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 . Claims 1-20 are pending.
Claim Objections
Claim 20 is objected to because of the following informalities.
Claim 20, though indicated as being dependent on claim 1, recites a “medium” in the preamble, which is the statutory class of claim 19. Examiner notes that it’s readily apparent that claim 20 is supposed to depend on claim 19, which is the interpretation applied by examiner below (i.e., claim 20 depends on claim 19).
This is not a matter of indefiniteness, given that the metes and bounds are clear. Still, appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-9 are to a method (i.e., a process), claims 10-18 are to a method (i.e., a process), and claims 19-20 are to a non-transitory computer-readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
(As noted above, claim 20, though indicated as dependent on clam 1, appears to depend on claim 19. Examiner has applied that interpretation below.)
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 10 is (claim 1 being representative):
translating information from a causal network into a causal knowledge graph according to a mapping, the causal knowledge graph comprising a plurality of causal links, wherein each of the causal links includes a cause entity, a causal relation, an effect entity, and a causal weight indicating a relative strength of causal influence of the cause entity on the effect entity;
converting the causal knowledge graph into embeddings, the embeddings comprising a latent vector space representation of the causal knowledge graph;
training the embeddings using a subset of the causal links of the causal knowledge graph; and
using the embeddings for causal discovery to predict additional causal links of the causal knowledge graph.
The abstract idea of claim 19 is:
translate information from a causal network into a causal knowledge graph according to a mapping, the causal knowledge graph comprising a plurality of causal links, wherein each causal link includes a cause entity, a causal relation, an effect entity, and a causal weight indicating a relative strength of causal influence of the cause entity on the effect entity, the mapping including mapping causal weights in the causal network to causal weights in the causal knowledge graph;
convert the causal knowledge graph into embeddings, the embeddings comprising a latent vector space representation of the causal knowledge graph;
train the embeddings using a subset of the causal links of the causal knowledge graph; and
use the embeddings for causal discovery to predict additional causal links of the causal knowledge graph.
The abstract idea steps italicized describe a mental process, culminating in predicting additional causal links of the causal knowledge graph (i.e., a judgment or opinion). Each of the articulated steps – translating information from a causal network into a causal knowledge graph according to a mapping; converting the causal knowledge graph into embeddings; training the embeddings; and using the embeddings for causal discovery to predict additional causal links – are those that could be performed by an administrator, either mentally or with pen and paper. (Examiner notes that the train[ing] step doesn’t recite any technical details and could be accomplished with pen and paper. For very small models, one can calculate weight adjustments manually.) If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe mathematical calculations, culminating in predicting additional causal links of the causal knowledge graph. Each of the articulated steps – translating information from a causal network into a causal knowledge graph according to a mapping; converting the causal knowledge graph into embeddings; training the embeddings; and using the embeddings for causal discovery to predict additional causal links – constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. This is further supported by [0001] of applicant’s specification as filed, which describes applications of knowledge graphs. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 doesn’t recite any additional elements.
Claim 10 recites the following additional elements: one or more hardware computing devices configured to.
Claim 19 recites the following additional elements: A non-transitory computer-readable medium comprising instructions […] that, when executed by one or more computing devices, cause the one or more computing devices to perform operations including to.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in Fig. 10 of applicant’s specification as filed along with the corresponding written disclosure.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-9, 11-18, and 20 further narrow the abstract idea above with additional abstract steps and/or information. This simple narrowing of the abstract idea doesn’t integrate it into practical application and/or add significantly more, and the same groupings highlighted previously apply. See MPEP 2106.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claims 1-4, 8-13, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over “CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning” by Jaimini et al. (NPL attached; hereinafter Jaimini) in view of “Knowledge Graph Embeddings for Causal Relation Prediction” by Khatiwada et al. (hereinafter Khatiwada; NPL attached).
Claims 1 and 10
Jaimini discloses:
[A method {processes or methods described on page 49}, comprising:]
[A system {systems described on page 49}, comprising:]
translating information from a causal network into a causal knowledge graph according to a mapping {As seen in Fig. 2 and caption below, a Causal Bayesian network (CBN) is translated into a Causal Knowledge Graph (CausalKG), based on a mapping; page 46}, the causal knowledge graph comprising a plurality of causal links, wherein each of the causal links includes a cause entity, a causal relation, an effect entity {Causal links represented by edges, where the edge connected with more than two nodes represents the causal relationship (i.e., relation) between entities, along with the associated causal effects; page 44}, and a causal weight indicating a relative strength of causal influence of the cause entity on the effect entity {total causal effect, natural direct effect, and natural indirect effect are represented as data properties in this ontology; page 45};
converting the causal knowledge graph into embeddings, the embeddings comprising a latent vector space representation of the causal knowledge graph {CausalKG utilizes the 1) domain knowledge embedded in the KG to provide a comprehensive search space for possible interventional and counterfactual variables that otherwise might be missed with just a Bayesian causality representation, and 2) the expressivity of KG to generate human understandable explanations; page 44}.
Jaimini doesn’t explicitly disclose, however, Khatiwada, in a similar field of endeavor directed to causal relation prediction, teaches:
for causal discovery using knowledge graph link prediction {In this paper, we study the problem of enriching an existing causal KG of news events using KG embeddings-based link prediction techniques; Abstract}, comprising:
training the embeddings using a subset of the causal links of the causal knowledge graph {We split the collected event related triples into test, train and validation set… The train set, which resembles causal KGs, contains causal triples along with other event-related triples.; 4.2. Causal Relation Prediction Benchmarks}; and
using the embeddings for causal discovery to predict additional causal links of the causal knowledge graph {Given a Knowledge Graph 𝒦 with a set of entities ℰ and a set of relations ℛ, source event entity es ∈ ℰ, causal relation r ∈ R, and an integer k, the causal relation prediction problem is find the set of top-k target event entities ℰtop = {e1,e2,...ek}, such that < es,r,ei >∈ 𝒦 for all ei ∈ ℰtop; 3.1. Preliminaries}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Jaimini to include the features of Khatiwada. Given that Jaimini is directed to analyzing and predicting causal relations, one of ordinary skill in the art would have been motivated to look to Khatiwada, in order to facilitate evaluating link prediction techniques on said causal relation predictions {See 1. Introduction of Khatiwada}.
Claims 2 and 11
Jaimini further discloses: wherein the mapping includes mapping causal weights in the causal network to causal weights in the causal knowledge graph {total causal effect, natural direct effect, and natural indirect effect are represented as data properties in this ontology; page 45}.
Claims 3 and 12
Jaimini further discloses: wherein the translating is performed conformant to a causal ontology, the causal ontology defining concepts to structure the causal knowledge graph {The three causal classes are treatment, mediator, and outcome with causal relationships causes and causesWith. In addition, total causal effect, natural direct effect, and natural indirect effect are represented as data properties in this ontology. The designed ontology can be extended to a given domain ontology to describe the causal relationships between the domain entities; page 45}.
Claims 4 and 13
Jaimini further discloses: wherein the mapping further includes: mapping nodes in the causal network into causal entities in the causal knowledge graph {As an initial input, the framework requires a CBN describing the causal relationships of the domain and the domain ontology with causal relations extended using the causal ontology; page 45}; and mapping edges in the causal network into causal links in the causal knowledge graph {The edge connected with more than two nodes represents the causal relationship, mediator variable, and the associated causal effects; page 44}.
Claims 8 and 17
Khatiwada further teaches: wherein the causal discovery includes casual explanation to predict, given an effect entity, a type of a cause entity of the additional causal link {See previous citations to Abstract, 3.1. Preliminaries, and 4.2. Causal Relation Prediction Benchmarks of Khatiwada}.
The motivation and rationale to include the additional features of Khatiwada is the same as set forth previously.
Claims 9 and 18
Khatiwada further teaches: wherein the causal discovery includes casual prediction to predict, given a cause entity, a type of an effect entity of the additional causal link {See previous citations to Abstract, 3.1. Preliminaries, and 4.2. Causal Relation Prediction Benchmarks of Khatiwada}.
The motivation and rationale to include the additional features of Khatiwada is the same as set forth previously.
Claims 5-7, 14-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Jaimini and Khatiwada, further in view of Garapati (US 20230102786).
Claims 5 and 14
The combination of Jaimini and Khatiwada, while teaching the features above, doesn’t explicitly teach, however, Garapati, in a similar field of endeavor directed to causal event graphs, teaches: removing causal links from the causal knowledge graph having causal weights below a predefined minimum threshold of causal weight {The edge generator 140 may be configured to reduce the number of candidate causal event pairs, e.g., by filtering out candidate causal event pairs with causal scores outside of a pre-determined threshold; [0158]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Jaimini and Khatiwada to include the features of Garapati. Given that Jaimini is directed to analyzing causal relations, one of ordinary skill in the art would have been motivated to look to Garapati, in order to facilitate reducing the number of causal events, thereby yielding a more manageable number of events for subsequent processing {See [0158] of Garapati}.
Claims 6 and 15
Jaimini further discloses: wherein a causal event graph is used as proxy for the causal network {See previous citations to pages 44-45}.
The combination of Jaimini and Khatiwada, while teaching the features above, doesn’t explicitly teach, however, Garapati, in a similar field of endeavor directed to causal event graphs, teaches: further comprising, when translating the information into the causal knowledge graph, removing cycles from the causal event graph {See previous citation to [0158]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Jaimini and Khatiwada to include the features of Garapati. Given that Jaimini is directed to analyzing causal relations, one of ordinary skill in the art would have been motivated to look to Garapati, in order to facilitate reducing the number of causal events, thereby yielding a more manageable number of events for subsequent processing {See [0158] of Garapati}.
Claims 7 and 16
Khatiwada further teaches: performing a Markov-based data split between the train and test sets further comprising performing a Markov-based data split between the train and test sets {See previous citation to 4.2. Causal Relation Prediction Benchmarks; examiner notes that performing a Markov-based data split simply describes dividing into splitting test, train and validation sets chronologically, which is reflected in the cited portions of Khatiwada}.
The motivation and rationale to include the additional features of Khatiwada is the same as set forth previously.
The combination of Jaimini and Khatiwada, while teaching the features above, doesn’t explicitly teach, however, Garapati, in a similar field of endeavor directed to causal event graphs, teaches: wherein the causal knowledge graph has a depth of greater than or equal to two nodes from root to leaf node {The arborescence graph 500 also may be defined as a directed, rooted tree in which all edges point away from the root. Thus, the arborescence graph 500 is an example of a directed acyclic graph (DAG), although not every DAG forms an arborescence graph; [0161]; Put another way, the arborescence graph 500 of FIG. 5 may be considered to represent an instance of many possible graph instances that could be generated from the causal event pairs of FIG. 4; [0162]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Jaimini and Khatiwada to include the features of Garapati. Given that Jaimini is directed to analyzing causal relations, one of ordinary skill in the art would have been motivated to look to Garapati, in order to facilitate reducing the number of extraneous causal events, thereby yielding a more manageable number of events for subsequent processing {See [0158] of Garapati}.
Claim 19
Jaimini discloses:
translate information from a causal network into a causal knowledge graph according to a mapping {As seen in Fig. 2 and caption below, a Causal Bayesian network (CBN) is translated into a Causal Knowledge Graph (CausalKG), based on a mapping; page 46}, the causal knowledge graph comprising a plurality of causal links, wherein each causal link includes a cause entity, a causal relation, an effect entity {Causal links represented by edges, where the edge connected with more than two nodes represents the causal relationship (i.e., relation) between entities, along with the associated causal effects; page 44}, and a causal weight indicating a relative strength of causal influence of the cause entity on the effect entity, the mapping including mapping causal weights in the causal network to causal weights in the causal knowledge graph {total causal effect, natural direct effect, and natural indirect effect are represented as data properties in this ontology; page 45};
convert the causal knowledge graph into embeddings, the embeddings comprising a latent vector space representation of the causal knowledge graph {CausalKG utilizes the 1) domain knowledge embedded in the KG to provide a comprehensive search space for possible interventional and counterfactual variables that otherwise might be missed with just a Bayesian causality representation, and 2) the expressivity of KG to generate human understandable explanations; page 44}.
Jaimini doesn’t explicitly disclose, however, Khatiwada, in a similar field of endeavor directed to causal relation prediction, teaches:
for causal discovery using knowledge graph link prediction {In this paper, we study the problem of enriching an existing causal KG of news events using KG embeddings-based link prediction techniques; Abstract}, comprising:
train the embeddings using a subset of the causal links of the causal knowledge graph {We split the collected event related triples into test, train and validation set… The train set, which resembles causal KGs, contains causal triples along with other event-related triples.; 4.2. Causal Relation Prediction Benchmarks}; and
use the embeddings for causal discovery to predict additional causal links of the causal knowledge graph {Given a Knowledge Graph 𝒦 with a set of entities ℰ and a set of relations ℛ, source event entity es ∈ ℰ, causal relation r ∈ R, and an integer k, the causal relation prediction problem is find the set of top-k target event entities ℰtop = {e1,e2,...ek}, such that < es,r,ei >∈ 𝒦 for all ei ∈ ℰtop; 3.1. Preliminaries}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Jaimini to include the features of Khatiwada. Given that Jaimini is directed to analyzing causal relations, one of ordinary skill in the art would have been motivated to look to Khatiwada, in order to facilitate evaluating link prediction techniques on causal relation predictions {See 1. Introduction of Khatiwada}.
The combination of Jaimini and Khatiwada doesn’t explicitly teach, however, Garapati, in a similar field of endeavor directed to causal event graphs, teaches: [a] non-transitory computer-readable medium comprising instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations including to {non-transitory computer-readable medium; [0140]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Jaimini and Khatiwada to include the features of Garapati. Given that Jaimini is directed to analyzing causal relations, one of ordinary skill in the art would have been motivated to look to Garapati, in order to facilitate processing computer-readable input {See [0007] of Garapati}.
Claim 20
Khatiwada further teaches: wherein the causal discovery includes one or more of: casual explanation to predict, given an effect entity, a type of a cause entity of the additional causal link {See previous citations to Abstract, 3.1. Preliminaries, and 4.2. Causal Relation Prediction Benchmarks of Khatiwada}; and causal discovery includes casual prediction to predict, given a cause entity, a type of an effect entity of the additional causal link {See previous citations to Abstract, 3.1. Preliminaries, and 4.2. Causal Relation Prediction Benchmarks of Khatiwada}.
The motivation and rationale to include the additional features of Khatiwada is the same as set forth previously.
(As noted above, claim 20, though indicated as dependent on clam 1, appears to depend on claim 19. Examiner has applied that interpretation here.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes” (NPL attached), which teaches: Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks. Nevertheless, traditional knowledge graph embedding models are not designed to capture such information, to the detriment of predictive power. We propose a novel method that injects numeric edge attributes into the scoring layer of a traditional knowledge graph embedding architecture. Experiments with publicly available numeric-enriched knowledge graphs show that our method outperforms traditional numeric-unaware baselines as well as the recent UKGE model.
US 20050197992, which teaches: A system, method, and computer program product for combining causal domain models with reasoning and text processing for knowledge driven decision support are provided. A knowledge driven decision support system is capable of creating a domain model, extracting and processing quantities of text according to the domain model, and generating understanding of the content and implications of information sensitive to analysts. An interface may be used to receive input to model complex relationships of a domain, establish implications of interest or request a query, and update the causal model.
US 20250036939, which teaches: A computer program product is tangibly embodied on a non-transitory computer-readable medium and includes instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to input a situation event graph and a corresponding scenario into a neural network model, where the neural network model includes a plurality of scenarios, the situation event graph represents a situation, and the corresponding scenario represents a plurality of situations similar to the situation. The neural network model processes the situation event graph and the corresponding scenario to determine a causal impact of the situation.
US 20250077713, which teaches: Methods and systems for managing artificial intelligence (AI) models are disclosed. To manage AI models, an instance of an AI model may not be re-trained using training data determined to be potentially poisoned. By doing so, malicious attacks intending to influence the AI model using poisoned training data may be prevented. To do so, a first level of strength of a first causal relationship present in historical training data may be compared to a second level of strength of a second causal relationship present in a candidate training data set. The first level of strength and the second level of strength may be expected to be similar within a threshold. If a difference between the first level of strength and the second level of strength is not within the threshold, the candidate training data may be treated as including poisoned training data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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