Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/9/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 10/18/2023.
Claims 1-15 are rejected.
Claims 1-15 are pending.
Specification
The specification filed on 10/18/2023 and 6/18/2024 are acceptable for examination purposes.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “318” has been used to designate convenience_for_bike (Figures 3, 5 and Paragraph [0051]) and School B (Figures 3, 5, Paragraphs [0050] and [0051]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Step 1: Claim 1 is a method claim. Therefore, Claims 1-13 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations (mental process - detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph and using judgement/evaluation to detect/identify counterfactual causes in a causality graph, the target property and the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities (mathematical concept - generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities may be performed by mathematical process, embedding the new entity in a latent space of the knowledge graph relative to existing entities. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: in the paragraph [0037], a neural knowledge graph embedding method, such as DistMult teaches a mathematical concept.)
simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph (mental process - simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph from generating the new entity and using judgement/evaluation to simulate a change of causes in the causality graph and the new entity in the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
A computer-implemented, machine learning method for incorporating a new entity in a knowledge graph for improving a target property (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A computer-implemented, machine learning method for incorporating a new entity in a knowledge graph for improving a target property (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-13. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2,
Step 2A Prong 1:
embedding latent vectors corresponding to each of the existing entities and relations between the existing entities in the latent space (mathematical concept - embedding latent vectors corresponding to each of the existing entities and relations between the existing entities in the latent space may be performed by mathematical process, embedding latent vectors corresponding to each of the existing entities and relations between the existing entities in the latent space. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: in the paragraph [0037], a neural knowledge graph embedding method, such as DistMult teaches a mathematical concept.)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein generating the new entity in the knowledge graph comprises [using a neural network] that includes an embedding layer that uses the embedded latent vectors of the existing entities and features of the new entity as input (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
using a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein generating the new entity in the knowledge graph comprises [using a neural network] that includes an embedding layer that uses the embedded latent vectors of the existing entities and features of the new entity as input (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
using a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 3,
Step 2A Prong 1:
wherein the detecting counterfactual causes comprises using an objective function that determines a minimal change in one or more of a plurality of direct causes to achieve the target property, and wherein the objective function includes as input the direct causes, a desired change in the target property and embedded latent vectors of existing entities (mathematical concept - wherein the detecting counterfactual causes comprises using an objective function that determines a minimal change in one or more of a plurality of direct causes to achieve the target property, and wherein the objective function includes as input the direct causes, a desired change in the target property and embedded latent vectors of existing entities may be performed by mathematical process, using an objective function that determines a minimal change in one or more of a plurality of direct causes. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: in the paragraph [0046],
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∆C1 is the minimal change and the paragraph [0046] teaches the objective function.)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 4,
Step 2A Prong 1:
See the rejection of Claim 3 above, which Claim 4 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein simulating the change of causes in the causality graph is performed [using a simulator] that receives as input an aggregation of an embedded latent vector of the new entity and embedded latent vectors of existing entities of a same type, and outputs a predicted new value for one of the causes (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
using a simulator (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein simulating the change of causes in the causality graph is performed [using a simulator] that receives as input an aggregation of an embedded latent vector of the new entity and embedded latent vectors of existing entities of a same type, and outputs a predicted new value for one of the causes (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
using a simulator (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 5,
Step 2A Prong 1:
recommending to add the new entity to a real-world implementation of a situation modeled by the knowledge graph based on a determination that the predicted new value for one of the causes is greater than or equal to the minimal change in the one or more of the direct causes that corresponds to the one of the causes (mental process - recommending to add the new entity to a real-world implementation of a situation modeled by the knowledge graph based on a determination that the predicted new value for one of the causes is greater than or equal to the minimal change in the one or more of the direct causes that corresponds to the one of the causes may be performed manually by a user with the aid of pen and paper by comparing the predicted new value for one of the causes with the minimal change in the one or more of the direct causes that corresponds to the one of the causes. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 6,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 6 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the simulator determines the changes of the causes due to the new entity through learning functional relationships between the entities of the knowledge graph and causes of the causality graph (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the simulator determines the changes of the causes due to the new entity through learning functional relationships between the entities of the knowledge graph and causes of the causality graph (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 7,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the knowledge graph is created by processing raw data that is collected using a sensor network that includes physical sensor readings, social media networks, databases, survey data and/or sensor stations into triples that connect entities in the knowledge graph (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application; in this case, the knowledge graph created by processing raw data that is collected using a sensor network does not integrate the exception into a practical application. See MPEP 2106.05(h).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the knowledge graph is created by processing raw data that is collected using a sensor network that includes physical sensor readings, social media networks, databases, survey data and/or sensor stations into triples that connect entities in the knowledge graph (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself; in this case, the knowledge graph created by processing raw data that is collected using a sensor network does not amount to significantly more. See MPEP 2106.05(h).)
Regarding Claim 8,
Step 2A Prong 1:
learning an influence of existing links of the knowledge graph based on the triples (mental process – learning an influence of existing links of the knowledge graph based on the triples may be performed manually by a user with the aid of pen and paper by observing/analyzing the knowledge graph based on the triples. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 9,
Step 2A Prong 1:
providing an explanation for the generation of the new entity by identifying the links of the knowledge graph that remarkably influence the predictions of the features and relations of the new entity (mental process – providing an explanation for the generation of the new entity by identifying the links of the knowledge graph that remarkably influence the predictions of the features and relations of the new entity may be performed manually by a user with the aid of pen and paper by observing/identifying the links of the knowledge graph influencing the predictions of the features and relations of the new entity. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 10,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 10 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein a graph-based counterfactual cause detector predicts the change of the causes based on the causality graph such that the target property is changed to a target value, and wherein, in the predictions, the causes are changed minimally in terms of costs/effort (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein a graph-based counterfactual cause detector predicts the change of the causes based on the causality graph such that the target property is changed to a target value, and wherein, in the predictions, the causes are changed minimally in terms of costs/effort (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 11,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 11 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application; in this case, the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city does not integrate the exception into a practical application. See MPEP 2106.05(h).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself; in this case, the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city does not amount to significantly more. See MPEP 2106.05(h).)
Regarding Claim 12,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 12 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application; in this case, the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient does not integrate the exception into a practical application. See MPEP 2106.05(h).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself; in this case, the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient does not amount to significantly more. See MPEP 2106.05(h).)
Regarding Claim 13,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 13 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application; in this case, the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops does not integrate the exception into a practical application. See MPEP 2106.05(h).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself; in this case, the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops does not amount to significantly more. See MPEP 2106.05(h).)
Regarding Claim 14,
Step 1: Claim 14 is a system claim. Therefore, Claim 14 is directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations (mental process - detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph and using judgement/evaluation to detect/identify counterfactual causes in a causality graph, the target property and the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities (mathematical concept - generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities may be performed by mathematical process, embedding the new entity in a latent space of the knowledge graph relative to existing entities. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: in the paragraph [0037], a neural knowledge graph embedding method, such as DistMult teaches a mathematical concept.)
simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph (mental process - simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph from generating the new entity and using judgement/evaluation to simulate a change of causes in the causality graph and the new entity in the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
A computer system programmed for incorporating a new entity in a knowledge graph for optimizing or improving a target property (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A computer system programmed for incorporating a new entity in a knowledge graph for optimizing or improving a target property (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 14 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 15,
Step 1: Claim 14 is a system claim. Therefore, Claim 14 is directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations (mental process - detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph and using judgement/evaluation to detect/identify counterfactual causes in a causality graph, the target property and the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities (mathematical concept - generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities may be performed by mathematical process, embedding the new entity in a latent space of the knowledge graph relative to existing entities. See MPEP 2106.04(a)(2)(I)(C). Examiner’s note: in the paragraph [0037], a neural knowledge graph embedding method, such as DistMult teaches a mathematical concept.)
simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph (mental process - simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph may be performed manually by a user with the aid of pen and paper by observing/analyzing the causality graph from generating the new entity and using judgement/evaluation to simulate a change of causes in the causality graph and the new entity in the knowledge graph. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
A tangible, non-transitory computer-readable medium for incorporating a new entity in a knowledge graph for optimizing or improving a target property (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the computer-readable medium having instructions thereon, which, upon being executed by one or more processors, provides for execution of the following steps (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A tangible, non-transitory computer-readable medium for incorporating a new entity in a knowledge graph for optimizing or improving a target property (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the computer-readable medium having instructions thereon, which, upon being executed by one or more processors, provides for execution of the following steps (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more.
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.
Claims 1, 2, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jaimini et al. (“CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning”) (hereinafter Jaimini), in view of Lee et al. (“INGRAM: Inductive Knowledge Graph Embedding via Relation Graphs”) (hereinafter Lee).
Regarding Claim 1,
Jaimini teaches:
A computer-implemented, machine learning method comprising: (preamble)
“detecting counterfactual causes in a causality graph that are to be modified to achieve the target property” (Jaimini, Page 2, Col. 2, Lines 2-3 and Lines 9-10, “causal knowledge graph (CausalKG) framework […] the CausalKG’s interventional and counterfactual reasoning can be used by […]”; Jaimini, Page 7, Col. 2, Lines 25-31, “The advantage of constructing a CausalKG is the integration of causality in reasoning and prediction processes […] Such integration can improve the accuracy and reliability of existing AI algorithms by providing better explainability of the outcome.”; Examiner’s note: causal knowledge graph’s (CausalKG’s) interventional and counterfactual reasoning, and the integration of causality in reasoning and prediction processes for better explainability of the outcome teach detecting counterfactual causes (counterfactual reasoning) in a causality graph that are to be modified to achieve the target property (better explainability of the outcome).)
“wherein the causality graph is connected to the knowledge graph by links representing semantic relations” (Jaimini, Page 3, Col. 1, Lines 12-13, “The goal of CausalKG is to support the integration of causal knowledge into the KGs […]”; Jaimini, Page 1, Col. 1, Lines 23-24, “KG is a graphical data model which captures the semantic relationships between entities such as events, objects, or concepts.”; Examiner’s note: the integration of causal knowledge into the KGs teaches the causality graph is connected to the knowledge graph by links and KG capturing the semantic relationships between entities further teaches links representing semantic relations.)
simulating a change of causes in the causality graph (Jaimini, Page 3, Lines 28-29, “supporting the ability to represent and respond to changes in the causal system.”; Examiner’s note: representing and responding to changes in the causal system teaches simulating a change of causes in the causality graph.)
Jaimini does not explicitly teach incorporating a new entity in a knowledge graph for optimizing or improving a target property, generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities and resulting from generating a new entity in the knowledge graph.
Lee teaches “INGRAM: Inductive Knowledge Graph Embedding via Relation Graphs (title)” comprising:
for incorporating a new entity in a knowledge graph for optimizing or improving a target property (Lee, Section 1, “At inference time, INGRAM generates embeddings of new relations and entities by aggregating neighbors’ embeddings […]”; Lee, Section 1, “The aggregation process is optimized to maximize the plausibility scores of triplets […]”; Examiner’s note: generating embeddings of new entities by aggregating neighbors’ embeddings teaches incorporating a new entity in a knowledge graph and aggregation process optimized to maximize the plausibility scores further teaches optimizing or improving a target property.)
“generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities” (Lee, Section 1, “INGRAM, an Inductive knowledge graph embedding method that can generate embedding vectors for new relations and entities only appearing at inference time […] entity embedding vectors are computed by attention-based aggregations of their neighbors’ embeddings.”; Examiner’s note: generating embedding vectors for new entities and entity embedding vectors computed by aggregations of their neighbors’ embeddings teach generating the new entity in the knowledge graph by embedding the new entity (entity embedding vectors) in a latent space of the knowledge graph (embedding vector space) relative to existing entities (neighbors’ embeddings).)
resulting from generating the new entity in the knowledge graph (Lee, Link Prediction in Fig. 2, Examiner’s note: link prediction in Fig. 2 shows resulting from generating the new entity in the knowledge graph.
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the invention in Jaimini by applying the new entities and relations generation as taught in Lee to the causal knowledge graph in Jaimini in order to “outperform[] different knowledge graph completion methods” (Lee, Section 1).
Regarding Claim 2,
The combination of Jaimini and Lee teaches:
“The method of claim 1, further comprising” (preamble)
“embedding latent vectors corresponding to each of the existing entities and relations between the existing entities in the latent space” (Lee, Section 1, “INGRAM, an Inductive knowledge graph embedding method that can generate embedding vectors for new relations and entities only appearing at inference time […] entity embedding vectors are computed by attention-based aggregations of their neighbors’ embeddings.”; Examiner’s note: generating embedding vectors for new relations and entities and entity embedding vectors computed by aggregations of their neighbors’ embeddings teach embedding latent vectors (embedding vectors) corresponding to each of the existing entities and relations between the existing entities in the latent space (aggregations of their neighbors’ embeddings).)
“wherein generating the new entity in the knowledge graph comprises using a neural network that includes an embedding layer that uses the embedded latent vectors of the existing entities and features of the new entity as input” (Lee, Section 1, “entity embedding vectors are computed by attention-based aggregations of their neighbors’ embeddings.”; Lee, Page 14, “We modified CompGCN so that the model also uses randomly initialized embeddings for new entities appearing at inference time.”; Examiner’s note: entity embedding vectors computed by aggregations of their neighbors’ embeddings teach the embedded latent vectors of the existing entities (neighbors’ embeddings) and features of the new entity (entity embedding vectors) as input. Modifying CompGCN (Composition-based Multi-Relational Graph Convolutional Network) so that the model also uses randomly initialized embeddings for new entities further teaches using a neural network that includes an embedding layer for generating the new entity in the knowledge graph.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 14,
Claim 14 recites substantially the same limitations as Claim 1, in the form of a system, therefore, it is rejected under the same rationale.
Regarding Claim 15,
Claim 15 is a system to perform the method of Claim 1, therefore, it is rejected under the same rationale.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee as applied in claim 1, and further in view of Crupi et al. (“Counterfactual Explanations as Interventions in Latent Space”) (hereinafter Crupi).
Regarding Claim 3,
The combination of Jaimini and Lee teaches:
“The method of claim 1,” (preamble)
Jaimini and Lee do not explicitly teach wherein the detecting counterfactual causes comprises using an objective function that determines a minimal change in one or more of a plurality of direct causes to achieve the target property, and wherein the objective function includes as input the direct causes, a desired change in the target property and embedded latent vectors of existing entities.
Crupi teaches “Counterfactual Explanations as Interventions in Latent Space (title)” comprising:
“wherein the detecting counterfactual causes comprises using an objective function that determines a minimal change in one or more of a plurality of direct causes to achieve the target property” (Crupi, Equation (11) on Page 14, “
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where a* is the “cheapest” action – in terms of the cost function cost(a, x) – that the individual identified by x0 needs to perform in order to reach a different model outcome.”; Examiner’s note: arg min cost function teaches detecting counterfactual causes using an objective function that determines a minimal change in one or more of a plurality of direct causes (arg min cost function) to achieve the target property (model outcome).)
“wherein the objective function includes as input the direct causes, a desired change in the target property and embedded latent vectors of existing entities” (Crupi, Section 3.2, “1. use the SCM to translate the problem from feature space to the space of exogenous and root variables, that we shall call latent space hereafter, 2. Apply an arbitrary counterfactual explanation optimizer on latent space, 3. Translate counterfactuals back to the original feature space.”; Examiner’s note: translating the problem to the space of exogenous and root variables (latent space) teaches the objective function including as input the direct causes and embedded latent vectors of existing entities (exogenous and root variables) and translating counterfactuals back to the original feature space further teaches a desired change in the target property.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini and Lee by applying the counterfactual explanations as interventions in latent space as taught in Crupi to the causal knowledge graph in Jaimini in order to “have[] the advantage of providing the end users with feasible actions to reach a desired outcome” (Crupi, Section 3.2).
Regarding Claim 10,
The combination of Jaimini, Lee and Crupi teaches:
“The method of claim 1,” (preamble)
“wherein a graph-based counterfactual cause detector predicts the change of the causes based on the causality graph such that the target property is changed to a target value, and wherein, in the predictions, the causes are changed minimally in terms of costs/effort” (Jaimini, Page 3, Col. 1, Lines 12-16, “The goal of CausalKG is to support the integration of causal knowledge into the KGs for improving domain explainability, promoting interventional, counterfactual reasoning, and causal inference in downstream AI tasks.”; Crupi, Equation (11) on Page 14, “
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where a* is the “cheapest” action – in terms of the cost function cost(a, x) – that the individual identified by x0 needs to perform in order to reach a different model outcome.” Examiner’s note: the integration of causal knowledge into the KGs for counterfactual reasoning teaches a graph-based counterfactual cause detector. Promoting interventional and causal inference further teaches predicting the change of the causes based on the causality graph such that the target property is changed to a target value. Arg min cost function further teaches wherein, in the predictions, the causes are changed minimally in terms of costs/effort.)
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee, and further in view of Crupi as applied in claim 3, and further in view of Hamilton et al. (“Inductive Representation Learning on Large Graphs”) (hereinafter Hamilton).
Regarding Claim 4,
The combination of Jaimini, Lee and Crupi teaches:
“The method of claim 3,” (preamble)
simulating the change of causes in the causality graph (Jaimini, Page 3, Lines 28-29, “supporting the ability to represent and respond to changes in the causal system.”; Examiner’s note: representing and responding to changes in the causal system teaches simulating a change of causes in the causality graph.)
Jaimini, Lee and Crupi do not explicitly teach using a simulator that receives as input an aggregation of an embedded latent vector of the new entity and embedded latent vectors of existing entities of a same type, and outputs a predicted new value for one of the causes.
Hamilton teaches “Inductive Representation Learning on Large Graphs (title)” comprising:
“using a simulator that receives as input an aggregation of an embedded latent vector of the new entity and embedded latent vectors of existing entities of a same type, and outputs a predicted new value for one of the causes” (Hamilton, Algorithm 1 and Section 3.1, “features for all nodes […] are provided as input […] After aggregating the neighboring feature vectors, GraphSAGE then concatenates the node’s current representation, hvk-1, with the aggregated neighborhood vector […] we denote the final representations output at depth K […]”; Examiner’s note: GraphSAGE aggregating the neighboring feature vectors and concentrating the node’s current representation with the aggregated neighborhood vector teach using a simulator that receives as input an aggregation of an embedded latent vector of the new entity and embedded latent vectors of existing entities of a same type. The final representations output at depth K further teaches outputs a predicted new value for one of the causes.
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini, Lee and Crupi by applying the general framework, called GraphSAGE (SAmple and aggreGatE) as taught in Hamilton to the causal knowledge graph in Jaimini in order to “consistently outperform[] state-of-the-art baselines, effectively trade[] off performance and runtime by sampling node neighborhoods” (Hamilton, Section 6).
Regarding Claim 5,
The combination of Jaimini, Lee, Crupi and Hamilton teaches:
“The method of claim 4, further comprising” (preamble)
“recommending to add the new entity to a real-world implementation of a situation modeled by the knowledge graph based on a determination that the predicted new value for one of the causes is greater than or equal to the minimal change in the one or more of the direct causes that corresponds to the one of the causes” (Crupi, Section 3.2, “generate explanations and corresponding recommendations by searching for nearest counterfactuals […] providing, besides counterfactual explanations, causal-aware recommendations […]”; Crupi, Section 6, “providing the end user with realistic explanations and feasible recommendations to gain the desired output […]”; Examiner’s note: generating explanations and corresponding recommendations by searching for nearest counterfactuals and providing the end user with realistic explanations and feasible recommendations to gain the desired output teach recommending to add the new entity (counterfactual explanations) to a real-world implementation of a situation modeled by the knowledge graph based on a determination that the predicted new value for one of the causes is greater than or equal to the minimal change (searching for nearest counterfactuals) in the one or more of the direct causes that corresponds to the one of the causes.)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 6,
The combination of Jaimini, Lee, Crupi and Hamilton teaches:
“The method of claim 4,” (preamble)
“wherein the simulator determines the changes of the causes due to the new entity through learning functional relationships between the entities of the knowledge graph and causes of the causality graph” (Jaimini, Page 3, Col. 1, Lines 12-16, “The goal of CausalKG is to support the integration of causal knowledge into the KGs for improving domain explainability, promoting interventional, counterfactual reasoning, and causal inference in downstream AI tasks.”; Jaimini, Page 4, Col. 2, Lines 26-29, “The CausalKG, with the causal relations, enables domain explainability using counterfactual reasoning (“e.g., was it action A which led to this effect?”)”; Jaimini, Page 7, Col. 2, Lines 25-29, “The advantage of constructing a CausalKG is the integration of causality in reasoning and prediction processes, such as the agent action understanding, planning, and medical diagnosis process.“; Examiner’s note: causal inference in downstream AI tasks teaches a simulator. Supporting the integration of causal knowledge into the KGs and the integration of causality in reasoning and prediction processes further teach determining the changes of the causes due to the new entity through learning functional relationships (causal relations) between the entities of the knowledge graph (prediction processes) and causes of the causality graph (causality in reasoning).)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee as applied in claim 1, and further in view of Tabatabaei et al. (US 20170094592 A1) (hereinafter Tabatabaei).
Regarding Claim 7,
The combination of Jaimini and Lee teaches:
“The method of claim 1,” (preamble)
Jaimini and Lee do not explicitly teach wherein the knowledge graph is created by processing raw data that is collected using a sensor network that includes physical sensor readings, social media networks, databases, survey data and/or sensor stations into triples that connect entities in the knowledge graph.
Tabatabaei teaches “SCALABLE DATA DISCOVERY IN AN INTERNET OF THINGS (IOT) SYSTEM (title)” comprising:
“wherein the knowledge graph is created by processing raw data that is collected using a sensor network that includes physical sensor readings, social media networks, databases, survey data and/or sensor stations into triples that connect entities in the knowledge graph” (Tabatabaei, Paragraph [0002], “data is provided by RFID, sensor nodes or other network-enabled devices (or is submitted directly by human users via social media and/or smart devices—i.e. Citizen Sensing) […] The data can be provided as raw values […] data can be stored on the nodes and devices”; Tabatabaei, Paragraph [0010], “The sensory data in LSM is annotated and transformed into RDF triples. The triples are then stored in storage, which is capable of executing the SPARQL queries”; Tabatabaei, Paragraph [0166], “a sensor, a digital camera”; Examiner’s note: the sensory data transformed into RDF triples teaches the knowledge graph is created by processing raw data.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini and Lee by applying the raw data collected using a sensor network as taught in Tabatabaei to the causal knowledge graph in Jaimini in order to “simplify the distribution of discovery data” (Tabatabaei, Paragraph [0025]).
Regarding Claim 8,
The combination of Jaimini, Lee and Tabatabaei teaches:
“The method of claim 7, further comprising” (preamble)
“learning an influence of existing links of the knowledge graph based on the triples” (Lee, Figure 2, “During training, INGRAM learns how to aggregate the neighbors’ embeddings by maximizing the scores of training triplets.“; Examiner’s note: learning how to aggregate the neighbors’ embeddings by maximizing the scores of training triplets teaches learning an influence of existing links (learning how to aggregate the neighbors’ embeddings) of the knowledge graph based on the triples.)
The reasons of obviousness have been noted in the rejection of Claim 7 above and applicable herein.
Regarding Claim 9,
The combination of Jaimini, Lee and Tabatabaei teaches:
“The method of claim 8, further comprising” (preamble)
providing an explanation (Jaimini, Page 4, Col. 1, Lines 11-13, “generate a human-understandable explanation taking the context information of a given observation into account.”)
for the generation of the new entity by identifying the links of the knowledge graph that remarkably influence the predictions of the features and relations of the new entity (Lee, Section 1, “INGRAM generates embeddings of new relations and entities by aggregating neighbors’ embeddings based on the new relation graph computed from a given inference knowledge graph and the attention weights learned during training.“; Examiner’s note: generating embeddings for new relations and entities based on the new relation graph computed from a given inference knowledge graph and the attention weights learned during training teach for the generation of the new entity (generating embeddings of entities) by identifying the links of the knowledge graph (the new relation graph computed from a given inference knowledge graph) that remarkably influence the predictions of the features and relations of the new entity (the attention weights learned during training).)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee as applied in claim 1, and further in view of Benes et al. (“Knowledge graphs for Smart Cities”) (hereinafter Benes).
Regarding Claim 11,
The combination of Jaimini and Lee teaches:
“The method of claim 1,” (preamble)
Jaimini and Lee do not explicitly teach wherein the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city.
Benes teaches “Knowledge graphs for Smart Cities (title)” comprising:
“wherein the knowledge graph is for a smart city, the new entity is an entity to be added to a geographic location in the smart city and the target property to improve represent an interest of citizens of the smart city” (Benes, Section III, “The coordinator (Smart Prague) must coordinate the proper connection between ontologies covered by knowledge graphs for the city.”; Benes, Fig. 4 and Section IV, “Various domains for parking are presented in the Knowledge Graph in Fig. 4, where intact components were released, and new relations were created (red color relations) […] modern cities need to find and set proper relations between different variables, creating new knowledge from current datasets.”; Examiner’s note: knowledge graph for Smart Prague with creating knowledge from current datasets and new relations for parking problems teaches wherein the knowledge graph is for a smart city (Prague), the new entity is an entity to be added to a geographic location in the smart city and the target property to improve (parking problems) represent an interest of citizens of the smart city.
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini and Lee by applying the knowledge graph for smart cities as taught in Benes to the causal knowledge graph in Jaimini in order to “lead[] to enormous data coordination and connection to the targeted city development” (Benes, Section VI).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee as applied in claim 1, and further in view of Nicholson et al. (“Constructing knowledge graphs and their biomedical applications”) (hereinafter Nicholson).
Regarding Claim 12,
The combination of Jaimini and Lee teaches:
“The method of claim 1,” (preamble)
Jaimini and Lee do not explicitly teach wherein the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient.
Nicholson teaches “Constructing knowledge graphs and their biomedical applications (title)” comprising:
“wherein the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient” (Nicholson, Section 3.2 and Section 3.2.2, “Knowledge graphs have been applied to many biomedical challenges ranging from identifying proteins’ functions [165] to prioritizing cancer genes [166] to recommending safer drugs for patients […] There are a multitude of examples where knowledge graphs have been applied to identify new properties of drugs. Tasks in this field involve predicting drugs interacting with other drugs [184] identifying molecular targets a drug might interact with [185] and identifying new disease treatments for previously established drugs [186].”; Examiner’s note: knowledge graphs applied to many biomedical challenges or knowledge graphs applied to identify new properties of drugs teaches wherein the knowledge graph is for a molecular system or for medical treatments, the new entity is a change in molecular structure or treatment and the target property to improve is a condition of a patient.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini and Lee by applying the knowledge graphs and their biomedical applications as taught in Nicholson to the causal knowledge graph in Jaimini in order to “find[] new treatments for existing drugs, aid[] efforts to diagnose patients and identify[] associations between diseases and biomolecules” (Nicholson, Section 3).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jaimini in view of Lee as applied in claim 1, and further in view of Rajasekhara et al. (“Construction and Integration of Knowledge Grid in Agricultural Information Management Services”) (hereinafter Rajasekhara).
Regarding Claim 13,
The combination of Jaimini and Lee teaches:
“The method of claim 1,” (preamble)
Jaimini and Lee do not explicitly teach wherein the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops.
Rajasekhara teaches “Construction and Integration of Knowledge Grid in Agricultural Information Management Services (title)” comprising:
“wherein the knowledge graph is for status of an agricultural crop and the causality graph is for crop stresses, the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops” (Rajasekhara, Section 1.2, “[…] for the farmer to know the useful information to avoid the disease in plantation and improve the crop production […] build knowledge grids with numerous elements on crop information […] The knowledge grid is a graph or network formed by an element entity and the relation between the elements […] Element is an entity […] It includes behaviors like seeds, […], crop disease […]”; Examiner’s note: knowledge grids with elements on crop information including crop disease for the farmer to know the useful information to avoid the disease in plantation and improve the crop production teaches wherein the knowledge graph is for status of an agricultural crop (knowledge grid with crop information) and the causality graph is for crop stresses (knowledge grid with crop disease), the new entity is an action to be taken on the crop and the target property to improve is a condition of the crops (improve crop production).)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Jaimini and Lee by applying the construction and integration of knowledge grid in agricultural information management services as taught in Rajasekhara to the causal knowledge graph in Jaimini in order to “avoid the disease in plantation and improve the crop production” (Rajasekhara, Section 1.2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bhowmik et al. teaches explainable link prediction for emerging entities in knowledge graphs. Ji teaches a survey on knowledge graphs: representation, acquisition, and applications.
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/YONG DOO RHO/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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