The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 01/22/2025, the following is a Non-Final Office Action for Application No. 19034205.
Status of Claims
Claims 1-20 are pending.
Drawings
The applicant’s drawings submitted on 01/22/2025 are acceptable for examination purposes.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01/29/2025 has been acknowledged. The submission 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
A computer-implemented method comprising:generating a knowledge graph representation of a plurality of features extracted based on input data comprising one or more of a plurality of documents, email communication, and records stored in one or more data sources;generating, based upon the knowledge graph representation, a plurality of instructor node embeddings and weighted nested domain context corresponding to each of the plurality of instructor node embeddings;receiving, from a client device associated with a user, a query input, wherein the query input comprises a description of a problem;identifying, based on the plurality of instructor node embeddings and the weighted nested domain context corresponding to each of the plurality of instructor node embeddings, a plurality of prior records having semantic similarity with the query input; andgenerating, based on a respective ranking of each prior record of the plurality of prior records, a list of resolutions to solve the problem identified in the query input.
[or]
A system comprising:at least one memory comprising machine executable instructions; andat least one processor communicatively coupled with the at least one memory and configured to execute the machine executable instructions to perform operations comprising: generating a knowledge graph representation of a plurality of features extracted based on input data comprising one or more of a plurality of documents, email communication, and records stored in one or more data sources; generating, based upon the knowledge graph representation, a plurality of instructor node embeddings and weighted nested domain context corresponding to each of the plurality of instructor node embeddings; receiving, from a client device associated with a user, a query input, wherein the query input comprises description of a problem; identifying, based on the plurality of instructor node embeddings and the weighted nested domain context corresponding to each of the plurality of instructor node embeddings, a plurality of prior records having semantic similarity with the query input; and generating, based on a respective ranking of each prior record of the plurality of prior records, a list of resolutions to solve the problem identified in the query input.
[or]
A non-transitory computer-readable medium (CRM) comprising machine executable instructions stored thereon, which, when executed by at least one processor of a computing device, cause the computing device to perform operations comprising:generating a knowledge graph representation of a plurality of features extracted based on input data comprising one or more of a plurality of documents, email communication, and records stored in one or more data sources;generating, based upon the knowledge graph representation, a plurality of instructor node embeddings and weighted nested domain context corresponding to each of the plurality of instructor node embeddings; receiving, from a client device associated with a user, a query input, wherein the query input comprises description of a problem; identifying, based on the plurality of instructor node embeddings and the weighted nested domain context corresponding to each of the plurality of instructor node embeddings, a plurality of prior records having semantic similarity with the query input; and generating, based on a respective ranking of each prior record of the plurality of prior records, a list of resolutions to solve the problem identified in the query input.
The claim(s) recite(s) the following summarization of the abstract idea which includes generating a list of resolutions to solve the problem identified in the query input executed by the additional element(s) of the above computer, processor, and/or memory mediums. This falls into at least the Abstract Idea Grouping of Mental Processes since the information can be analyzed by an abstract evaluation judgment process. Thus, per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity since the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The computer, processor, and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic computer, processor, and/or memory medium limitation is no more than mere instructions to apply the exception using a generic computer component. Further, sending prediction results to the cloud platform and receiving cloud data for training and learning by a computer, processor, and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The computer, processor, and/or memory medium is used to generally apply the abstract idea without placing any limits on how the computer, processor, and/or memory medium functions. Rather, these limitations only recite the outcome of the functions and do not include any details about how the functions via computer, processor, and/or memory medium are accomplished. See MPEP 2106.05(f). Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: computer, processor, and memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. The additional element of a computer, processor, and/or memory medium is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Further, sending prediction results to the cloud platform and receiving cloud data for training and learning by a computer, processor, and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0096 wherein “Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any appropriate kind of digital computer.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Panda et al. (US 20230342629 A1) hereinafter referred to as Panda in view of Demenschonok et al. (WO 2025250755 A1) hereinafter referred to as Demenschonok.
Panda teaches:
Claim 1. A computer-implemented method comprising:
generating a knowledge graph representation of a plurality of features extracted based on input data comprising one or more of a plurality of documents, email communication, and records stored in one or more data sources (¶0016 Knowledge graphs extract information out of the documents by analyzing the text of the document and extracting content of the documents. In general, knowledge graphs want to extract valuable knowledge from the documents. One example of a knowledge graph is extracting words and phrases from the documents as entities in the graph (e.g., nodes of the graph) with different relationships of the words and phrases (e.g., edges of the graph). The edges connect the different nodes of the knowledge graph with a connection (e.g., a link or line) illustrating the relationships between the nodes ¶0017 The methods and systems generate knowledge graphs on-the-fly that act as a smart compendium of the extracted knowledge from the text. The knowledge graphs are generated in real time without requiring pre-computation. The knowledge graphs may be further utilized for downstream tasks, such as, answering graph-based queries, exploring nontrivial connections between entities, summarization of the textual data, etc. The methods and systems may hop from one textual data source to another via query-based traversal of intermediate knowledge graphs that are generated on-the-fly. The methods and systems facilitate exploration over multiple textual data sources. In general, the search and exploration space is restricted to either the source dataset or the pre-built knowledge base. The methods and systems of the present disclosure enables search across different data sources. The methods and systems integrate different exploration functions desired and merge different data sources using intermediate on-the-fly creation of knowledge graphs);
generating, based upon the knowledge graph representation, a plurality of instructor node embeddings and weighted nested domain context corresponding to each of the plurality of instructor node embeddings (¶0037 The user 104 may use a combination of functions to achieve the results (e.g., a first function to output similar proteins to ‘pA’ and applying a second function to the output of the first function to find related drugs to the similar proteins). In some implementations, the knowledge graph generator function 10 automatically selects a function 34 or a combination of functions 34 to use. The knowledge graph generator function 10 may plug in other models to use as the functions 34. In some implementations, the functions 34 are specific to a certain domain of users. Examples of functions specific to a certain domain of users include named entity recognition (NER) and relation extraction for specific types. The output of the function 34 or the combination of function 34 includes one or more new nodes 38 and one or more new edges 40 based on the processing of the functions 34. The knowledge graph generator function 10 expands the initial knowledge graph 24 with the new node(s) 38 and the new edge(s) 40 and generates an extended initial knowledge graph 26 with the nodes 28, the edges 30, the new node(s) 38, and the new edge(s) 40.);
receiving, from a client device associated with a user, a query input, wherein the query input comprises a description of a problem (¶0018 The methods and systems may hop from one textual data source to another via query-based traversal of intermediate knowledge graphs that are generated on-the-fly. The methods and systems facilitate exploration over multiple textual data sources. In general, the search and exploration space is restricted to either the source dataset or the pre-built knowledge base. The methods and systems of the present disclosure enables search across different data sources. The methods and systems integrate different exploration functions desired and merge different data sources using intermediate on-the-fly creation of knowledge graphs The methods and systems may explore on the fly by adding newer datasets to the knowledge graphs, and thus, helps with keeping information relevant in the knowledge graphs without banking on stale information. ¶0034 The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106.);
identifying, based on the plurality of instructor node embeddings and the weighted nested domain context corresponding to each of the plurality of instructor node embeddings, a plurality of prior records having semantic similarity with the query input (¶0020 The methods and systems first use generic health entity and relation extraction models to produce a knowledge graph from the set of papers on the research about proteins. A subset of the nodes of this graph include protein names. In this example, if traditional knowledge mining is used, the search for the protein ‘pA’ is restricted to this graph. However, the methods and systems provide additional options to the user to do further exploration. For instance, if the user is interested in finding other proteins that are structurally similar to ‘pA,’ the methods and systems apply a function to identify structurally similar proteins that are structurally similar to the protein ‘pA’ and receive the output as protein ‘pB’ (a protein structurally similar to the protein ‘pA’). The methods and systems extend the original knowledge graph by adding an edge with an appropriate edge description between the two protein nodes ‘pA’ and ‘pB.’ Furthermore, the new protein ‘pB’ may be present in other sets of research papers that are different from the original set of papers, thereby facilitating hopping and traversing between different sets of data sources. ¶0034 The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106); and
generating, based on a respective ranking of each prior record of the plurality of prior records, a list of resolutions to solve the problem identified in the query input (¶0034 The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106.¶0046 The knowledge graph generator function 10 may output a merged knowledge graph 48 with the initial knowledge graph 24 and the second knowledge graph 42. The initial knowledge graph 24 is connected to the second knowledge graph 42 by at least one new edge 40. The merged knowledge graph 48 identifies connections between the different data sources (e.g., the new edge(s) 40 identifying a relationship between the node(s) 26 in the initial knowledge graph 24 and the new node(s) 38 in the second knowledge graph 42). In addition, the merged knowledge graph 48 aids the user 104 in discovering more knowledge by exploring the merged knowledge graph 48 based on the different data sources (e.g., the initial data source 12 and the other data source 16) in a compressed form. ¶0067 machine learning models include deep learning machine learning models that receive an input, process the input, and provide an output based on the processing. In some implementations, the functions 34 are heuristic graph based algorithms. The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106. In some implementations, the knowledge graph generator function 10 provides a plurality of functions as a suggestion to the user 104. The plurality of functions are selected based on a type of the node 28 or a text of the node 28. The knowledge graph generator function 10 receives a selection of the function 34 from the plurality of functions provided. For example, the user 104 may select one or more functions 34 or a combination of functions 34 from the set of suggested functions.).
Although not explicitly taught by Panda, Demenschonok teaches in the analogous art of techniques for filtering aggressor signals from biopotential signals:
generating, based upon the knowledge graph representation, a plurality of instructor node embeddings and weighted nested domain context corresponding to each of the plurality of instructor node embeddings (¶00232 (B3) In some embodiments of B1 or B2, the plurality of analog correlators comprises analog 1-D correlators configured to operate in charge, voltage or current domain. (B4) In some embodiments of any of B1 – B3, each analog correlator is configured to correlate the time-series analog signals by applying a respective quantized weight to the time-series analog signals at a predetermined sample rate, wherein the respective quantized weight comprises a coarsely quantized weight that is quantized in amplitude and/or time. (B5) In some embodiments of any of B1 – B4, each comparator is a single-bit comparator configured to compare the respective degree of correlation with a respective threshold and output a respective digital value. (B6) In some embodiments of any of B1 – B5, the plurality of analog correlators is configured to include a predetermined number of correlator weights per channel per gesture, for a predetermined set of target gestures. B7) In some embodiments of any of B1 – B6, the respective quantized weight has a size of 2 bits. (B8) In some embodiments of any of B1 – B7, the respective filter impulse response template corresponds to a predetermined number of samples. (B9) In some embodiments of any of B1 – B9, a first set of analog correlators of the plurality of analog correlators is coupled to a first electrode and a second set of analog correlators of the plurality of analog correlators is coupled to a second electrode, a first set of comparators of the plurality of comparators coupled to the first set of analog correlators is configured to output a first binary output (e.g., correlator 808-2) corresponding to a first gesture, a second set of comparators of the plurality of comparators coupled to the second set of analog correlators is configured to output a second binary output (e.g., correlator 808-4) corresponding to a second gesture. (B10) In some embodiments of B9, the first binary output and the second binary output are combined to generate an interrupt signal (e.g., interrupt 810), the apparatus further comprising a fusion model (e.g., fusion model 812), wherein the fusion model is configured to be woken by the interrupt signal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the techniques for filtering aggressor signals from biopotential signals of Demenschonok with the exploring entities of interest over multiple data sources using knowledge graphs of Panda for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Panda C.1 L.60 teaches that there is a need for knowledge graphs to be updated to keep the information relevant;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Panda Abstract teaches systems identify entities and the relations among the entities within the text of an initial data source and generate knowledge graphs on-the-fly for the identified entities and the relations, and Demenschonok Abstract teaches various implementations that include techniques and apparatuses for multi-channel biopotential signal acquisition, biopotential signal pre-processing, and adaptive signal conditioning; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Panda at least the above cited paragraphs, and Demenschonok at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the techniques for filtering aggressor signals from biopotential signals of Demenschonok with the exploring entities of interest over multiple data sources using knowledge graphs of Panda. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Panda teaches:
Claim 2. The computer-implemented method of claim 1, wherein the generating the knowledge graph comprises:performing data denoising and formatting of the input data to generate denoised and formatted input data;generating or extracting, upon performing the data denoising and formatting, insights by deforming and extracting relevant insights from the denoised and formatted input data;extracting, from each sentence of the relevant insights, a plurality of features, wherein each feature of the plurality of features represents a combination of an entity of a plurality of entities and a respective relationship of the entity with other entities of the plurality of entities; andgenerating, for each feature of the plurality of features, a plurality of multi-level knowledge graphs for each ticket of a plurality of previous tickets (¶0015 This disclosure generally relates to creating intelligence out of documents. Knowledge graphs are often used to represent the knowledge extracted from the documents in a condensed form. Knowledge graphs represent knowledge as concepts and the relationships between the concepts. Knowledge graphs typically provide a framework to understand massive data in a compressed manner. In general, the search and exploration space for knowledge graphs is restricted to either the source dataset or the pre-built knowledge base. Moreover, knowledge graphs can become so large that it becomes difficult to navigate the vast amount of information included in the knowledge graphs. The information included in knowledge graphs may become noise and not of interest to the user. In addition, the information in existing knowledge graphs may not be valid as the information used to create the knowledge graphs may have changed and the knowledge graphs may need to be updated to keep the information relevant. Knowledge graphs extract information out of the documents by analyzing the text of the document and extracting content of the documents. In general, knowledge graphs want to extract valuable knowledge from the documents. One example of a knowledge graph is extracting words and phrases from the documents as entities in the graph (e.g., nodes of the graph) with different relationships of the words and phrases (e.g., edges of the graph). The edges connect the different nodes of the knowledge graph with a connection (e.g., a link or line) illustrating the relationships between the nodes. ¶0017 The present disclosure provides methods and systems for exploring textual data from different data sources. The methods and systems identify relevant entities and the relations among the relevant entities within the text. The relevant entities may be based on a given area of interest.).
Panda teaches:
Claim 3. The computer-implemented method of claim 2, wherein each entity of the plurality of entities represents a feature comprising one of: a manufacturer, a model, a country, a part or component, or a technology (¶0016 Knowledge graphs extract information out of the documents by analyzing the text of the document and extracting content of the documents. In general, knowledge graphs want to extract valuable knowledge from the documents. One example of a knowledge graph is extracting words and phrases from the documents as entities in the graph (e.g., nodes of the graph) with different relationships of the words and phrases (e.g., edges of the graph). The edges connect the different nodes of the knowledge graph with a connection (e.g., a link or line) illustrating the relationships between the nodes. For example, the documents are medical documents and the different drugs mentioned in the medical document are included in the knowledge graph as the entities (nodes of the graph) and the edges of the graphs are the different relationships identified in the medical documents between the drugs (e.g., different side effects of the drugs or how the drugs interact with each other). Another example includes a medical document, and the knowledge graph includes different proteins and drugs discussed in the medical document as the nodes of the knowledge graph and the edges of the knowledge graph represent how the proteins and drugs interact.).
Panda teaches:
Claim 4. The computer-implemented method of claim 2, further comprising identifying, for a set of features of the plurality of features, a set of nodes of a multi-level knowledge graph of the plurality of multi-level knowledge graphs, wherein the set of nodes comprises one or more nodes connecting different entities (¶0034 The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106. The functions 34 are used to expand the initial knowledge graph 24 by identifying a new node 38 that has a new edge 40 (e.g., a relationship) to one or more nodes 28 in the initial knowledge graph 24. The functions 34 also provide connections between different data sources (e.g., the initial data source 12 and the other data source 16) and expand the initial knowledge graph 24 with information from different data sources.).
Panda teaches:
Claim 5. The computer-implemented method of claim 2, further comprising identifying, for a set of features of the plurality of features, a set of nodes of a multi-level knowledge graph of the plurality of multi-level knowledge graphs, wherein the set of nodes comprises one or more nodes found responsible for an issue reported in the plurality of previous tickets (¶0035 The functions 34 are used to expand the initial knowledge graph 24 by identifying a new node 38 that has a new edge 40 (e.g., a relationship) to one or more nodes 28 in the initial knowledge graph 24. The functions 34 also provide connections between different data sources (e.g., the initial data source 12 and the other data source 16) and expand the initial knowledge graph 24 with information from different data sources. In some implementations, the knowledge graph generator function 10 provides suggestions with different functions 34 to select based on the type and/or the text of the nodes 28. The user 104 may select one or more functions 34 or a combination of functions 34 from the set of suggested functions. For example, if the user 104 is interested in a protein in the initial knowledge graph 24 identified by the text and/or the type of the node 28, the knowledge graph generator function 10 may provide a set of functions 34 relating to proteins as suggestions to the user 104.).
Panda teaches:
Claim 6. The computer-implemented method of claim 1, wherein the generating the weighted nested domain context corresponding to each of the plurality of instructor node embeddings comprises:aggregating a plurality of nodes of a multi-level knowledge graph of a plurality of multi- level knowledge graphs to generate a set of instructor nodes, wherein the plurality of nodes is common across the plurality of features;constructing a nested instructor node relationship across the plurality of features;computing a nested instructor node relationship score for the constructed nested instructor node relationship; andapplying the nested instructor node relationship score as a weighted nested instructor score to all nodes of the set of instructor nodes (¶0033 The knowledge graph generator function 10 may access a machine learning model 106 that performs the one or more functions 34. The machine learning model 106 may include a plurality of functions with each function 34 performing different processing. The functions 34 may be pre-trained machine learning models. One example of the machine learning models include deep learning machine learning models that receive an input, process the input, and provide an output based on the processing. In some implementations, the functions 34 are heuristic graph based algorithms. As such, the functions 34 may be of deterministic (algorithm based) or predictive (machine learning or deep learning based) nature. Optionally, the functions 34 may also take into account the previously observed data and/or trends for a certain task to enhance the performance of the functions 34. For example, the task may be to predict structurally similar proteins and the function 34 may take a protein entity as input and run a machine learning model to output a list of structurally similar proteins.).
Panda teaches:
Claim 7. The computer-implemented method of claim 1, wherein the generating the list of resolutions to solve the problem comprises generating the list of resolutions having the respective ranking of each prior record of the plurality of prior records that exceeds a specified threshold value (¶0033 The knowledge graph generator function 10 may access a machine learning model 106 that performs the one or more functions 34. The machine learning model 106 may include a plurality of functions with each function 34 performing different processing. The functions 34 may be pre-trained machine learning models. One example of the machine learning models include deep learning machine learning models that receive an input, process the input, and provide an output based on the processing. In some implementations, the functions 34 are heuristic graph based algorithms. As such, the functions 34 may be of deterministic (algorithm based) or predictive (machine learning or deep learning based) nature. Optionally, the functions 34 may also take into account the previously observed data and/or trends for a certain task to enhance the performance of the functions 34. For example, the task may be to predict structurally similar proteins and the function 34 may take a protein entity as input and run a machine learning model to output a list of structurally similar proteins. The functions 34 may provide an answer to a question or query. One example function 34 is to identify similar objects or items. The function 34 receives the text of the node 28 and the type of the node 28 (e.g., the object name and/or object type) as an input and outputs one or more similar objects based on the processing. Another example function 34 is to identify side-effects of a drug. The function 34 receives the text of the node 28 and the type of the node 28 as the input with the drug name and outputs one or more side-effects of the drug based on the processing performed by the machine learning model 106.).
As per claims 8-14 and 15-20, the system and manufacture tracks the method of claims 1-7 and 1-3,5-7, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7 and 1-3,5-7 are applied to claims 8-14 and 15-20, respectively. Panda discloses that the embodiment may be found as a system and manufacture (Fig. 1 and ¶0052).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
