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 .
This action is in response to the application and claims filed 10/06/2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
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 an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding independent claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claim 1 is directed to a computer-implemented method, corresponding to one of the four statutory categories – a process.
Step 2A Prong 1: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
The claim recites the following limitations:
“generating an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the task performed using the source content to generate the target content; and
identifying an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content” – as drafted, under their broadest reasonable interpretation (BRI), in view of the specification, these limitations cover concepts encompassing mental processes (evaluation, judgement, or opinion to generate a comparison score based on comparing an observed concept and a performed task and to identify an issue in observed content based on the comparison score.
Accordingly claim 1 recites an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “A computer-implemented method, executed on a computing device, comprising:
extracting an ontological concept from the source content using a natural
language processing (NLP) engine;
extracting an ontological concept from the target content.”
These are insignificant extra-solution activities that are not integrated into the claim as a whole and do not add meaningful limitations to the above-noted mental processes specified in this claim.
That is, “extracting an ontological concept from the source content;” and “extracting an ontological concept from the target content” amount to mere data gathering. (See MPEP 2106.05(g)).
The claim also recites the additional elements: “A computer-implemented method, executed on a computing device”, “using the NLP engine” and “processing target content generated by processing source content using a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content;” which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “a computing device” performing generic computer function) , which does not integrate a judicial exception into a practical application.. See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this claim as a combination does not add anything further than the individual elements.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “extracting an ontological concept from the source content” and “extracting an ontological concept from the target content” are the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of extracting transmitted and received content/data (WURC), comparing it (mental process - abstract) and identifying an issue based on the comparison and the content (mental process – abstract). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding claim 2, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step1: Claim 2 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites “mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph.”
The mapping limitation added by this claim, as drafted, under its BRI, covers a concept performed in the human mind (source content can be observed by a human and then evaluation, judgement, or opinion used to do mapping/correlating). This limitation in the context of this claim encompasses a mental process to map/correlate concepts from observed content to target desired content in the graph.
Dependent claim 2, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element:
“obtaining an ontological graph including a plurality of ontological concepts; and”
This is insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted mental processes specified in this claim. That is, “obtaining an ontological graph including a plurality of ontological concepts; and,” amounts to mere data gathering. (See MPEP 2106.05(g)).
Step 2B:
The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this claim as a combination does not add anything further than the individual elements.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “extracting an ontological concept from the source content” and “extracting an ontological concept from the target content” is the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of extracting transmitted and received content/data (WURC), comparing it (mental process - abstract) and identifying an issue based on the comparison and the content (mental process – abstract). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 3 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites “determining a location of the ontological concept from the source content within the ontological graph;”
“determining a location of the ontological concept for the target content within the ontological graph; and”
“determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.”
The determining a location from the source content limitation, as drafted, under its BRI, in view of the specification, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the source data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determining a location from the target content limitation, as drafted, under its BRI, in view of the specification, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the target data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determining a distance and a relationship limitation added by this claim covers a mathematical concept (determining the distance and relationship to find the ontological concept comparison score). Such distance and relationship finding of the source and target content uses the ontological concept comparison score for particular task:
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can be done by hand with pen and paper, as suggested by the discussion of this step, in paragraph 56 of the specification, further providing evidence that the claimed distance and relationship finding is for the purpose of reaching a comparison score that is itself a mathematical concept.
Dependent claim 3, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 4, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 4 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites “an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content.”
The equivalent relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “an equivalent relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
The parent-child relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “a parent-child relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed parent-child relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
Dependent claim 4, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 5, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 5 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites “identifying a hallucination in the target content; and
identifying a missing ontological concept in the target content.”
The identifying a hallucination limitation, as drafted under its BRI, in view of the specification, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying “an ontological concept that is fabricated by a generative AI model… (see e.g., paragraph 62 and figure 200 of specification) in the target content). The above limitation in the context of this claim encompasses a “generative AI safeguarding process 10 (which) is able to identify 108 issues automatically without supervision for a particular task by identifying 120 the hallucination.” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper. The identifying a missing ontological concept limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying missing data). The above limitation in the context of this claim encompasses a “task-based rules engine 222 (which) identifies a “hallucination” in target content 206 (i.e., an ontological concept that is fabricated by generative AI model 200) when there is not a corresponding ontological concept in source content 202” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper.
Dependent claim 5, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 6, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 6 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites “providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue.”
The limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to provide feedback on different kinds of observed content/data in a received request and an identified issue).The above limitation in the context of this claim encompasses “providing 124 feedback to the generative AI model includes one or more of: processing 126 a request including the source content, the target content, and the hallucination in the target content; and processing 128 a request including the source content, the target content, and the missing ontological concept in the target content” (see e.g., paragraph 65 of specification). More specifically “Since generative AI safeguarding process 10 provides explainable results that can point exactly which ontological concepts in the target/source are missing/hallucinated, generative AI safeguarding process 10 can use this information to improve or fix a generated response by generative AI model 200” (see e.g., paragraph 64 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Dependent claim 6, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 1.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 7, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 7 is directed to a method as depending from claim 6 and further depending from claim 1, thus the analysis for patent eligibility is incorporated herein.
Step 2A Prong 2: The claim recites “processing a request including the source content, the target content, and the hallucination in the target content; and
processing a request including the source content, the target content, and the missing ontological concept in the target content.”
These limitations amount to mere data gathering.
The judicial exceptions are not integrated into a practical application.
The limitation of “processing a request including the source content, the target content, and the hallucination in the target content” describes data gathering with the request being the input of data. Such data gathering can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The claim also recites “processing a request including the source content, the target content, and the missing ontological concept in the target content.” This limitation describes data gathering with the request being the input of data. Such data gathering can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding independent claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claim 8 is directed to a computing system comprising: a memory; and a processor, corresponding to one of the four statutory categories – a machine.
Step 2A Prong 1: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
The claim recites the following limitations for a processor configured:
to generate an ontological graph including a plurality of ontological concepts using the NLP engine;
to generate an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the ontological graph and the task performed using the source content to generate the target content, and
to identify an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content” – as drafted, under their broadest reasonable interpretation (BRI), in view of the specification, these limitations cover concepts encompassing mental processes (evaluation, judgement, or opinion to generate a comparison score based on comparing an observed concept and a performed task and to identify an issue in observed content based on the comparison score.
Accordingly claim 8 recites an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “A computing system comprising: a memory; and a processor configured to … extract an ontological concept from the source content using a natural language processing (NLP) engine; … to extract an ontological concept from the target content.”
These are insignificant extra-solution activities that are not integrated into the claim as a whole and do not add meaningful limitations to the above-noted mental processes specified in this claim.
That is, “to extract an ontological concept from the source content;” and “to extract an ontological concept from the target content” amount to mere data gathering. (See MPEP 2106.05(g)).
The claim also recites the additional elements: “a processor configured to process target content generated by processing source content using a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content;” “using a natural language processing (NLP) engine” and “using the NLP engine”, which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – “a computing system comprising: a memory; and a processor” performing generic computer function) , which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this claim as a combination does not add anything further than the individual elements.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “extracting an ontological concept from the source content” and “extracting an ontological concept from the target content” is the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d). Also, the “a memory; and” can be characterized as WURC. See also MPEP 2106.05(d).
As an ordered whole, the claim is directed to a method of extracting transmitted and received content/data (WURC), comparing it (mental process - abstract) and identifying an issue based on the comparison and the content (mental process – abstract). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding claim 9, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step1: Claim 9 is directed to a system as depending from claim 8, thus the analysis for patent eligibility of claim 8 is incorporated herein.
Step 2A Prong 1: The claim recites “mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph.”
The mapping the ontological concept, as drafted, under its BRI, covers a concept performed in the human mind (source content can be observed by a human and then evaluation, judgement, or opinion used to do mapping/correlating).. This limitation in the context of this claim, encompasses a mental process to map/correlate concepts from observed content to target desired content in the graph. Dependent claim 9, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim is not directed to an abstract idea. Thus, the limitations do nothing to alter the analysis of claim 8.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element:
“obtaining an ontological graph including a plurality of ontological concepts; and”
This is insignificant extra-solution activity that is not integrated into the claim as a whole and does not add a meaningful limitation to the above-noted mental processes specified in this claim. That is, “obtaining an ontological graph including a plurality of ontological concepts; and,” amounts to mere data gathering. (See MPEP 2106.05(g)).
Step 2B:
The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this claim as a combination does not add anything further than the individual elements.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “extracting an ontological concept from the source content” and “extracting an ontological concept from the target content” are the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of extracting transmitted and received content/data (WURC), comparing it (mental process - abstract) and identifying an issue based on the comparison and the content (mental process – abstract). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding claim 10, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 10 is directed to a system as depending from claim 8, thus the analysis for patent eligibility of claim 8 is incorporated herein.
Step 2A Prong 1: The claim recites “determining a location of the ontological concept from the source content within the ontological graph;
determining a location of the ontological concept for the target content
within the ontological graph; and
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.”
The determining a location from the source content limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above determining a location limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the source data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determining a location from the target content limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above determining a distance limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the target data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determine a distance and a relationship limitation added by this claim covers a mathematical concept (determining the distance and relationship to find the ontological concept comparison score). Such distance and relationship finding of the source and target content uses the ontological concept comparison score for particular task:
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can be done by hand with pen and paper, as suggested by the discussion of this step inside paragraph 56 of the specification, further providing evidence that the claimed distance and relationship finding is for the purpose of reaching a comparison score that is itself a mathematical concept.
Dependent claim 10, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 8.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 11, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 11 is directed to a system as depending from claim 8, thus the analysis for patent eligibility of claim 8 is incorporated herein.
Step 2A Prong 1: The claim recites “an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content.”
The equivalent relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “an equivalent relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing where the claimed relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
The parent-child relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “a parent-child relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed parent-child relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
Dependent claim 11, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 8.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 12, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 12 is directed to a system as depending from claim 8, thus the analysis for patent eligibility of claim 8 is incorporated herein.
Step 2A Prong 1: The claim recites “identifying a hallucination in the target content; and”
“identifying a missing ontological concept in the target content.”
The identifying a hallucination limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying “an ontological concept that is fabricated by generative AI model… (see e.g., paragraph 62 and figure 200 of specification) in the target content). The above limitation in the context of this claim encompasses a “generative AI safeguarding process 10 (which) is able to identify 108 issues automatically without supervision for a particular task by identifying 120 the hallucination.” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper.
The identifying a missing ontological concept limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying missing data). The above limitation in the context of this claim encompasses a “task-based rules engine 222 (which) identifies a “hallucination” in target content 206 (i.e., an ontological concept that is fabricated by generative AI model 200) when there is not a corresponding ontological concept in source content 202” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper.
Dependent claim 12, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 8.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 13, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 13 is directed to a system as depending from claim 8, thus the analysis for patent eligibility of claim 8 is incorporated herein.
Step 2A Prong 1: The claim recites “providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue.”
The limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to provide feedback on different kinds of observed content/data in a received request and an identified issue). The above limitation in the context of this claim encompasses “providing 124 feedback to the generative AI model includes one or more of: processing 126 a request including the source content, the target content, and the hallucination in the target content; and processing 128 a request including the source content, the target content, and the missing ontological concept in the target content” (see e.g., paragraph 65 of specification). More specifically “Since generative AI safeguarding process 10 provides explainable results that can point exactly which ontological concepts in the target/source are missing/hallucinated, generative AI safeguarding process 10 can use this information to improve or fix a generated response by generative AI model 200” (see e.g., paragraph 64 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Dependent claim 13, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 8.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 14, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 14 is directed to a system as depending from claim 13, thus the analysis for patent eligibilities of claim 13 and base claim 8 are incorporated herein.
Step 2A Prong 2: The claim recites “processing a request including the source content, the target content, and the hallucination in the target content; and
processing a request including the source content, the target content, and the missing ontological concept in the target content.”
These limitations amount to mere data gathering.
The judicial exceptions are not integrated into a practical application.
The claim recites “processing a request including the source content, the target content, and the hallucination in the target content; and” This limitation describes data gathering with the request being the input of data. Such statement can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The claim also recites “processing a request including the source content, the target content, and the missing ontological concept in the target content.” This limitation describes data gathering with the request being the input of data. Such statement can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding independent claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claim 15 is directed to a computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations, corresponding to one of the four statutory categories – an article of manufacture.
Step 2A Prong 1: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
The claim recites the following limitations:
“generating an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the ontological graph and the task performed using the source content to generate the target content;
identifying an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content; and” – as drafted, under their broadest reasonable interpretation (BRI), in view of the specification, cover concepts encompassing mental processes (evaluation, judgement, or opinion for the task performed using the source content to generate the target content).
providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue”
The limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion to provide feedback on different kinds of observed content/data in a received request and an identified issue). The above limitation in the context of this claim encompasses “providing 124 feedback to the generative AI model includes one or more of: processing 126 a request including the source content, the target content, and the hallucination in the target content; and processing 128 a request including the source content, the target content, and the missing ontological concept in the target content” (see e.g., paragraph 65 of specification). More specifically “Since generative AI safeguarding process 10 provides explainable results that can point exactly which ontological concepts in the target/source are missing/hallucinated, generative AI safeguarding process 10 can use this information to improve or fix a generated response by generative AI model 200” (see e.g., paragraph 64 of specification). (corresponding to mental processes which can be done mentally or by pen and paper).
Accordingly claim 15 recites an abstract idea.
Step 2A Prong 2:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:
extracting an ontological concept from the source content using a natural
language processing (NLP) engine;
obtaining an ontological graph including a plurality of ontological concepts;
extracting an ontological concept from the target content using the NLP engine;”
These are insignificant extra-solution activities that are not integrated into the claim as a whole and do not add meaningful limitations to the above-noted mental processes specified in this claim.
That is, “extracting an ontological concept from the source content;” “extracting an ontological concept from the target content” amount to mere data gathering. (See MPEP 2106.05(g)).
The claim also recites “A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: processing target content generated by processing source content using a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content;” “using a natural language processing (NLP) engine” and “using the NLP engine” which amount to recitation of the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components - a non-transitory computer readable medium and a processor - performing generic computer function), which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Viewing the additional element of this claim as a combination does not add anything further than the individual elements.
Receiving and communicating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “extracting an ontological concept from the source content” and “extracting an ontological concept from the target content” are the well-understood, routine, conventional (WURC) activity of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
As an ordered whole, the claim is directed to a method of extracting transmitted and received content/data (WURC), comparing it (mental process - abstract) and identifying an issue based on the comparison and the content (mental process – abstract). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding claim 16, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step1: Claim 16 is directed to a computer program product residing on a non-transitory computer readable medium as depending from claim 15, thus the analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: The claim recites “mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph.”
This limitation, as drafted, under their BRI, covers a concept performed in the human mind (source content can be observed by a human and then evaluation, judgement, or opinion used to do mapping/correlating). This limitation in the context of this claim, encompasses a mental process to map/correlate concepts from observed content to target desired content in the graph.
Dependent claim 16, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, this limitation does nothing to alter the analysis of claim 15.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 17, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 17 is directed to a computer program product residing on a non-transitory computer readable medium as depending from claim 15, thus the analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: The claim recites “determining a location of the ontological concept from the source content within the ontological graph;”
“determining a location of the ontological concept for the target content
within the ontological graph; and”
“determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.”
The determining a location from the source content limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the source data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determining a location from the target content limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on the placement of the ontological concept on a grid). The above limitation in the context of this claim encompasses determining the location of the ontological concept with respect to the target data on a graph (corresponding to a mental process which can be done mentally or by pen and paper).
The determining a distance and relationship limitation added by this claim covers a mathematical concept (determining the distance and relationship to find the ontological concept comparison score). Such distance and relationship finding of the source and target content uses the ontological concept comparison score for particular task:
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can be done by hand with pen and paper, as suggested by the discussion of this step inside paragraph 56 of the specification, further providing evidence that the claimed distance and relationship finding is for the purpose of reaching a comparison score that is itself a mathematical concept.
Dependent claim 17, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 15.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 18, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 18 is directed to a computer program product residing on a non-transitory computer readable medium as depending from claim 15, thus the analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: The claim recites “an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content.”
The equivalent relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “an equivalent relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
The parent-child relationship limitation added by this claim merely limits the invention to a narrower abstract idea by reciting “a parent-child relationship.” This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the claimed parent-child relationship is, namely “between the ontological concept from the source content and the ontological concept from the target content.”
Dependent claim 18, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitation fails to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 15.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 19, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 19 is directed to a computer program product residing on a non-transitory computer readable medium as depending from claim 15, thus the analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: The claim recites “identifying a hallucination in the target content; and
identifying a missing ontological concept in the target content.”
The identifying a hallucination limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying “an ontological concept that is fabricated by generative AI model… (see e.g., paragraph 62 and figure 200 of specification) in the target content). The above limitation in the context of this claim encompasses a “generative AI safeguarding process 10 (which) is able to identify 108 issues automatically without supervision for a particular task by identifying 120 the hallucination.” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper.
The identifying a missing ontological concept limitation, as drafted under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion based on identifying missing data). The above limitation in the context of this claim encompasses a “task-based rules engine 222 (which) identifies a “hallucination” in target content 206 (i.e., an ontological concept that is fabricated by generative AI model 200) when there is not a corresponding ontological concept in source content 202” (see e.g., paragraph 62 of specification). This is a mental process which can be done mentally or by pen and paper.
Dependent claim 19, when analyzed as a whole, is not patent eligible under 35 U.S.C. 101 because the additional limitations fail to establish that the claim is not directed to an abstract idea. Thus, these limitations do nothing to alter the analysis of claim 15.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 20, this claim is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 20 is directed to a computer program product residing on a non-transitory computer readable medium as depending from claim 19 and further depending from claim 15, thus the analysis for patent eligibility is incorporated herein.
Step 2A Prong 2: The claim recites “processing a request including the source content, the target content, and the hallucination in the target content; and
processing a request including the source content, the target content, and the missing ontological concept in the target content.”
These limitations amount to mere data gathering.
The judicial exceptions are not integrated into a practical application.
The claim recites “processing a request including the source content, the target content, and the hallucination in the target content; and” This limitation describes data gathering with the request being the input of data. Such statement can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The claim also recites “processing a request including the source content, the target content, and the missing ontological concept in the target content.” This limitation describes data gathering with the request being the input of data. Such statement can be characterized as insignificant extra-solution activity. See MPEP 2106.05(g).
The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-8, 12-15, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang; Jenny Z. (U.S. Publication No. 20230245651, hereinafter “Wang”).
Regarding independent claim 1, Wang discloses the invention as claimed including A computer-implemented method, executed on a computing device, comprising: processing target content generated by processing source content using a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content (see, e.g., paragraphs 501 and 144, “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention” and “Generative AI is a subfield of artificial intelligence that focuses on the creation of new content or data, such as text, images, or audio, based on input data and context.” [i.e., a computer-implemented method to generate/ create new content/target content derived from source content/input data]);
extracting an ontological concept from the source content using a natural
language processing (NLP) engine;
extracting an ontological concept from the target content using the NLP engine (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications … (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., In order to detect inconsistencies/errors, the ontological concepts/additional semantic information of both the source content and the target content are extracted by using the graph engine and NLP engine]);
generating an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the task performed using the source content to generate the target content (see, e.g., paragraph 360, “Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.” [e.g., Comparing contextual information/source and target content for ontological concept, using similarity measures to generate a ranking based off of similarity/ontological concept comparison score]);
identifying an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content (see, e.g., paragraphs 195 and 360, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications … (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” and “the Contextual Matching (CM) method involves several steps. First, contextual information is extracted by collecting and processing relevant data or content from the input or query. Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.”
[e.g., Detecting/identifying an error/issue in the new information/target data from the knowledge graph engine that uses ML and reasoning techniques and context matching to process input/source data for ranking based on similarity/comparison score]).
Regarding claim 5, as discussed above, Wang discloses the method of claim 1.
Wang further discloses identifying a missing ontological concept in the target content (see, e.g., paragraph 125, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filing missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data.” [e.g., Missing ontological concept would have been identified during data cleaning when replacing/filing missing values/concept]);
wherein identifying the issue in the target content includes one or more of: identifying a hallucination in the target content; and (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., identifying errors such as inconsistencies/hallucinations in new information/target content]).
Regarding claim 6, as discussed above, Wang discloses the method of claim 1.
Wang further discloses further comprising: providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue. (see, e.g., paragraph 275, Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics. [e.g., AI system/ generative AI model provides predictions/recommendations/feedback when preparing data for the AI system including missing, inconsistent, or erroneous values]);
Regarding claim 7, as discussed above, Wang discloses the method of claim 6 which depends on claim 1.
Wang further discloses wherein providing feedback to the generative AI model includes one or more of: processing a request including the source content, the target content, and the hallucination in the target content; and (see, e.g., paragraph 275, “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics.” [e.g., AI system/ generative AI model provides predictions/recommendations/feedback when preparing data for the AI system including missing, inconsistent, or erroneous values which are all characteristics of hallucination]);
processing a request including the source content, the target content, and the missing ontological concept in the target content (see, e.g., paragraph 125 and 275, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filling missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data (paragraph 125).” “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics (paragraph 275).” [e.g., Data cleaning finds missing data values/ontological concepts which are involved in the data validation process of training and evaluating AI models where input is processed]).
Regarding independent claim 8, Wang discloses the invention as claimed including A computing system comprising: a memory; and a processor configured to process target content generated by processing source content using a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content, to extract an ontological concept from the source content using a natural language processing (NLP) engine (see, e.g., paragraphs 501 and 144, “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention” and “Generative AI is a subfield of artificial intelligence that focuses on the creation of new content or data, such as text, images, or audio, based on input data and context.” [i.e., a system having a storage medium/memory and processor to generate/ “create new content” (i.e., /target content derived from source content/input data]);
to extract an ontological concept from the source content using a natural language processing (NLP) engine;
to extract an ontological concept from the target content using the NLP engine(see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., In order to detect inconsistencies/errors, the ontological concepts/additional semantic information of both the source content and the target content are extracted by the graph engine used by the NLP]);
to generate an ontological graph including a plurality of ontological concepts using the NLP engine (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., knowledge graph/ontological graph contains semantic information of a plurality of concepts such as, entity types, categories, and hierarchies]);
to generate an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the ontological graph and the task performed using the source content to generate the target content (see, e.g., paragraph 360, “Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.” [e.g., Comparing contextual information/source and target content using similarity measures to generate a ranking based off of similarity/comparison score]);
and to identify an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content (see, e.g., paragraphs 195 and 360, “[0195] … (3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications … (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [0360] In an approach for the Contextual Matching (CM) method involves several steps. First, contextual information is extracted by collecting and processing relevant data or content from the input or query. Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.”
[e.g., Detecting/identifying an error/issue in the new information/target data from the knowledge graph engine that uses ML and reasoning techniques and context matching to process input/source data for ranking based on similarity/comparison score]).
Regarding claim 12, as discussed above, Wang discloses the system of claim 8.
Wang further discloses identifying a missing ontological concept in the target content. (see, e.g., paragraph 125, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filing missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data.” [e.g., Missing ontological concept would have been identified during data cleaning when replacing/filing missing values/concept]);
wherein identifying the issue in the target content includes one or more of: identifying a hallucination in the target content; and (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., identifying errors such as inconsistencies/hallucinations in new information/target content]).
Regarding claim 13, as discussed above, Wang discloses the system of claim 8.
Wang further discloses further comprising: providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue. (see, e.g., paragraph 275, Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics. [e.g., AI system/ generative AI model provides predictions/recommendations/feedback when preparing data for the AI system including missing, inconsistent, or erroneous values]);
Regarding claim 14, as discussed above, Wang discloses the system of claim 13 which depends on claim 8.
Wang further discloses wherein providing feedback to the generative AI model includes one or more of: processing a request including the source content, the target content, and the hallucination in the target content; and (see, e.g., paragraph 275, “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics.” [e.g., AI system/ generative AI model provides predictions/recommendations/feedback when preparing data for the AI system including missing, inconsistent, or erroneous values which are all characteristics of hallucination]);
processing a request including the source content, the target content, and the missing ontological concept in the target content (see, e.g., paragraph 125 and 275, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filling missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data (paragraph 125).” “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics (paragraph 275).” [e.g., Data cleaning finds missing data values/ontological concepts which are involved in the data validation process of training and evaluating AI models where input is processed]).
Regarding independent claim 15, Wang discloses the invention as claimed including A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: processing target content generated by processing source content using
a generative artificial intelligence (AI) model, wherein the generative AI model performs a task using the source content to generate the target content (see, e.g., paragraphs 501 and 144, “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention” and “Generative AI is a subfield of artificial intelligence that focuses on the creation of new content or data, such as text, images, or audio, based on input data and context.” [i.e., a computer program product having a storage medium/memory to generate/ processor and the “create new content” /target content) derived from source content/input data]);
extracting an ontological concept from the source content using a natural language processing (NLP) engine; extracting an ontological concept from the target content using the NLP engine (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., In order to detect inconsistencies/errors, [e.g., In order to detect inconsistencies/errors, the ontological concepts/additional semantic information of both the source content and the target content are extracted by the graph engine used by the NLP]);
obtaining an ontological graph including a plurality of ontological concepts (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., knowledge graph/ontological graph contains semantic information of a plurality of concepts such as, entity types, categories, and hierarchies]);
generating an ontological concept comparison score by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the ontological graph and the task performed using the source content to generate the target content (see, e.g., paragraph 195, “In an approach for the Contextual Matching (CM) method involves several steps. First, contextual information is extracted by collecting and processing relevant data or content from the input or query. Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.” [e.g., Comparing contextual information/source and target content using similarity measures to generate a ranking based off of similarity/comparison score]);
identifying an issue in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content (see, e.g., paragraphs 195 and 360, “[0195] … (3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications … (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [0360] In an approach for the Contextual Matching (CM) method involves several steps. First, contextual information is extracted by collecting and processing relevant data or content from the input or query. Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity. Finally, the results are ranked based on their similarity scores, and the most relevant or contextually appropriate result is selected.”
[e.g., Detecting/identifying an error/issue in the new information/target data from the knowledge graph engine that uses ML and reasoning techniques and context matching to process input/source data for ranking based on similarity/comparison score]); and
providing feedback to the generative AI model by processing a request including the source content, the target content, and the identified issue (see, e.g., paragraphs 96 and 97, “This could involve developing techniques that enable human experts to provide feedback on specific decisions made by the AI model, using this feedback to refine the decision-making process. By including human expertise in this manner, the AI model can become more transparent and explainable, allowing potential issues to be identified and addressed more effectively. Moreover, integrating human-in-the-loop strategies can be valuable in providing insights and improving the model iteratively. This could involve using expert feedback to label or annotate data, validate AI model outputs, or prioritize areas of improvement.” [e.g., Feed back to the AI model provides feedback as insight to improve model through input and output data and potential issues identified]).
Regarding claim 19, as discussed above, Wang discloses the product of claim 15.
Wang further discloses identifying a missing ontological concept in the target content (see, e.g., paragraph 125, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filing missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data.” [e.g., Missing ontological concept would have been identified during data cleaning when replacing/filing missing values/concept]);
wherein identifying the issue in the target content includes one or more of: identifying a hallucination in the target content; and (see, e.g., paragraph 195, “(3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies. This enhances the usability and expressiveness of the knowledge graph for various applications… (5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors.” [e.g., identifying errors such as inconsistencies/hallucinations in new information/target content]).
Regarding claim 20 as discussed above, Wang discloses the product of claim 19 which depends on claim 15.
Wang further discloses wherein providing feedback to the generative AI model includes one or more of: processing a request including the source content, the target content, and the hallucination in the target content; and (see, e.g., paragraph 275, “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics.” [e.g., AI system/ generative AI model provides predictions/recommendations/feedback when preparing data for the AI system including missing, inconsistent, or erroneous values which are all characteristics of hallucination]);
processing a request including the source content, the target content, and the missing ontological concept in the target content (see, e.g., paragraph 125 and 275, “Data cleaning involves several steps and techniques, which may include: removing duplicates, filling missing values, correcting data entry errors, standardizing and transforming data, validating and correcting data, outlier detection and treatment, and merging and integrating data (paragraph 125).” “Ensuring the quality and correctness of the data used for training and evaluating AI models is important. Data validation includes checking for missing, inconsistent, or erroneous values, as well as ensuring data is representative of the problem domain. This process may involve data cleaning, normalization, and transformation techniques to prepare the data for the AI system. (2) Model Validation: This aspect focuses on evaluating the performance of AI models, such as ML or deep learning algorithms, on unseen data… AI system often produce outputs in the form of predictions, recommendations, or decisions. Validating these outputs involves comparing them with ground-truth data or known outcomes, measuring their accuracy, precision, recall, F1-score, or other relevant metrics (paragraph 275).” [e.g., Data cleaning finds missing data values/ontological concepts which are involved in the data validation process of training and evaluating AI models where input is processed]).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-4, 9-11 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang as applied to claims 1, 8, and 15 above, in view of Gardner; Stephen Philip et al. (U.S. Publication No. 20060074836, hereinafter “Gardner”).
Regarding claim 2, as discussed above, Wang discloses the method of claim 1.
However, Wang does not explicitly disclose the following limitations: wherein generating the ontological concept comparison score includes: obtaining an ontological graph including a plurality of ontological concepts;
mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph.
In the same field, analogous art Gardner teaches wherein generating the ontological concept comparison score includes: obtaining an ontological graph including a plurality of ontological concepts (see e.g., paragraph 162, “Multi-relational pane 1601 may display the concepts and relationships of an ontology in a graph representation.” [e.g., ontological graph displays concepts and relationships of ontology]);
mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph (see e.g., paragraph 162, “Multi-relational pane 1601 may display the concepts and relationships of an ontology in a graph representation. A graph representation in multi-relational pane may access the same underlying ontology data as the hierarchical pane, but may show a more complete set of relationships existing therein” [e.g., If ontological graph displays concepts and relationships, those relationships would have been determined after the mapping of ontological content/data onto the graph]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “As each concept within each pair may also be paired (and thus related by multiple descriptive relationships) with other concepts within the ontology, a complex set of logical connections is formed. These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network allows discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, "show me all genes expressed-in liver tissue that-are-associated-with diabetes." (see e.g., Gardner, paragraph 67). Doing so would have allowed Wang to use Gardner’s ontological graph and mapping that displays concepts and relationships to create assertions where a pair of concepts – e.g., target and source content - have a relationship by complex connections to provide a knowledge network which enables the discovery of relationships and concepts and broaden knowledge of multiple domains, as suggested by Gardner (see, e.g., Gardner, paragraphs 67 and 162).
Regarding claim 3, as discussed above, Wang discloses the method of claim 1.
However, Wang does not explicitly disclose the following limitations: wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph;
determining a location of the ontological concept for the target content within the ontological graph; and;
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.
In the same field, analogous art Gardner teaches wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph (see, e.g., paragraph 67, “These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph. It is also mentioned that this knowledge network enables the discovery of relationships between different concepts and concept types such as source data”]);
determining a location of the ontological concept for the target content within the ontological graph; and (see, e.g., paragraph 67, …“These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph. It is also mentioned that this knowledge network enables the discovery of relationships between different concepts and concept types such as target data”]);
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph. (see, e.g., paragraph 134, “According to an embodiment, the document viewer may, for example, enable a user to call up a specific document from a specified corpus that contains a keyword of interest. All of the ontology concepts contained within the document may be presented in a hierarchy pane or display 920, and highlighted or otherwise identified in the text appearing in text display 930. Recognized relationships may also be highlighted or otherwise identified in the text. Where concepts of the correct types are potentially connected by appropriate relationships within a specified distance with a sentence, they may be highlighted or otherwise identified as suggested candidate assertions in a candidate assertion pane or display 940. Existing assertions already in the ontology, and those suggested by the automated text-mining may also be highlighted or otherwise identified.” [e.g., Concept’s relationship connecting concept types and distance between ontological concept involving sentences and text is mentioned]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “Once one or more ontologies are published, they can be used in a variety of ways. For example, one or more users may view one or more ontologies and perform other knowledge discovery processes via a graphical user interface (GUI) as enabled by a user interface module. A path-finding module may enable the paths of assertions existing between concepts of an ontology to be selectively navigated. A chemical support module may enable the storage, manipulation, and use of chemical structure information within an ontology (see e.g., Gardner, paragraph 144).” Doing so would have allowed Wang to use Gardner’s determining, location, distance, and relationship of ontological concept in order to perform knowledge discovery processes with a graphical user interface (GUI) and allow concepts of an ontology to be selectively navigated to a path-finding module, as suggested by Gardner (see, e.g., Gardner, paragraphs 67, 134, and 144).
Regarding claim 4, as discussed above, Wang discloses the method of claim 1.
Wang further discloses a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content. (see, e.g., paragraph 121, Depending on the specific use cases and applications, a training dataset may need to be constructed by gathering data from one or multiple sources or selecting a subset of data from one or more sources. These training datasets can represent one or more subsets of a larger dataset, and the data sources can be available in various formats. [e.g., subset of data of a larger data set implies a parent and child relationship, while one or more sources of data can be source and target data]).
However, Wang does not explicitly disclose wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
In the same field, analogous art Gardner teaches wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and (see, e.g., paragraph 89, “Types of rule-based products may include, for example, tagged document content (including tagged or stored structure information for structured data sources), rules-based assertions, reified assertions, identification of semantically divergent assertions, production or identification of semantically equivalent assertions” [e.g., Between the document content/source content and document content/target content, there is semantic equivalence - the same underlying meaning, even if they differ in form, wording, or structure]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “For example, a semantic equivalence may exist for the concept "heart attack." The concept "myocardial infarction" may be semantically equivalent to the concept "heart attack." As such, these concepts, and certain assertions in which they reside, may be considered equivalent. Conversely, certain terms may have semantically divergent meanings. For example, the term "cold" may refer to the temperature of a substance, or may refer to an infection of the sinuses. As such, contextual and other information may be used to recognize the semantic difference in the term "cold" and treat assertions containing that term accordingly. In some embodiments, an analysis of which relationships can be used to join certain pairs of concepts may be used for semantic normalization” (see, e.g., Gardner, paragraph 119). Doing so would have allowed Wang to use Gardner’s “semantically equivalent assertions for semantic normalization in which analysis of which relationships can be used to join certain pairs of concepts”, as suggested by Gardner (see, e.g., Gardner, paragraphs 89 and 119).
Regarding claim 9, as discussed above, Wang discloses the system of claim 1.
However, Wang does not explicitly disclose the following limitations: wherein generating the ontological concept comparison score includes: obtaining an ontological graph including a plurality of ontological concepts;
mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph.
In the same field, analogous art Gardner teaches wherein generating the ontological concept comparison score includes: obtaining an ontological graph including a plurality of ontological concepts (see e.g., paragraph 162, “Multi-relational pane 1601 may display the concepts and relationships of an ontology in a graph representation.” [e.g., ontological graph displays concepts and relationships of ontology]);
mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph (see e.g., paragraph 162, “Multi-relational pane 1601 may display the concepts and relationships of an ontology in a graph representation. A graph representation in multi-relational pane may access the same underlying ontology data as the hierarchical pane, but may show a more complete set of relationships existing therein” [e.g., If ontological graph displays concepts and relationships, those relationships would have been determined after the mapping of ontological content/data onto the graph]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “As each concept within each pair may also be paired (and thus related by multiple descriptive relationships) with other concepts within the ontology, a complex set of logical connections is formed. These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network allows discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, "show me all genes expressed-in liver tissue that-are-associated-with diabetes." (see e.g., Gardner, paragraph 67) Doing so would have allowed Wang to use Gardner’s ontological graph and mapping, that displays concepts and relationships to create assertions where a pair of concepts – e.g., target and source content - have a relationship by complex connections to provide a knowledge network which enables the discovery of relationships and concepts and broaden knowledge of multiple domains, as suggested by Gardner (see, e.g., Gardner, paragraphs 67 and 162).
Regarding claim 10, as discussed above, Wang discloses the system of claim 8.
However, Wang does not explicitly disclose the following limitations: wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph;
determining a location of the ontological concept for the target content within the ontological graph; and;
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.
In the same field, analogous art Gardner teaches wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph (see, e.g., paragraph 67, “These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph, and the knowledge network enables the discovery of relationships between different concepts and concept types such as source data]);
determining a location of the ontological concept for the target content within the ontological graph; and (see, e.g., paragraph 67, “These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph. It is also mentioned that this knowledge network enables the discovery of relationships between different concepts and concept types such as target data]);
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph. (see, e.g., paragraph 134, “According to an embodiment, the document viewer may, for example, enable a user to call up a specific document from a specified corpus that contains a keyword of interest. All of the ontology concepts contained within the document may be presented in a hierarchy pane or display 920, and highlighted or otherwise identified in the text appearing in text display 930. Recognized relationships may also be highlighted or otherwise identified in the text. Where concepts of the correct types are potentially connected by appropriate relationships within a specified distance with a sentence, they may be highlighted or otherwise identified as suggested candidate assertions in a candidate assertion pane or display 940. Existing assertions already in the ontology, and those suggested by the automated text-mining may also be highlighted or otherwise identified.” [e.g., Concept’s relationship connecting concept types and distance between ontological concept involving sentences and text is mentioned]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “Once one or more ontologies are published, they can be used in a variety of ways. For example, one or more users may view one or more ontologies and perform other knowledge discovery processes via a graphical user interface (GUI) as enabled by a user interface module. A path-finding module may enable the paths of assertions existing between concepts of an ontology to be selectively navigated. A chemical support module may enable the storage, manipulation, and use of chemical structure information within an ontology (see e.g., Gardner, paragraph 144).” Doing so would have allowed Wang to use Gardner’s determining, location, distance, and relationship of ontological concept in order to perform knowledge discovery processes with a graphical user interface (GUI) and enable concepts of an ontology to be selectively navigated to with a path-finding module, as suggested by Gardner (see, e.g., Gardner, paragraph 67, 134, and 144).
Regarding claim 11, as discussed above, Wang discloses the system of claim 8.
Wang further discloses a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content. (see, e.g., paragraph 121, Depending on the specific use cases and applications, a training dataset may need to be constructed by gathering data from one or multiple sources or selecting a subset of data from one or more sources. These training datasets can represent one or more subsets of a larger dataset, and the data sources can be available in various formats. [e.g., subset of data of a larger data set implies a parent and child relationship, while one or more sources of data can be source and target data]).
However, Wang does not explicitly disclose wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
In the same field, analogous art Gardner teaches wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and (see, e.g., paragraph 89, “Types of rule-based products may include, for example, tagged document content (including tagged or stored structure information for structured data sources), rules-based assertions, reified assertions, identification of semantically divergent assertions, production or identification of semantically equivalent assertions” [e.g., Between the document content/source content and document content/target content, there is semantic equivalence - the same underlying meaning, even if they differ in form, wording, or structure]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “For example, a semantic equivalence may exist for the concept "heart attack." The concept "myocardial infarction" may be semantically equivalent to the concept "heart attack." As such, these concepts, and certain assertions in which they reside, may be considered equivalent. Conversely, certain terms may have semantically divergent meanings. For example, the term "cold" may refer to the temperature of a substance, or may refer to an infection of the sinuses. As such, contextual and other information may be used to recognize the semantic difference in the term "cold" and treat assertions containing that term accordingly. In some embodiments, an analysis of which relationships can be used to join certain pairs of concepts may be used for semantic normalization” (see, e.g., Gardner, paragraph 119). Doing so would have allowed Wang to use Gardner’s “semantically equivalent assertions for semantic normalization in which analysis of which relationships can be used to join certain pairs of concepts”, as suggested by Gardner (see, e.g., Gardner, paragraph 89 and 119).
Regarding claim 16, as discussed above, Wang discloses the product of claim 15.
However, Wang does not explicitly disclose further comprising: mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph
In the same field, analogous art Gardner teaches further comprising: mapping the ontological concept from the source content and the ontological concept from the target content onto the ontological graph (see e.g., paragraph 162, “Multi-relational pane 1601 may display the concepts and relationships of an ontology in a graph representation. A graph representation in multi-relational pane may access the same underlying ontology data as the hierarchical pane, but may show a more complete set of relationships existing therein” [e.g., If ontological graph displays concepts and relationships, those relationships would have been determined after the mapping of ontological content/data onto the graph]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “As each concept within each pair may also be paired (and thus related by multiple descriptive relationships) with other concepts within the ontology, a complex set of logical connections is formed. These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network allows discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, "show me all genes expressed-in liver tissue that-are-associated-with diabetes." (see e.g., Gardner, paragraph 67). Doing so would have allowed Wang to use Gardner’s ontological graph which upon mapping on the graph, displays concepts and relationships to create assertions where a pair of concepts – e.g., target and source content - have a relationship by complex connections providing a knowledge network which enables the discovery of relationships and concepts and broadens knowledge of multiple domains, as suggested by Gardner (see, e.g., paragraphs 67 and 162).
Regarding claim 17, as discussed above, Wang discloses the product of claim 15.
However, Wang does not explicitly disclose the following limitations: wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph;
determining a location of the ontological concept for the target content;
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph.
In the same field, analogous art Gardner teaches wherein generating the ontological concept comparison score includes: determining a location of the ontological concept from the source content within the ontological graph (see, e.g., paragraph 67, “These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph. It is also mentioned that this knowledge network enables the discovery of relationships between different concepts and concept types such as source data]);
determining a location of the ontological concept for the target content within the ontological graph; and (see, e.g., paragraph 67, “These complex connections provide a comprehensive "knowledge network" of what is known directly and indirectly about concepts within a single domain. The knowledge network may also be used to represent knowledge between and among multiple domains. This knowledge network enables discovery of complex relationships between the different concepts or concept types in the ontology. The knowledge network also enables, inter alia, queries involving both direct and indirect relationships between multiple concepts such as, for example, ‘show me all genes expressed-in liver tissue that-are-associated-with diabetes.’” [e.g., “show me all genes expressed-in liver tissue that-are-associated-with diabetes” is a statement for locating an ontological concept in a knowledge network/ontological graph. It is also mentioned that this knowledge network enables the discovery of relationships between different concepts and concept types such as target data]);
determining a distance and a relationship type between the ontological concept from source content and the ontological concept for the target content within the ontological graph. (see, e.g., paragraph 134, “According to an embodiment, the document viewer may, for example, enable a user to call up a specific document from a specified corpus that contains a keyword of interest. All of the ontology concepts contained within the document may be presented in a hierarchy pane or display 920, and highlighted or otherwise identified in the text appearing in text display 930. Recognized relationships may also be highlighted or otherwise identified in the text. Where concepts of the correct types are potentially connected by appropriate relationships within a specified distance with a sentence, they may be highlighted or otherwise identified as suggested candidate assertions in a candidate assertion pane or display 940. Existing assertions already in the ontology, and those suggested by the automated text-mining may also be highlighted or otherwise identified.” [e.g., Concept’s relationship connecting concept types and distance between ontological concept involving sentences and text is mentioned]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “Once one or more ontologies are published, they can be used in a variety of ways. For example, one or more users may view one or more ontologies and perform other knowledge discovery processes via a graphical user interface (GUI) as enabled by a user interface module. A path-finding module may enable the paths of assertions existing between concepts of an ontology to be selectively navigated. A chemical support module may enable the storage, manipulation, and use of chemical structure information within an ontology (see e.g., Gardner, paragraph 144).” Additionally, doing so would have allowed Wang to use Gardner’s determining, location, distance, and relationship of ontological concept in order to perform knowledge discovery processes with a graphical user interface (GUI) or even have concepts of an ontology be selectively navigated with a path-finding module (see, e.g., Gardner, paragraph 67, 134, and 144).
Regarding claim 18, as discussed above, Wang discloses the product of claim 15.
Wang further discloses a parent-child relationship between the ontological concept from the source content and the ontological concept from the target content. (see, e.g., paragraph 121, Depending on the specific use cases and applications, a training dataset may need to be constructed by gathering data from one or multiple sources or selecting a subset of data from one or more sources. These training datasets can represent one or more subsets of a larger dataset, and the data sources can be available in various formats. [e.g., subset of data of a larger data set implies a parent and child relationship, while one or more sources of data can be source and target data]).
However, Wang does not explicitly disclose wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and
wherein the ontological concept comparison score includes one or more of: an equivalent relationship between the ontological concept from the source content and the ontological concept from the target content; and (see, e.g., paragraph 89, “Types of rule-based products may include, for example, tagged document content (including tagged or stored structure information for structured data sources), rules-based assertions, reified assertions, identification of semantically divergent assertions, production or identification of semantically equivalent assertions” [e.g., Between the document content/source content and document content/target content, there is semantic equivalence - the same underlying meaning, even if they differ in form, wording, or structure]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Gardner so that “For example, a semantic equivalence may exist for the concept "heart attack." The concept "myocardial infarction" may be semantically equivalent to the concept "heart attack." As such, these concepts, and certain assertions in which they reside, may be considered equivalent. Conversely, certain terms may have semantically divergent meanings. For example, the term "cold" may refer to the temperature of a substance, or may refer to an infection of the sinuses. As such, contextual and other information may be used to recognize the semantic difference in the term "cold" and treat assertions containing that term accordingly. In some embodiments, an analysis of which relationships can be used to join certain pairs of concepts may be used for semantic normalization” (see, e.g., Gardner, paragraph 119). Doing so would have allowed Wang to use Gardner’s “semantically equivalent assertions for semantic normalization in which analysis of which relationships can be used to join certain pairs of concepts”, as suggested by Gardner (see, e.g., Gardner, paragraphs 89 and 119).
Conclusion
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/STEVEN PENG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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