path consistent with the decision chain exists in the decision tree, the machine learning model using a loss function so as to minimize a path deviation between the decision tree and the decision chain. ((Gambella) Page 9 Section 3.3 Decision Trees under equation 38, “The objective function (22) minimizes the normalized total misclassification loss … The splitting of the data points at each of the branch nodes is enforced by constraints (31)(32)…” The splitting of the data points at each of the branch nodes is adjusting the machine learning model. The objective function that minimizes misclassification loss is equivalent to a loss function that minimizes a path deviation between the decision tree and the decision chain.)
Gambella and Dahlgren are in the same area of invention, that being machine learning including decision trees:
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented adjusting the machine learning model by splitting data points at nodes based on an objective function, as taught by Gambella, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, Smith, and Li, in order to minimize path error. This use of a known technique to allow adjustment of the machine learning model in the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, Smith and Li, would produce the predictable result that is equivalent to the invention disclosed in claim 13.

Claim(s) 5, 7, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, and in further view of US 20220230181 A1 by Gupta et al., hereafter Gupta.

Regarding claim 5, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teaches the material disclosed in claim 2.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Gupta does teach:
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and ((Gupta) Paragraph [111], “In the depicted embodiment, output from the guidance for the first recommended action 570 includes a return code (e.g., return code: 400) and a test status (e.g., failed)” The guidance for the first recommended action is scheme matching the natural language text. A test status that displays failed is a statement indicating that the guidance (scheme) failed to be found.)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node. ((Gupta) Paragraph [0109], “Upon the agent selecting one of the cases, the processor 82 may provide the corresponding guidance for a selected recommended action.” The guidance is a scheme of the leaf node.)
Gupta and Dahlgren are in the same area of invention, that being machine learning using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the outputting of error and outputting of successful end result, as disclosed in Gupta, with the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, in order to allow a user to view the results. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 5.

Regarding claim 7, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teaches the material disclosed in claim 2.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Gupta does teach:
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions. ((Gupta) Paragraph [0113], “The completed guidance 790 may provide the agent a view of the questions and answers, tests and results, customer information and answers, and so forth, for each guidance until receiving the resolution (e.g., work order 764).”)
Gupta and Dahlgren are in the same area of invention, that being machine learning using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the view of answers after completion, as taught by Gupta, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, in order to allow a user to view the results. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 7.

Regarding claim 14, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teaches the material disclosed in claim 11.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Gupta does teach:
outputting, in the case where the answer to the current question fails to be acquired from the natural language text, a statement indicating that a scheme matching the natural language text fails to be found; and ((Gupta) Paragraph [111], “In the depicted embodiment, output from the guidance for the first recommended action 570 includes a return code (e.g., return code: 400) and a test status (e.g., failed)” The guidance for the first recommended action is scheme matching the natural language text. A test status that displays failed is a statement indicating that the guidance (scheme) failed to be found.)
outputting, in response to the answer to the question corresponding to the node at the previous depth of the leaf node being acquired from the natural language text, a scheme of the leaf node in the decision tree according to the answer to the question corresponding to the node at the previous depth of the leaf node. ((Gupta) Paragraph [0109], “Upon the agent selecting one of the cases, the processor 82 may provide the corresponding guidance for a selected recommended action.” The guidance is a scheme of the leaf node.)
Gupta and Dahlgren are in the same area of invention, that being machine learning using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the outputting of error and outputting of successful end result, as disclosed in Gupta, with the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, in order to allow a user to view the results. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 14.

Regarding claim 16, Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, teaches the material disclosed in claim 11.
Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not explicitly disclose, but together with Gupta does teach:
outputting, after completion of the repeated steps, the answers acquired using the natural language text and the corresponding questions. ((Gupta) Paragraph [0113], “The completed guidance 790 may provide the agent a view of the questions and answers, tests and results, customer information and answers, and so forth, for each guidance until receiving the resolution (e.g., work order 764).”)
Gupta and Dahlgren are in the same area of invention, that being machine learning using decision trees.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the view of answers after completion, as taught by Gupta, into the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, in order to allow a user to view the results. This simple addition of a known element to improve the method disclosed by Dahlgren, in view of Rainwater, Fernandez Musoles, and Smith, does not change the functionality of either element and would produce the predictable result that is equivalent to the invention disclosed in claim 16.

Claim(s) 6, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dahlgren in view of Rainwater, and Fernandez Musoles, and in further view of US 20240411968 A1 by Sarar et al., hereafter Sarar.

Regarding claim 6, Dahlgren, in view of Rainwater, and Fernandez Musoles, teaches the material disclosed in claim 1.
Dahlgren does not explicitly disclose:
converting the decision tree into a directional acyclic graph form; and
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
Rainwater teaches:
converting the decision tree into a directional acyclic graph form; ((Rainwater) Paragraph [0093], “For example, subject data may be source code to be analyzed or maybe a decision tree reflecting a decision process over business domain or troubleshooting domain.”; Paragraph [0094], “At step 920, process 900 transforms the subject data access at step 910 into a directed acyclic graph” Directional acyclic graph and directed acyclic graph are being assumed to be the same type of graph.)
Rainwater and Dahlgren are in the same area of invention, that being the use of use of graph structures for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the conversion of the decision tree as taught by Dahlgren into a directional acyclic graph, as taught by Rainwater, in order to reflect a decision process over troubleshooting domain. This application of a known technique to a known structure would produce the predictable result of a directional acyclic graph converted from a decision tree.
Dahlgren, in view of Fernandez Musoles and Rainwater, still does not explicitly disclose:
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
Sarar teaches:
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network. ((Sarar) Paragraph [0070], “As illustrated, the input DAG 305 and/or the prediction-related features 402 are processed by a graph model 405 (e.g., a GNN comprising one or more layers) to generate a set of node embeddings 410 (e.g., an embedding for each node in the DAG 305)” A DAG is a directional acyclic graph. A GNN is a graph neural network.)
Sarar, Rainwater, and Dahlgren are in the same area of invention, that the use graphical structures in computer modeling.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the processing of a directed acyclic graph, as would result in the method taught in Dahlgren, in view of Fernandez Musoles, and Rainwater, to generate node embeddings of a machine learning model using a graph neural network, as taught by Sarar, in order to more efficiently fit machine learning model input. This application of a known technique to a known structure would produce the invention claimed in claim 6.

Regarding claim 15, Dahlgren, in view of Rainwater, and Fernandez Musoles, teaches the material disclosed in claim 10.
Dahlgren does not explicitly disclose:
converting the decision tree into a directional acyclic graph form; and
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
Rainwater teaches:
converting the decision tree into a directional acyclic graph form; ((Rainwater) Paragraph [0093], “For example, subject data may be source code to be analyzed or maybe a decision tree reflecting a decision process over business domain or troubleshooting domain.”; Paragraph [0094], “At step 920, process 900 transforms the subject data access at step 910 into a directed acyclic graph” Directional acyclic graph and directed acyclic graph are being assumed to be the same type of graph.)
Rainwater and Dahlgren are in the same area of invention, that being the use of use of graph structures for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art to have implemented the conversion of the decision tree as taught by Dahlgren into a directional acyclic graph, as taught by Rainwater, in order to reflect a decision process over troubleshooting domain. This application of a known technique to a known structure would produce the predictable result of a directional acyclic graph converted from a decision tree.
Dahlgren, in view of Fernandez Musoles and Rainwater, still does not explicitly disclose:
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network.
Sarar teaches:
encoding the directional acyclic graph form into an embedding of the machine learning model using the graph neural network. ((Sarar) Paragraph [0070], “As illustrated, the input DAG 305 and/or the prediction-related features 402 are processed by a graph model 405 (e.g., a GNN comprising one or more layers) to generate a set of node embeddings 410 (e.g., an embedding for each node in the DAG 305)” A DAG is a directional acyclic graph. A GNN is a graph neural network.)
Sarar, Rainwater, and Dahlgren are in the same area of invention, that the use graphical structures in computer modeling.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the processing of a directed acyclic graph, as would result in the method taught in Dahlgren, in view of Fernandez Musoles, and Rainwater, to generate node embeddings of a machine learning model using a graph neural network, as taught by Sarar, in order to more efficiently fit machine learning model input. This application of a known technique to a known structure would produce the invention claimed in claim 15.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to decision trees, question-and-answering systems, conversion from decision trees to directed acyclic graphs, and conversion from directed acyclic graphs to graph neural networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN H LAI whose telephone number is [phone]. The examiner can normally be reached Monday - Friday 7:30am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 5712524241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.

DYLAN H. LAI
Examiner
Art Unit 2144

/TAMARA T KYLE/ Supervisory Patent Examiner, Art Unit 2144

Prosecution Timeline

• Oct 11, 2023: Application Filed
• Jun 08, 2026: Non-Final Rejection mailed — §101, §103, §112 (current)