NON-FINAL REJECTION, SECOND DETAILED ACTION
Status of Prosecution
The present application 18/477,817, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on September 29,2023 and is a continuation in part of application 18/116,176, filed on March 1, 2023 which in turns turn claims priority to provisional application 63/468,192 filed May 22, 2023.
A requirement for restriction/election, first detailed action was mailed on May 28, 2026. Applicant responded with an election with traverse on July 28, 2026.
Claims 1-24 are pending and are all rejected. Claims 1 and 21, and 23 are independent claims.
Status of Claims
Claims 1-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 5, 15 and 20 are rejected under 35 USC § 112(b) for indefiniteness.
Claims 1-3, 9-12, 14-15, 20-21 and 23-24 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published in November 5, 2020.
Claims 4-5, 17-19 and 22 are rejected under 35 U.S.C. § 103 as being unpatentable over REKS in view of Paturi in further view of Oh et al. (“Oh”), United States Patent Application Publication 2011/0087656 published on April 14, 2011.
Claims 6-8 are rejected under 35 U.S.C. § 103 as being unpatentable over REKS in view of Paturi in further view of non-patent literature, Peng et al. (“Peng”), “Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback,” published on Mar. 8, 2023.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over REKS in view of Paturi in further view of Castelli et al. (“Castelli”), United States Patent 10,366, 160 published on July 30, 2019.
Claim 16 is rejected under 35 U.S.C. § 103 as being unpatentable REKS in view of Paturi in further view of Tibbs et al. (“Tibbs”), United States Patent Application Publication 2005/0256819 published on Nov. 17, 2005.
Restriction/Election Requirement Withdrawn
Examiner has reconsidered the restriction/election requirement in the last Office Action and has withdrawn it.
Specification Objection
The title of the invention is not descriptive. It presently is “Artificial Intelligence Enhanced Knowledge Framework.” A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation – 35 USC § 112(f)
The following is a quotation of 35 U.S.C. § 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: session unit, machine learning unit, and knowledge framework unit in claim 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. § 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5, 15 and 20 are rejected under 35 USC § 112(b) for indefiniteness.
The terms “trustworthy” and “untrustworthy in claim 5 are relative terms which renders the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 15 recites “another document” which appears to lack antecedent basis or definiteness.
Claim 20 recites “understandable” which is a relative term that renders the claim indefinite. It also recites “other unites” which has no antecedent basis.
Correction or clarification is required.
Claim Rejections – § 101 Subject Matter Eligibility
Claims 1-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 1, at step 1, the claim recites a computer-implemented system, and therefore is an material or manufacture, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites a computer-implemented system for development and use of a knowledge framework.
The following limitations are the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C):
create or enhance the knowledge framework based on the machine learning data and the session data; and
create additional machine learning data using the knowledge framework as a source of information.
Therefore, the claim recites at least one abstract idea per this part of the analysis.
At step 2A prong 2, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d). The limitations
a memory including computer program code configured to, when executed, cause the one or more processors to perform the steps above as well as
receive session data related to responses received from a participant in a session;
receive machine learning data.
that are under its broadest reasonable interpretation, is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, specifically model training. See MPEP §§ 2106.04(d), 2106.05(h).
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 therefore directed to an abstract idea.
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional amount to nothing more than linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Therefore, claim 1 is ineligible.
As to the dependent claims 2-20, the analysis of the parent claim is incorporated.
For some elements, such as the calculation of filtering information (e.g. claims 4-5,) verifying machine data (claims 6-7) or determining base scores and adjustments (e.g. claims 17-19) are also abstract ideas of mathematical calculations or in the alternative mental processes. See MPEP § 2106.04(a)(2)(III)(D).
In the step 2A, prong 2 analysis, there are additional limitations that are either additional elements that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g) or additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). Furthermore, many of the additional elements are directed to receiving or transmitting data over a network and storing and retrieving information in memory which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
The claim is also ineligible.
As to independent claim 21 and 23, the analysis of claim 1 is incorporated. Where it differs is in the step zero analysis, they are statutory as well
As to dependent claims 22 and 24, they are similarly rejected as to the related dependent claims.
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
A.
Claims 1-3, 9-12, 14-15, 20-21 and 23-24 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published in November 5, 2020.
As to Claim 1, REKS teaches or suggests: A computer-implemented system for development and use of a knowledge framework, the system comprising:
one or more processors; and
a memory including computer program code configured to, when executed, cause the one or more processors to:
receive session data (REKS: Fig. 2, Sec. III.B, session information is received and input into the system);
receive machine learning data (REKS: Fig. 2, Sec. III.A, from Basic Models an anonymous session Se may be outputted and received in the system (i.e. machine learning data));
create or enhance the knowledge framework based on the machine learning data and the session data (REKS: Fig. 2, Sec. III.B, a knowledge graph is constructed using information from the session data and machine learning data; and
create additional machine learning data using the knowledge framework as a source of information (REKS: Fig. 2, Sec. III.B, “Secondly, we formulate our problem as a Markov decision process to simultaneously tackle recommendation and explanation tasks, where we design a policy network to also consider session information besides the KG information, and propose an appropriate reward function to moderately address our tasks.”)
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REKS may not explicitly teach: one or more processors; and
a memory including computer program code configured to, when executed, cause the one or more processors to perform the steps recited above and
receive session data related to responses received from a participant in a session.
While REKS does teach that experimental data is used that is from user data, it is not necessarily from a participant the session data related to responses from a participant in a session for the particular system (REKS: Sec. IV.A, the data sets in the experimental setup have datasets consisting of both user-item interaction records and the meta information of users and items). Paturi teaches in general concepts related to systems, methods and apparatuses for calculating the likelihood of a cyberattack on a target based on its security posture (Paturi: Abstract). Specifically, Paturi teaches that processors and related instructions on memory are used to implement the teachings (Paturi: par. 0276). An event capture module captures user activity and communicates with a knowledge based and also with a deduction module to help process and create the predictions (Paturi: Fig. 17, par. 0052).
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It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified REKS by allowing session data to be captured from user activity during sessions as taught and suggested by Paturi. Such a person would have done so with an expectation for success to allow for the user interactions to be accounted for in the prediction system for effective use.
As to Claim 2, REKS and Paturi teach the limitations of claim 1.
Paturi further teaches: wherein the one or more processors include a session unit, a machine learning unit, and a knowledge framework unit, wherein the session unit is configured to generate the session data (Paturi: Fig. 17, event detection module) , wherein the machine learning unit is configured to generate the machine learning data (Paturi: deduction module), and wherein the knowledge framework unit (Paturi: Fig. 17, MLN management module) is configured to develop the knowledge framework by receiving the machine learning data from the machine learning unit, receiving the session data from the session unit, and creating or enhancing the knowledge framework based on the machine learning data and the session data.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have implemented the REKS-Paturi device and techniques by allowing the Paturi software modules to perform REKS’ functions as claimed. Such a person would have done so with an expectation for success to allow for the successful implementation of the system with well-known design principles.
As to Claim 3, REKS and Paturi teach the limitations of claim 1.
REKS and Paturi further teaches: wherein the knowledge framework is iteratively enhanced based on the machine learning data and the session data (REKS: as seen in Fig. 2, the data is iteratively used to improve the framework; Paturi: par. 0129, “Particularly desirable results can be obtained if the learning mode is always enabled because it allows this embodiment of the present invention to iteratively self-enhance the knowledge base.”).
As to Claim 9, REKS and Paturi teach the limitations of claim 1.
Paturi further teaches: wherein the knowledge framework comprises an ontology or a taxonomy wherein creating or enhancing the knowledge framework is performed by evolving the ontology or the taxonomy within the knowledge framework unit based on the machine learning data (Paturi: par. 0004, “the present invention implements an intelligent learning loop using artificial intelligence that creates an ontology-based knowledge base from application request and response sequences”).
As to Claim 10, REKS and Paturi teach the limitations of claim 1.
REKS further teaches: wherein the computer program code is configured to, when executed, cause the one or more processors to:
receive input data from at least one external source; and classify the input data to form classified input data for use in the knowledge framework (Paturi: par. 0172, “the content classified by its priority (using the markup structure) in the target web application. This classification of content in the concept hierarchy is a key criterion used for ontology matching”).
As to Claim 11, REKS and Paturi teach the limitations of claim 10.
Paturi further teaches: wherein the computer program code is configured to, when executed, cause the one or more processors to: transform the classified input data into a different format for use in the knowledge framework (Paturi: par. 0170-71, a machine readable format may used from the RDF).
As to Claim 12, REKS and Paturi teach the limitations of claim 11.
Paturi further teaches: wherein the computer program code is configured to, when executed, cause the one or more processors to: transform the classified input data so that the classified input data semantically aligns with language of a taxonomy or an ontology in the knowledge framework (Paturi: pars. 0166-67, the semantic similarity module establishes the semantic meaning of the content; par. 0172, ontology derivation process includes ontology matchin and converting ontology to predicate logic representation which utilizes concept hierarchy which has the semantic meaning earlier).
As to Claim 14, REKS and Paturi teach the limitations of claim 10.
REKS and Paturi as combined further teaches: wherein the knowledge framework is created or enhanced based on the machine learning data, the session data, and the classified input data (Examiner notes that the combination and use of the software modules as noted similarly in claim 2 would allow for the creation or enhancement of the framework as combined).
As to Claim 15, REKS and Paturi teach the limitations of claim 14.
Paturi further teaches: wherein the input data includes data from one or more external sources, and wherein the input data includes data related to at least one of a domain, a stakeholder, an assessment, an opportunity, a use case, a challenge, a capability maturity level, a session focus, a survey focus, a guidance focus, an insight focus, a data interpretation focus, a foundational models focus, an external web source (Paturi: par. 0090, publicly available security incident databases), a standard, a framework, a best practice, a regulation, a taxonomy, an ontology, a lexicon, a machine learning corpus, or another document.
As to Claim 20, REKS and Paturi teach the limitations of claim 1.
REKS further teaches: wherein the knowledge framework has components that represent knowledge understandable by both humans and computers, and wherein the knowledge framework provides a contextual interpretation of data provided to the knowledge framework by other units (REKS: Introduction, explainable and non-explainable session recommendations are discussed and the REKS framework that allows for human-readable information).
As to Claim 21, it is rejected for similar reasons as claim 1.
As to Claim 23, it is rejected for similar reasons as claim 1.
As to Claim 24, it is rejected for similar reasons as claim 4.
B.
Claims 4-5, 17-19 and 22 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published on November 5, 2020 in further view of Oh et al. (“Oh”), United States Patent Application Publication 2011/0087656 published on April 14, 2011.
As to Claim 4, REKS and Paturi teach the limitations of claim 1.
REKS and Paturi may not explicitly teach: wherein the computer program code is configured to, when executed, cause the one or more processors to: filter the machine learning data and the session data before use of the machine learning data and the session data in creating or enhancing the knowledge framework.
Oh teaches in general concepts related to question answering based on answer trustworthiness (Oh: Abstract). Specifically, a knowledgebase has documents that have filtered trustworthy documents stored within (Oh: par. 0078, documents meeting a threshold value of trustworthiness are stored).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by limiting the data into the framework as taught and suggested by Paturi. Such a person would have done so with an expectation for success to allow for reducing unneeded data to be considered and optimizing the system as a whole (Oh: par. 0078).
As to Claim 5, REKS, Paturi and Oh teach the limitations of claim 4.
Oh further teaches: wherein the machine learning data and the session data are filtered by identifying data that is trustworthy and data that is untrustworthy, wherein only the data that is trustworthy is used to create or enhance the knowledge framework (Oh: par. 0078, documents meeting a threshold value of trustworthiness are stored).
As to Claim 17, REKS and Paturi teach the limitations of claim 1.
REKS and Paturi may not explicitly teach: wherein the computer program code is configured to, when executed, cause the one or more processors to:
receive at least one response;
determine a base score for the at least one response;
determine one or more scoring adjustments; and
determine a weighted score for the at least one response based on the base score and the one or more scoring adjustments.
Oh teaches in general concepts related to question answering based on answer trustworthiness (Oh: Abstract). Specifically, a knowledgebase has documents that have filtered trustworthy documents stored within (Oh: par. 0078, documents meeting a threshold value of trustworthiness are stored). Scoring is conducted to also rank answers (Oh: par. 0016). The trustworthiness is calculated with weights for each answer candidate which represents the semantic relatedness between a question and an answer candidate (Oh: eq. 1, par. 0068).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by implementing scoring and ranking based on the trustworthiness adjustments as taught and suggested by Oh. Such a person would have done so with an expectation for success to allow for reducing unneeded data to be considered and optimizing the system as a whole (Oh: par. 0078).
As to Claim 18, REKS, Paturi and Oh teach the limitations of claim 17.
Oh further teaches: wherein the one or more scoring adjustments includes at least one of an importance level scoring adjustment based on an importance level of the at least one response, a trustworthiness scoring adjustment based on a trustworthiness of the at least one response (Oh: eq. 1, par. 0068), or a certainty scoring adjustment based on an uncertainty level of the at least one response.
As to Claim 19, REKS, Paturi and Oh teach the limitations of claim 17.
Oh further teaches: wherein creating or enhancing the knowledge framework is performed using the weighted score for the at least one response (Oh: par. 0078, the trustworthiness values are used to determine which documents are stored in the knowledge base).
As to Claim 22, it is rejected for similar reasons as claim 17.
C.
Claims 6-8 are rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published on November 5, 2020 in further view of non-patent literature, Peng et al. (“Peng”), “Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback,” published on Mar. 8, 2023.
As to Claim 6, REKS and Paturi teach the limitations of claim 1.
REKS and Paturi may not explicitly teach: wherein the computer program code is configured to, when executed, cause the one or more processors to:
verify further machine learning data using the knowledge framework.
Peng teaches in general concepts related to an LLM-Augmenter system which augments a black-box LLM with a plug and play modules (Peng: Abstract). Specifically, Peng teaches that it is able to verify candidate responses in an against an LLM (Peng: Fig. 2, Sec. 1, the candidate response is checked whether it hallucinates evidence and generates feedback message).
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It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by verifying data using the LLM (i.e. knowledge framework) as taught and suggested by Peng. Such a person would have done so with an expectation for success to allow for reduction of hallucinations in the output (Peng: Abstract).
As to Claim 7, REKS, Paturi and Peng teach the limitations of claim 6.
Peng further teaches: wherein verifying the further machine learning data using the knowledge framework is performed automatically and periodically (Peng: Title, Fig. 1, the feedback is automated).
As to Claim 8, REKS and Paturi teach the limitations of claim 1.
REKS and Paturi may not explicitly teach: wherein the knowledge framework is a large language model.
Peng teaches in general concepts related to an LLM-Augmenter system which augments a black-box LLM with a plug and play modules (Peng: Abstract). Specifically, Peng teaches that it is able to verify candidate responses in an against an LLM (Peng: Fig. 2, Sec. 1, the candidate response is checked whether it hallucinates evidence and generates feedback message).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by verifying data using the LLM (i.e. knowledge framework) as taught and suggested by Peng. Such a person would have done so with an expectation for success to allow for reduction of hallucinations in the output (Peng: Abstract).
D.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published on November 5, 2020 in further view of Castelli et al. (“Castelli”), United States Patent 10,366, 160 published on July 30, 2019.
As to Claim 13, REKS and Paturi teach the limitations of claim 11.
REKS and Paturi may not explicitly teach: wherein the session data comprises a participant response, wherein the computer program code is configured to, when executed, cause the one or more processors to: assess whether a topic taxonomy instance is applicable to the participant response; and search for a second topic taxonomy instance to identify a match for the participant response.
Castelli teaches in general concepts related to assisting users by identifying concepts in a conversation and matching the concepts in a knowledge base(Castelli: Abstract). Specifically, Castelli teaches that the matched concepts are based on attributes in a taxonomy within the knowledge base (Castelli: cl. 1).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by assessing a taxonomy within the knowledge framework with matched concepts as taught and suggested by Castelli. Such a person would have done so with an expectation for success to allow for maximization of best matches using taxonomic principles with the knowledge base.
E.
Claim 16 is rejected under 35 U.S.C. § 103 as being unpatentable over non-patent literature Wu et al., “A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation,” (“REKS”) published in 2023 in view of Paturi et al. (“Paturi”), United States Patent Application Publication 2020/0351298 published on November 5, 2020 in further view of Tibbs et al. (“Tibbs”), United States Patent Application Publication 2005/0256819 published on Nov. 17, 2005.
As to Claim 16, REKS and Paturi teach the limitations of claim 1.
REKS further teaches: wherein the session data comprises an ontology or a taxonomy (Paturi: par. 0004, “the present invention implements an intelligent learning loop using artificial intelligence that creates an ontology-based knowledge base from application request and response sequences”).
REKS and Paturi may not explicitly and wherein the ontology or the taxonomy guide a client session.
Tibbs teaches in general concepts related to defining system behavior by selecting optimal actions via reasoning about a system self knowledge (Tibbs: Abstract). Specifically, Tibbs teaches a generic control framework for sequencing through knowledge categories with specific application knowledge to guide the selection of a system response (Tibbs: par. 0017, specific domain ontologies are associated with each category of knowledge).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the invention to have modified the REKS-Paturi techniques and devices by using the ontology to guide the selection of a system response as taught and suggested by Tibbs. Such a person would have done so with an expectation for success to allow for versatile use of the ontologies in different applications(Tibbs: par. 0017, “This capability to have a generic control framework combined with the use of specific domain ontologies gives the SAF the ability to be used in many different applications.”).
Conclusion
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/JAMES T TSAI/Primary Examiner, Art Unit 2147
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