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 (filed on 28 September 2022) are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 22 August 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because:
Claim 20 recites "A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method of generating a digital representation of an organization, the method comprising...", the specification does not clearly define which forms the above “computer usable medium” may take. Such a medium may take many forms, including, but not limited to, non-volatile, volatile and transmission media etc. If the computer readable medium may take the form of the transmission signal, this would render the claim not statutory because it's not tangible.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1, 15, 16 and 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Costabello et al. (U.S. Patent No.: US 10157226, hereinafter Costabello), in view of Sbodio et al. (U.S. Pub. No.: U.S. 20230297851, hereinafter Sbodio), and further in view of Ennajari et al. (“Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling,” 24 September, 2021).
For claim 1, Costabello discloses a system for generating a digital representation of an organization, the system comprising:
at least one processor (Costabello: column 1, lines 20-38, “According to some implementations, a device may include one or more memories, and one or more processors, communicatively coupled to the one or more memories, to receive training data and an ontology for the training data, where the training data may include information associated with a subject of the ontology. The one or more processors may generate a knowledge graph based on the training data and the ontology, and may convert the knowledge graph into knowledge graph embeddings, where the knowledge graph embeddings may include points in a k-dimensional metric space. The one or more processors may receive a new entity that is not present in the knowledge graph embeddings, and may generate a new embedding of the new entity. The one or more processors may add the new embedding to the knowledge graph embeddings, and may utilize the knowledge graph embeddings, with the new embedding, to perform an action…”); and
at least one memory comprising computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the at least one processor to (Costabello: column 1, lines 20-38, “According to some implementations, a device may include one or more memories, and one or more processors, communicatively coupled to the one or more memories, to receive training data and an ontology for the training data, where the training data may include information associated with a subject of the ontology. The one or more processors may generate a knowledge graph based on the training data and the ontology, and may convert the knowledge graph into knowledge graph embeddings, where the knowledge graph embeddings may include points in a k-dimensional metric space. The one or more processors may receive a new entity that is not present in the knowledge graph embeddings, and may generate a new embedding of the new entity. The one or more processors may add the new embedding to the knowledge graph embeddings, and may utilize the knowledge graph embeddings, with the new embedding, to perform an action…”, column 10, line 56-column 11, lines 5, “Bus 310 includes a component that permits communication among the components of device 300. Processor 320 is implemented in hardware, firmware, or a combination of hardware and software. Processor 320 is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, processor 320 includes one or more processors capable of being programmed to perform a function. Memory 330 includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by processor 320”):
obtain ontology and contextual data for a plurality of domains (Costabello: column 3, lines 5-26, “FIGS. 1A-1G are diagrams of an overview of an example implementation 100 described herein. As shown in FIG. 1A, a user device may be associated with a prediction platform. As shown in FIG. 1A, and by reference number 105, a user of the user device (e.g., via a user interface provided to the user) may cause the user device to provide, to the prediction platform, training data for training a knowledge graph associated with a particular disease (e.g., severe acute respiratory syndrome (SARS)). As further shown in FIG. 1A, and by reference number 110, the user may cause the user device to provide, to the prediction platform, an ontology for the training data. In some implementations, the training data and the ontology may not be stored in the user device, but the user device may cause the training data and the ontology to be provided from a resource, storing the training data and the ontology, to the prediction platform. In some implementations, the training data and the ontology may be stored in the prediction platform. In some implementations, although FIGS. 1A-1G relate to healthcare and biomedical domains, the prediction platform may be used with any type of domain and may be domain agnostic”
column 12, lines 6-23, “As shown in FIG. 4, process 400 may include receiving training data and an ontology for the training data (block 410). For example, prediction platform 220 (e.g., using computing resource 224, processor 320, and/or the like) may receive training data and an ontology for the training data. In some implementations, a user of user device 210 may cause user device 210 to provide, to prediction platform 220, training data for training a knowledge graph associated with a particular disease (e.g., SARS). In some implementations, the user may cause user device 210 to provide, to prediction platform 220, an ontology for the training data. In some implementations, the training data and the ontology may not be stored in user device 210, but user device 210 may cause the training data and the ontology to be provided from a resource, storing the training data and the ontology, to prediction platform 220.”
WHERE “ontology and contextual data” is broadly interpreted as “training data and an ontology for the training data”);
generate an ontological knowledge graph using the ontology and contextual data, the ontological knowledge graph comprising a plurality of nodes with links between the plurality of nodes, the plurality of nodes contained within a spherically represented data structure (Costabello: column 4, lines “As further shown in FIG. 1B, and by reference number 120, the knowledge graph may include the training data integrated within the ontology as nodes that represent concepts, and edges or links that show interrelationships (e.g., relations) between the concepts..” column 12, lines 6-23, “As shown in FIG. 4, process 400 may include receiving training data and an ontology for the training data (block 410). For example, prediction platform 220 (e.g., using computing resource 224, processor 320, and/or the like) may receive training data and an ontology for the training data. In some implementations, a user of user device 210 may cause user device 210 to provide, to prediction platform 220, training data for training a knowledge graph associated with a particular disease (e.g., SARS). In some implementations, the user may cause user device 210 to provide, to prediction platform 220, an ontology for the training data. In some implementations, the training data and the ontology may not be stored in user device 210, but user device 210 may cause the training data and the ontology to be provided from a resource, storing the training data and the ontology, to prediction platform 220.”
WHERE “generate an ontological knowledge graph” is broadly interpreted as “training a knowledge graph associated with a particular disease”);
However, Costabello does not explicitly disclose for a plurality of domains,
display the plurality of nodes with the links in a digital view within the spherically represented data structure.
Sbodio discloses for a plurality of domains (Sbodio: paragraph [0002], “…the method includes receiving a first set of data on an entity, where the first set of data includes distinct characteristics of the entity. The method further includes receiving a second set of data on one or more domains of the entity. The method further includes generating, using the first and second set of data, a probabilistic knowledge graph that includes an entity node and a first and second plurality of nodes…” ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “PROBABILISTIC ENTITY-CENTRIC KNOWLEDGE GRAPH COMPLETION” as taught by Sbodio, because it would provide Costabello’s system with the enhanced capability of “Aspects of the present disclosure relate to knowledge graphs, while more particular aspects of the present disclosure relate to generating probabilistic entity-centric knowledge graphs that include both factual entities and relationships of an entity as well as probabilistic entities of the entity. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.” (Sbodio: paragraph [0010]).
However, Costabello and Sbodio do not explicitly disclose display the plurality of nodes with the links in a digital view within the spherically represented data structure.
Ennajari discloses display the plurality of nodes with the links in a digital view within the spherically represented data structure (Ennajari: page 1, “…we propose a Bayesian embedded spherical topic model (ESTM) that combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets…” page 8, “…The topic diversity scores for ESTM and all the baseline models are presented in Table V…we visualized a part of the spherical space where the topic embedding is performed. For this task, we performed the t-distributed stochastic neighbor embedding (t-SNE) visualization technique [55] over all the word vectors and plot the resulting three components, as illustrated in Fig. 4…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling” as taught by Ennajari, because it would provide Costabello’s modified system with the enhanced capability of “…combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.” (Ennajari: paragraph [0001])
For claim 15, Costabello, Sbodio and Ennajari disclose the system of claim 1, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor to provide knowledge representations in one or more forms comprising
a single point status indicator, point numericals, a linear connection data graph, a roadmap Gantt, a semantic graph, a systemigram, a knowledge graph, a knowledge network illustration, a circular Sankey plot, dimensional knowledge model, digital thread, or digital twin (Ennajari: page 1, “…we propose a Bayesian embedded spherical topic model (ESTM) that combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets…” page 8, “…The topic diversity scores for ESTM and all the baseline models are presented in Table V…we visualized a part of the spherical space where the topic embedding is performed. For this task, we performed the t-distributed stochastic neighbor embedding (t-SNE) visualization technique [55] over all the word vectors and plot the resulting three components, as illustrated in Fig. 4…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling” as taught by Ennajari, because it would provide Costabello’s modified system with the enhanced capability of “…combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.” (Ennajari: paragraph [0001])
For claim 16, it is a method claim having similar limitations as recited in claim 1. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of rejected claim 1.
For claim 20, it is a computer program product claim having similar limitations as recited in claim 1. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of rejected claim 1.
Claims 2, 3 and 17 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Costabello et al. (U.S. Patent No.: US 10157226, hereinafter Costabello), in view of Sbodio et al. (U.S. Pub. No.: U.S. 20230297851, hereinafter Sbodio), and further in view of Ennajari et al. (“Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling,” 24 September, 2021, hereinafter Ennajari), and further in view of Muhlmeyer et al. (“Information Spread in a Social Media Age,” 2021, herein after Muhlmeyer).
For claim 2, Costabello, Sbodio and Ennajari disclose the system of claim 1, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor to perform analytics on the ontological knowledge graph (Costabello: column 4, lines “…As further shown in FIG. 1F, and by reference number 175, the candidate generation engine may generate information indicating candidate drugs based on the query and based on the analysis with the knowledge graph…”).
However, Costabello does not explicitly disclose by processing a socio-technical network structure, to determine one or more knowledge network properties comprising centrality, modularity, closure, degree, structural holes, brokers, clustering, transitivity, and optimization.
Muhlmeyer discloses by processing a socio-technical network structure, to determine one or more knowledge network properties comprising centrality, modularity, closure, degree, structural holes, brokers, clustering, transitivity, and optimization.
(Muhlmeyer: Page 203, “…There are three main concerns in socio-technical systems when applied to social media: technical subsystems, social subsystems, and external subsystems.”
column 43, lines “…This chapter will review some basic social network analysis fundamentals. Understanding the essential theories, concepts, and terminology is critical for further discussion of information modeling and control within the scope of online Social networks. The network concepts of density, structural holes, strength of ties, centrality, and distance are briefly explained with visual examples and some mathematical representations. Small world networks and polarization are discussed and are of particular interest when examining online social media groups. Using a simple three-node network group, the relationship between a network configuration and its adjacency matrix is examined. To conclude the chapter, an example of a directional sociogram and its accompanying adjacency matrix for a sports club is given to pave the way toward practical social network analysis applications”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Information Spread in a Social Media Age” as taught by Muhlmeyer, because it would provide Costabello’s modified system with the enhanced capability of “…Understanding the essential theories, concepts, and terminology is critical for further discussion of information modeling and control within the scope of online social networks. The network concepts of density, structural holes, strength of ties, centrality, and distance are briefly explained with visual examples and some mathematical representations..” (Muhlmeyer: page 43)
For claim 3, Costabello, Sbodio and Ennajari disclose the system of claim 2, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor to
receive a query set and align the query set with the ontological knowledge graph and results of the performing of the analytics (Costabello: column 1, lines 40-68, “The one or more instructions may cause the one or more processors to receive a query for information associated with the knowledge graph, and generate candidate responses to the query based on the knowledge graph. The one or more instructions may cause the one or more processors to score the candidate responses based on the revised knowledge graph embeddings, and identify a particular candidate response, of the candidate responses, based on scores for the candidate responses.”).
For claim 17, it is a method claim having similar limitations as recited in claim 2. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of rejected claim 2.
Claim 4 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Costabello et al. (U.S. Patent No.: US 10157226, hereinafter Costabello), in view of Sbodio et al. (U.S. Pub. No.: U.S. 20230297851, hereinafter Sbodio), and further in view of Ennajari et al. (“Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling,” 24 September, 2021, hereinafter Ennajari), and further in view of Ingvaldsen et al. (U.S. Pub. No.: U.S. 20190188332, Ingvaldsen).
For claim 4, Costabello, Sbodio and Ennajari disclose the system of claim 1, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor to frame knowledge representation digital views and visual language (Ennajari: “We now evaluate ESTM on sentiment analysis or opinion mining tasks, one of the most important applications in the NLP domain. It aims to recognize people’s feelings and opinions expressed in the text (e.g., positive, negative, objective, and subjective). We adopt SVM as our classification algorithm for ESTM to predict the ground-truth labels based on the learned topic proportions of each user review. We compared our proposed model to the baseline methods for the sentiment analysis task and measured to what extent our model can differentiate between negative and positive users’ reviews for the Movie reviews dataset, and objective and subjective comments on the Subjectivity dataset. For performance evaluation, we conduct fivefold cross validation on both datasets and used four common metrics, including recall, precision, F1, and accuracy, where higher scores indicate better performance…”
page 8, “…The topic diversity scores for ESTM and all the baseline models are presented in Table V…we visualized a part of the spherical space where the topic embedding is performed. For this task, we performed the t-distributed stochastic neighbor embedding (t-SNE) visualization technique [55] over all the word vectors and plot the resulting three components, as illustrated in Fig. 4…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling” as taught by Ennajari, because it would provide Costabello’s modified system with the enhanced capability of “…combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.” (Ennajari: paragraph [0001])
However, Costabello, Sbodio and Ennajari do not explicitly disclose determine user persons via one or more machine agents and logs.
Ingvaldsen discloses determine user persons via one or more machine agents and logs to frame knowledge representation digital views and visual language (Ingvaldsen: paragraph [0242], “A user interface (UI) is provided as part of the knowledge graph system in an additional embodiment, capable of presenting content of interest to a user. The UI is connected to the graph analytics module. The UI-delivered content is textual, graphics, voice-based, or multi-media in various embodiments, and may include information about the entities, their relationships, and further analytics regarding the entities and relationships including time series data. In alternative embodiments, one or both of “push” and “pull” strategies are enabled to send content to users. In a certain embodiment, the user interface is adapted to receive a query from the user, and the content is responsive to the query. In further embodiments, the user interface is a smart phone, an augmented reality and/or virtual reality (AR/VR) device, a web browser, or a robotic assistant that is connected into the system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “SYSTEM OF DYNAMIC KNOWLEDGE GRAPH BASED ON PROBABALISTIC CARDINALITIES FOR TIMESTAMPED EVENT STREAMS” as taught by Ingvaldsen, because it would provide Costabello’s modified system with the enhanced capability of “…for constructing knowledge graphs and their underlying ontologies and dynamically updating them based on probabilistic cardinalities for timestamped event streams...” (Ingvaldsen: paragraph [0001]).
Claims 5, 6, 7 and 18 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Costabello et al. (U.S. Patent No.: US 10157226, hereinafter Costabello), in view of Sbodio et al. (U.S. Pub. No.: U.S. 20230297851, hereinafter Sbodio), and further in view of Ennajari et al. (“Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling,” 24 September, 2021, hereinafter Ennajari), and further in view of Pillai et al. (U.S. Pub. No.: U.S. 20210358601, Pillai).
For claim 5, Costabello, Sbodio and Ennajari disclose the system of claim 1, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor.
However, Costabello, Sbodio and Ennajari do not explicitly disclose to define knowledge containers as the plurality of nodes, the knowledge containers representing at least on of explicit or tacit knowledge of a plurality of individuals.
Pillai discloses to define knowledge containers as the plurality of nodes, the knowledge containers representing at least on of explicit or tacit knowledge of a plurality of individuals (Pillai: paragraph [0065], “…the ontology knowledge graph can be a non-linear data structure (e.g., a tree data structure, a container tree data structure, a container tree, and/or the like) where data objects are organized in terms of hierarchical relationships with a root value and subtrees of children with a parent node, represented as a set of linked nodes…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “ARTIFICIAL INTELLIGENCE SYSTEM FOR CLINICAL DATA SEMANTIC INTEROPERABILITY” as taught by Pillai, because it would provide Costabello’s modified system with the enhanced capability of “…performed by the inference/rule engine 110 can determine an optimal match for the medical concept “long action pain medication” based at least in part on the description “long action pain medication” and the description “Medication.”... The ontology knowledge graph can be a network of interlinked medical concepts with a hierarchy of class levels and/or interconnections that represent relationships between the medical concepts…” (Pillai: paragraph [0065]).
For claim 6, Costabello, Sbodio, Ennajari and Pillai disclose the system of claim 5, wherein the computer program code is further configured to, with the at least one processor, cause the at least one processor to define a plurality of knowledge paths forming edges as co-occurrence data connections between one or more properties of the knowledge containers (Ennajari: page 1, “…KG can be defined as a structured graph representation of facts about a specific domain in the form of entities (nodes) and relations (edges).”
pages 3-4, “…Before we describe our proposed approach, we first define the mathematical notations used in this article. A corpus C is defined as a collection of Nd documents, where each piece of text d is modeled by a set of words Xd = {xd1, xd2, . . . , xdNx }. Each word is represented by a fixed pretrained D-dimensional vector w that belongs to an embedding matrix obtained by JoSe [29], a text embedding technique trained in the spherical space based on both word to word and word to paragraph cooccurrences. We assume that a document d contains also a set of recognized entities Ed = {ed1, ed2, . . . , edNe} represented by a continuous vector v that belongs to an embedding matrix of entities linked to an external KG lying on the same D-dimensional spherical space as word embeddings…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling” as taught by Ennajari, because it would provide Costabello’s modified system with the enhanced capability of “…combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.” (Ennajari: paragraph [0001])
For claim 7, Costabello, Sbodio and Ennajari disclose the system of claim 1, wherein the digital view is a knowledge representation view and the spherically represented data structure defines a spherical knowledge container as an outer shell to scope one or more boundaries of the knowledge representation view (Ennajari: page 1, “…KG can be defined as a structured graph representation of facts about a specific domain in the form of entities (nodes) and relations (edges).”
pages 3-4, “…Before we describe our proposed approach, we first define the mathematical notations used in this article. A corpus C is defined as a collection of Nd documents, where each piece of text d is modeled by a set of words Xd = {xd1, xd2, . . . , xdNx }. Each word is represented by a fixed pretrained D-dimensional vector w that belongs to an embedding matrix obtained by JoSe [29], a text embedding technique trained in the spherical space based on both word to word and word to paragraph cooccurrences. We assume that a document d contains also a set of recognized entities Ed = {ed1, ed2, . . . , edNe} represented by a continuous vector v that belongs to an embedding matrix of entities linked to an external KG lying on the same D-dimensional spherical space as word embeddings…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling” as taught by Ennajari, because it would provide Costabello’s modified system with the enhanced capability of “…combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.” (Ennajari: paragraph [0001]).
However, Costabello, Sbodio and Ennajari do not explicitly disclose to define knowledge containers as the plurality of nodes, the knowledge containers representing at least on of explicit or tacit knowledge of a plurality of individuals.
Pillai discloses to define knowledge containers as the plurality of nodes, the knowledge containers representing at least on of explicit or tacit knowledge of a plurality of individuals (Pillai: paragraph [0064], “…Further to the non-limiting example described in connection with the natural language processing engine 402, a medical concept determined by the natural language processing engine 402 can be “long acting pain medication,” one or more modifiers determined by the natural language processing engine 402 can be “Long Acting” and “Multiple Types,” an assertion determined by the natural language processing engine 402 can be “Conditional (Patient is intolerant to long acting pain medications, Intolerance to Medication [CONDITION_1234]),” and a relation determined by the natural language processing engine 402 can be “Patient.” Furthermore, the ontology traversal technique performed by the inference/rule engine 110 can determine that the medical concept “long action pain medication” is not included in the medical ontology database 408 as a medical concept. However, the inference/rule engine 110 can determine that a “Pain Medication [MEDICATION_5678]” is included in the medical ontology database 408 and corresponds to the medical concept “long action pain medication.” As such, the inference/rule engine 110 can modify the medical concept “Pain Medication [MEDICATION_5678]” such that an aliasTerm=“long acting pain medication” is added to the medical concept “Pain Medication [MEDICATION_5678]” included in the medical ontology database 408.”
paragraph [0065], “…the ontology knowledge graph can be a non-linear data structure (e.g., a tree data structure, a container tree data structure, a container tree, and/or the like) where data objects are organized in terms of hierarchical relationships with a root value and subtrees of children with a parent node, represented as a set of linked nodes…”
paragraph [0070], “…The population data can be based at least in part on a number of patients associated with the one or more unmatched root concepts. An individual feature score can be based at least in part on a scale (e.g., from 0 to 10) that can be computed for each interlinked concept found in the clinical document using the population data. In certain embodiments, the inference/rule engine 110 can associate a strength of binding to root concepts based at least in part on one or more scores and/or a range of scores.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve upon “Predicting Links In Knowledge Graphs Using Ontological Knowledge” as taught by Costabello by implementing “ARTIFICIAL INTELLIGENCE SYSTEM FOR CLINICAL DATA SEMANTIC INTEROPERABILITY” as taught by Pillai, because it would provide Costabello’s modified system with the enhanced capability of “…performed by the inference/rule engine 110 can determine an optimal match for the medical concept “long action pain medication” based at least in part on the description “long action pain medication” and the description “Medication.”... The ontology knowledge graph can be a network of interlinked medical concepts with a hierarchy of class levels and/or interconnections that represent relationships between the medical concepts…” (Pillai: paragraph [0065]).
For claim 18, it is a method claim having similar limitations as recited in claim 5. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of rejected claim 5.
Allowable Subject Matter
Claims 8-14 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/YU ZHAO/Primary Examiner, Art Unit 2169