DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1-20 are subject to review.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/17/2023 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 8-11, 16 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 4 recites the limitation "the analysis component". There is insufficient antecedent basis for this limitation in the claim.
Claims 8-11, 16, and 17 are rejected for similar reasons.
Claim 4 recites the limitation "the shared DAG weight matrix". There is insufficient antecedent basis for this limitation in the claim.
Claim 17 is rejected for similar reasons.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-11 are directed to a “system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claims 12-18 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 19-20 are directed to a “computer program product” which describes one of the four statutory categories of patentable subject matter, i.e., a manufacture.
Regarding Claim 1:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
As per Claim 1, the claim recites limitations of:
“a decomposition component that acts on the collected datasets to perform structured decomposition on multiple directed acyclic graphs (DAG) into a shared DAG with private DAGs for each environment or domain to generalize DAG learning.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as the process of “structured decomposition” on a graph is considered to be an algorithm, and as the process is performed on a graph, “structural decomposition” is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising:” – is directed to merely applying an abstract idea using a generic computer as a tool (See MPEP 2106.05(f)(2), 2106.04(d)).
“a data gathering component that collects datasets from a plurality of different environments or domains; and” – is merely a recitation of an insignificant extra-solution data gathering (See MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising:” – is directed to merely applying an abstract idea using a generic computer as a tool (See MPEP 2106.05(f)(2), 2106.04(d)).
“a data gathering component that collects datasets from a plurality of different environments or domains; and” – the broadest reasonable interpretation of this limitation is found to be merely gathering data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 2 recites an abstract idea, substantially as follows:
“wherein the system further comprises an analysis component that performs local task adaptation by using classical gradient descent over domain-specific data.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as the use of “classical gradient descent” is considered an algorithm, and therefore is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 2 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 3:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 3 recites an abstract idea, substantially as follows:
“wherein the analysis component updates the shared DAG by combining private DAG weights learned from lower-level processes.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing an arithmetic operation, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 4:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 4 recites an abstract idea, substantially as follows:
“wherein the analysis component minimizes a score function and applies an augmented Lagrangian method to enforce DAG structure on the shared DAG weight matrix.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing functions and algorithms, which are considered to be mathematical calculations.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 5:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 5 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the shared DAG is learned using validation data samples and the private DAGs are learned from domain-specific data samples.” – is merely indicating a field of use or technological environment directed towards the technology of directed acyclic graphs (See MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the shared DAG is learned using validation data samples and the private DAGs are learned from domain-specific data samples.” – is merely indicating a field of use or technological environment directed towards the technology of directed acyclic graphs (See MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 6:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 6 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the shared DAG characterizes causal relationships among factors across a subset of the environments and domains and, wherein the private DAGs quantify corresponding strengths of the factors.” – is merely indicating a field of use or technological environment directed towards the technology of directed acyclic graphs (See MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the shared DAG characterizes causal relationships among factors across a subset of the environments and domains and, wherein the private DAGs quantify corresponding strengths of the factors.” – is merely indicating a field of use or technological environment directed towards the technology of directed acyclic graphs (See MPEP 2106.05(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 7:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 7 recites an abstract idea, substantially as follows:
“The system of claim 3, wherein the analysis component computes gradients of loss functions and selects corresponding step sizes to formalize discovery of a shared DAG weight matrix and the private DAGs’ weight matrices.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing computation of a function, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 8:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“wherein the analysis component uses a loss function with a validation dataset and a loss function with a training dataset to rectify discrepancies between a subset of the datasets.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing computation of a function, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 9:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 9 recites an abstract idea, substantially as follows:
“wherein the analysis component imposes a DAG constraint to enable automatic retrieval of the DAGs.”– is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing a constraint, which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 10:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 10 recites an abstract idea, substantially as follows:
“wherein the analysis component employs a self-supervised structural equation model to enable continuous optimization.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing an equation model, which is considered to be an equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 11:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“wherein the analysis component guarantees finding Karush-Kuhn-Tucker (KKT) points with theoretical convergence rates.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing mathematical conditions, which is considered to be a mathematical equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 12:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 12 recites an abstract idea, substantially as follows:
As per Claim 12, the claim recites limitations of:
“acting on the collected datasets to perform structured decomposition on multiple directed acyclic graphs (DAG) into a shared DAG with private DAGs for each environment or domain to generalize DAG learning.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as the process of “structured decomposition” on a graph is considered to be an algorithm, and as the process is performed on a graph, “structural decomposition” is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“collecting datasets from a plurality of different environments or domains; and” – is merely a recitation of an insignificant extra-solution data gathering (See MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“collecting datasets from a plurality of different environments or domains; and” – the broadest reasonable interpretation of this limitation is found to be merely gathering data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 13:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 13 recites an abstract idea, substantially as follows:
“further comprising engaging an analysis component to perform local task adaptation by using classical gradient descent over domain-specific data.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as the use of “classical gradient descent” is considered an algorithm, and therefore is considered to be a mathematical calculation
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception
Regarding Claim 14:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 14 recites an abstract idea, substantially as follows:
“further comprising engaging the analysis component to update the shared DAG by combining private DAG weights learned from lower-level processes.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing an arithmetic operation, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 15:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 15 recites an abstract idea, substantially as follows:
“further comprising imposing a DAG constraint to enable automatic retrieval of the DAGs.”– is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing a constraint, which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 16:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 16 recites an abstract idea, substantially as follows:
“further comprising engaging the analysis component to guarantee finding Karush-Kuhn-Tucker (KKT) points with theoretical convergence rates.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing mathematical conditions, which is considered to be a mathematical equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 17:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 17 recites an abstract idea, substantially as follows:
“further comprising engaging the analysis component to minimize a score function and apply an augmented Lagrangian method to enforce DAG structure on the shared DAG weight matrix.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing functions and algorithms, which are considered to be mathematical calculations.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 18:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 18 recites an abstract idea, substantially as follows:
“further comprising employing a self-supervised structural equation model to enable continuous optimization.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as it is describing an equation model, which is considered to be an equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 19:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 19 recites an abstract idea, substantially as follows:
“use a decomposition component that acts on the collected datasets to perform structured decomposition on multiple directed acyclic graphs (DAG) into a shared DAG with private DAGs for each environment or domain to generalize DAG learning.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as the process of “structured decomposition” on a graph is considered to be an algorithm, and as the process is performed on a graph, “structural decomposition” is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“use a data gathering component that collects datasets from a plurality of different environments or domains; and” – is merely a recitation of an insignificant extra-solution data gathering (See MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“use a data gathering component that collects datasets from a plurality of different environments or domains; and” – the broadest reasonable interpretation of this limitation is found to be merely gathering data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP 2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 20:
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 20 recites an abstract idea, substantially as follows:
“wherein the program instructions are further executable to cause the processor to: engage an analysis component to perform local task adaptation by using classical gradient descent over domain-specific data.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04((2)) as the use of “classical gradient descent” is considered an algorithm, and therefore is considered to be a mathematical calculation
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, 12 and 19 are rejected under 35 U.S.C 102(a)(1) and (a)(2) as being anticipated by Miyashita, Hisashi (US 20120019535 A1) (hereinafter referred to as “Miyashita”).
Regarding Claim 1,
Miyashita recites:
“a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising:” (Miyashita at [0062] In FIG. 5, a CPU 504, a main memory (RAM) 506, a hard disk drive (HDD) 508, a keyboard 510, a mouse 512 and a display 514 are connected onto a system bus 502.)
“a data gathering component that collects datasets from a plurality of different environments or domains; and” (Miyashita at abstract, importing data from the first domain DAG and data from the second domain DAG; Miyashita at [0059], FIG. 4, there may be more domains or any number of domains.)
“a decomposition component that acts on the collected datasets to perform structured decomposition on multiple directed acyclic graphs (DAG) into a shared DAG with private DAGs for each environment or domain to generalize DAG learning.” (Miyashita at [0026] Since the association graph [Examiner note: mapped to shared DAG], which is the DAG generated as a result of the process described above, is an abstraction of the structure [Examiner note: mapped to structured decomposition] of data of each domain, one can understand the meaning of an association without referring to data of each domain.; Miyashita at [0071] A graph generation module 606 generates a directed acyclic graph (DAG) data 608 for each domain [Examiner note: mapped to private DAGs for each environment or domain] from domain data)
As per Claim 12, this is a method claim corresponding to Claim 1, and is rejected for similar reasons.
As per Claim 19, this is a computer readable medium claim corresponding to Claim 1, and is rejected for similar reasons.
Regarding Claim 5
Miyashita recites:
“wherein the shared DAG is learned using validation data samples and the private DAGs are learned from domain-specific data samples.” (Miyashita at [0073] An association graph generation module 614 refers to DAG data 608 and association data [Examiner Note: mapped to validation data samples] to generate association graph [Examiner Note: mapped to shared DAG]; Miyashita at [0078] the function of the communication module 602 to import data 2 managed with external tool 2 of domain 2 as domain data [Examiner Note: mapped to domain-specific data samples] 604.; Miyashita at [0016] The imported data reflects hierarchical relationship of the original data to form a DAG for each domain [Examiner Note: mapped to private DAGs].)
[Examiner Note: validation data samples is mapped to data used to generate the shared DAG]
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. 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) 2, 13, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Miyashita in view of Larlus, Diane (US 20220172048 A1) (hereinafter referred to as “Larlus”)
Regarding Claim 2
Miyashita recites the “The system of claim 1”, and the limitations are shown in the rejection above.
Miyashita does not disclose:
“wherein the system further comprises an analysis component that performs local task adaptation by using classical gradient descent over domain-specific data.”
On the other hand, Larlus recites:
“wherein the system further comprises an analysis component that performs local task adaptation by using classical gradient descent over domain-specific data.” (Larlus at [0035] A two-fold regularizer can be provided that, on the one hand, encourages models to remember previously encountered domains when exposed to new ones (e.g., by means of optimization updates, such as gradient descent updates, on these tasks), and on the other hand, encourages an efficient adaptation to such domains. [Examiner Note: mapped to using a gradient descent method to perform local task adaption)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Miyashita with the above teachings of Larlus by using a technique that generalizes DAG learning by decomposing multiple DAGs into domain specific DAGs and a shared DAG, as taught by Miyashita, and adapting tasks to domains using gradient descent, as taught by Larlus. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve adaption to domains in meta learning as suggested by Larlus at [0029 - 0030]: “Example meta- learning methods provided herein are based on the concept of "auxiliary domains. Example training methods may include designing effective auxiliary domains (auxiliary datasets) that significantly improve adaptation to more diverse domains.”
As per Claim 13, this is a method claim corresponding to Claim 2, and is rejected for similar reasons.
As per Claim 20, this is a computer readable medium claim corresponding to Claim 2, and is rejected for similar reasons.
Claim(s) 3, 4, 7, 14 and 17 are rejected under 35 U.S.C 103 as being unpatentable over Miyashita in view of Larlus and in further view of NPL reference Gao, Erdun “FedDAG: Federated DAG Structure Learning” (hereinafter referred to as “Gao”).
Regarding Claim 3
Miyashita in view of Larlus recites the “The system of claim 2,” and the limitations are shown in the rejection above. Miyashita in view of Larlus further recites:
“wherein the analysis component updates the shared DAG by combining private DAG weights learned from lower-level processes.” [Examiner Note: Miyashita teaches a shared DAG and private DAGs, but not the rest of the limitation] (Miyashita at [0026] Since the association graph [Examiner note: mapped to shared DAG], which is the DAG generated as a result of the process described above, is an abstraction of the structure of data of each domain, one can understand the meaning of an association without referring to data of each domain.; Miyashita at [0071] A graph generation module 606 generates a directed acyclic graph (DAG) data 608 for each domain [Examiner note: mapped to private DAGs for each environment or domain] from domain data)
Miyashita in view of Larlus fails to disclose:
“wherein the analysis component updates the shared DAG by combining private DAG weights learned from lower-level processes.”
On the other hand, Gao recites:
“wherein the analysis component updates the shared DAG by combining private DAG weights learned from lower-level processes.” (Gao at Pg.7, 4.2 Federated DAG structure Learning, last paragraph: "After itfl local updates [mapped to lower-level processes], the server randomly chooses r clients [mapped to domains ie. private] to collect their Us [mapped to private weights] to the
set U. Then, Us in U are averaged [mapped to combining] to get Unew [mapped to updating a shared structure]. ")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Miyashita and Larlus with the above teachings of
Gao by using a learning method with a shared DAG and private DAGs, as taught by Miyashita and Larlus, and updating a shared structure using weights derived from private structures as taught by Gao. The modification would have been obvious because one of ordinary skill in the art would be motivated to optimize learning techniques as suggested by Gao on Pg. 1, Abstract: “FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency.”
As per Claim 14, this is a method claim corresponding to Claim 3, and is rejected for similar reasons.
Regarding Claim 4
Miyashita in view of Larlus recites the “The system of claim 2,” and the limitations are shown in the rejection above.
However, Miyashita in view of Larlus does not disclose:
“wherein the analysis component minimizes a score function and applies an augmented Lagrangian method to enforce DAG structure on the shared DAG weight matrix.”
On the other hand, Gao recites:
“wherein the analysis component minimizes a score function and applies an augmented Lagrangian method to enforce DAG structure on the shared DAG weight matrix.” (Gao at Page 5, Section 4. Methodology: “To solve the federated DAG structure learning problem, we formulate a continuous score-based method [Examiner Note: mapped to score function] named FedDAG to learn the DAG structure [Examiner Note: mapped to enforce DAG structure] from decentralized data … The GSL part is parameterized by a matrix Uck ∈ Rd×d, [Examiner Note: mapped for shared weight matrix] which would be the same for all clients finally”)
(Page 5, under Section 4. Methodology) [Examiner Note: the prior art Gao involve complex equations, as such screenshots of the prior are used to show teaching of the claims]
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(Gao at Page 7, Section 4.2 Federated DAG structure learning: “the hard-constraint optimization problem in Eq. (6) [Examiner Note: see screenshot above of cited equation] can be addressed by an Augmented Lagrangian Method (ALM) to get an approximate solution.”)
The same motivation that was utilized for combining Miyashita with Larlus and Gao, as set forth in Claim 3, is equally applicable to Claim 4.
As per Claim 17, this is a method claim corresponding to Claim 4, and is rejected for similar reasons.
Regarding Claim 7
Miyashita in view of Larlus recites the “The system of claim 3,” and the limitations are shown in the rejection above. Miyashita in view of Larlus further recites:
“wherein the analysis component computes gradients of loss functions and selects corresponding step sizes to formalize discovery of a shared DAG weight matrix and the private DAGs’ weight matrices.” [Examiner Note: Miyashita teaches a shared DAG and private DAGs, but not the rest of the limitation] (Miyashita at [0026] Since the association graph [Examiner note: mapped to shared DAG], which is the DAG generated as a result of the process described above, is an abstraction of the structure of data of each domain, one can understand the meaning of an association without referring to data of each domain.; Miyashita at [0071] A graph generation module 606 generates a directed acyclic graph (DAG) data 608 for each domain [Examiner note: mapped to private DAGs for each environment or domain] from domain data)
Larlus further recites:
“wherein the analysis component computes gradients of loss functions and selects corresponding step sizes to formalize discovery of a shared DAG weight matrix and the private DAGs’ weight matrices.” (Larlus at [0015] A gradient descent step may be proportional to a gradient (or approximate gradient) of a loss function at a current point.)
Miyashita in view of Larlus fails to disclose:
“wherein the analysis component computes gradients of loss functions and selects corresponding step sizes to formalize discovery of a shared DAG weight matrix and the private DAGs’ weight matrices.”
On the other hand, Gao recites:
“wherein the analysis component computes gradients of loss functions and selects corresponding step sizes to formalize discovery of a shared DAG weight matrix and the private DAGs’ weight matrices.” [Examiner Note: the prior art Gao involve complex equations, as such screenshots of the prior are used to show teaching of the claims]
(Gao on Page 32, Section E: More discussions on the experimental results: “For simplicity , we mark all parameters (actually Φ and U [Examiner Note: mapped to weight matrix]) of client ck together as
θ
c
k
)
(Gao on Page 32, Section E: More discussions on the experimental results)
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[Examiner Note:
θ
c
k
includes the weight matrix U, therefore updating the parameters using lr, the learning rate, is mapped to selecting corresponding step sizes to formalize discovery of weight matrices]
The same motivation that was utilized for combining Miyashita with Larlus and Gao, as set forth in Claim 3, is equally applicable to Claim 7.
Claim 8 is rejected under 35 U.S.C 103 as being unpatentable over Miyashita in view of Hall, Jonathan (20230148321 A1) (hereinafter referred to as “Hall”).
Regarding Claim 8
Miyashita recites “The system of claim 1” and the limitations are shown in the rejection above.
However, Miyashita fails to disclose:
“wherein the analysis component uses a loss function with a validation dataset and a loss function with a training dataset to rectify discrepancies between a subset of the datasets.”
On the other hand, Hall recites:
“wherein the analysis component uses a loss function with a validation dataset and a loss function with a training dataset to rectify discrepancies between a subset of the datasets.” (Hall at [0158] Graphical information regarding the training process which has taken place, such as plots describing the loss as a function of epoch (for both the training set and the validation set), is instructive for determining whether the model a) has systematically improved its loss over a range of epochs and thus learned information, b) has converged to a steady state, and c) has not overtrained (i.e., the validation loss deteriorates while the training loss continues to improve) [Examiner Note: mapped to discrepancies between a subset of the datasets].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Miyashita with the above teachings of Hall by using a technique that generalizes DAG learning by decomposing multiple DAGs into domain specific DAGs and a shared DAG, as taught by Miyashita, and a method that uses a loss function on validation and training datasets, as taught by Hall. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve model generalizability as suggested by Hall in Abstract: “Computational methods and systems for training Artificial Intelligence (AI) models with improved translatability or generalisability (robustness) comprises training a plurality of Artificial Intelligence (AI) models using a common validation dataset over a plurality of epochs.”
Claim(s) 6, 9, 10, 15 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Miyashita in view of NPL reference Ge, Yunhao “Invariant Structure Learning for Better Generalization and Causal Explainability” (herein referred to as “Ge”).
Regarding Claim 6
Miyashita recites “The system of claim 1” and the limitations are shown in the rejection above. Miyashita further recites:
“wherein the shared DAG characterizes causal relationships among factors across a subset of the environments and domains and,” (Miyashita at [0073] generate association graph [Examiner Note: mapped to shared DAG] data 616 representing association [Examiner Note: mapped to relationships] between two domains; Miyashita at [0073] According to the present invention, the association graph data 616 is also a DAG.)
However, Miyashita fails to disclose:
“wherein the shared DAG characterizes causal relationships among factors across a subset of the environments and domains and,” [Examiner Note: Miyashita does not teach causal relationships]
“wherein the private DAGs quantify corresponding strengths of the factors.” [Examiner Note: Miyashita does not teach strengths of factors]
On the other hand, Ge discloses,
“wherein the shared DAG characterizes causal relationships among factors across a subset of the environments and domains and,” (Ge at Page 2, paragraph before bullet points: “Specifically, ISL uses generalization accuracy as a constraint to learn the invariant structure (as a DAG) that represents
the causal relationship among variables”.)
“wherein the private DAGs quantify corresponding strengths of the factors.” [Examiner Note: the prior art Ge involves complex equations, as such screenshots of the prior are used to show teaching of the claims]
(Page 5, paragraph under Eq. (2))
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[Examiner Note: the adjacency matrix is mapped to a DAG, and the connection strength is mapped to strengths of factors]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Miyashita with the above teachings of Ge by using a technique utilizing a shared DAG to characterize relationships across domains, as taught by Miyashita, and using a method that DAGs to represent causal relationships, as taught by Ge. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve causal graph discovery on complex graphs as suggested by Ge at Page 2, last bullet before Related Works “ISL yields state-of-the-art causal graph discovery (clearly outperforming alternatives on real-world data) with a particularly prominent improvement for complex graphs structures.”
Regarding Claim 9
Miyashita recites “The system of claim 1” and the limitations are shown in the rejection above.
Miyashita fails to disclose:
“wherein the analysis component imposes a DAG constraint to enable automatic retrieval of the DAGs.”
On the other hand, Ge discloses:
“wherein the analysis component imposes a DAG constraint to enable automatic retrieval of the DAGs.” (Ge on Page 2, paragraph above 2 Related Works: "We generalize ISL to self-supervised causal structure learning, which first treats the discovered invariant correlations as potential causal edges, and then uses a DAG constraint to finalize the causal structure”) [Examiner Note: mapped to enable automatic retrieval of the DAGs.]
The same motivation that was utilized for combining Miyashita with Ge, as set forth in Claim 6, is equally applicable to Claim 9.
As per Claim 15, this is a method claim corresponding to Claim 9, and is rejected for similar reasons.
Regarding Claim 10
Miyashita recites “The system of claim 1” and the limitations are shown in the rejection above.
However, Miyashita fails to disclose:
“wherein the analysis component employs a self-supervised structural equation model to enable continuous optimization.”
On the other hand, Ge recites:
“wherein the analysis component employs a self-supervised structural equation model to enable continuous optimization.” (Ge on Page 2, second bullet point above 2 Related Works: "We generalize ISL to self-supervised causal structure learning, which first treats the discovered invariant correlations as potential causal edges, and then uses a DAG constraint to finalize the causal structure”; Ge on Page 2, paragraph above bullet points: "Specifically, ISL uses generalization accuracy as a constraint to learn the invariant structure (as a DAG) that represents the causal relationship among variables. Take Fig. 1 as an example, where we simplify the object recognition task by using variables to represent the key factors: X1: object shape, X2: object texture (including color), X3: image background (as context), with the output label Y . Fig. 1 (a) shows the ground truth (GT) Structural Causal Model (SCM). [Examiner Note: mapped to structural equation model] ") [Examiner Note: ISL implements self-supervised learning in order to finalize causal structure using structural causal models, therefore is mapped to a self-supervised structural equation model].
The same motivation that was utilized for combining Miyashita with Ge, as set forth in Claim 6, is equally applicable to Claim 10.
As per Claim 18, this is a method claim corresponding to Claim 10, and is rejected for similar reasons.
Claim(s) 11 and 16 are rejected under 35 U.S.C 103 as being unpatentable over Miyashita in view of NPL reference Zhou, Pan “Efficient Meta Learning via Minibatch Proximal Update), hereinafter referred to as “Zhou”.
Regarding Claim 11:
Miyashita recites “The system of claim 1” and the limitations are shown in the rejection above.
Miyashita fails to disclose:
“wherein the analysis component guarantees finding Karush-Kuhn-Tucker (KKT) points with theoretical convergence rates.”
On the other hand, Zhou discloses:
“wherein the analysis component guarantees finding Karush-Kuhn-Tucker (KKT) points with theoretical convergence rates.” [Examiner Note: the prior art Zhou involve complex equations, as such screenshots of the prior are used to show teaching of the claims]
(Zhou at Page 5, second paragraph)
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(Zhou at Page 5, last paragraph)
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Miyashita with the above teaching of Zhou by using a technique that generalizes DAG learning by decomposing multiple DAGs into domain specific DAGs and a shared DAG, and using Karush-Kuhn-Tucker points to ensure convergence as taught by Zhou. The modification would have been obvious because one of ordinary skill in the art would be motivated to optimize meta learning as suggested by Zhou at Page 2, Paragraph 2, “we develop an SGD based meta-optimization algorithm for efficient meta-learning via minibatch proximal update, with provable guarantees established for a broader range of convex and non-convex learning problems”
As per Claim 16, this is a method claim corresponding to Claim 11, and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240419995 A1- recites DAGs used to represent causal relationships.
NPL reference Zhun et al. “DAGs with NO TEARS: Continuous Optimization for Structure Learning” - recites score-based learning to estimate the structure of DAGs.
NPL reference Yu et al. “DAGs with No Curl: An Efficient DAG Structure Learning Approach” – recites weighted adjacency matrices of DAGs.
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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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