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 . 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to Application
18/769,158 - filed on 07/10/2024.
Priority
The Examiner has noted the Applicant claiming Priority from Provisional Application 63/512,812 filed 7/10/2023.
Status of Claims
Claims 1-22 are currently pending of which:
Claims 1-22 are currently under examination and have been rejected as follows.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Interpretation
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 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 limitations use 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.
In this instant case:
Claim 1 is a method claim which recites:
“[..] an extraction module comprising an AI data extraction agent configured to identify and extract raw digital data relevant to the educational organization from multiple digital data sources;
“a transform module comprising an AI data transformation agent configured to receive the raw digital data from the extraction module and transform the raw digital data into transformed digital data having a usable form within the computer-implemented educational platform;
“an integration module comprising an AI data integration agent configured to integrate the transformed data from the transform module into a digital knowledge base; and
“a manager module comprising an AI agent in charge that coordinates workflow of and dataflow between the extraction module, the transform module, and the integration module” [bolded emphasis added].
The Examiner interprets “extraction module”, “AI data extraction agent”, “transform module”, “AI data transformation agent”, “integration module”, “AI data integration agent”, “manager module”, and “AI agent in charge” as generic placeholders followed by their respective functions of: “identify”, “receive”, “integrate”, and “coodinates”, and further not modified by sufficient structure. Thus, it appears that independent claim 1 and dependent claims 2-22 invoke 35 USC 112(f).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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 § 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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-15, 17-22 are directed to a system or machine which is a statutory category.
Claim 16 is directed to a method or process which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), including: “…assistant for … an educational organization”, “identify and extract raw… data relevant to the educational organization”, “receive the raw digital data… and transform the raw digital data into transformed digital data having a usable form within the… educational platform”, “integrate the transformed data… into a digital knowledge base”, and “coordinates workflow”. Assisting students in an educational environment by collecting, transforming, standardizing, and integrating data into a knowledge base falls within managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claims 1, 16, 17 recite the following additional elements: “artificial intelligence (AI) assistant”, “computer-implemented educational platform”, “extraction module”, “AI data extraction agent”, “transform module”, “AI data transformation agent”, “integration module”, “AI data integration agent”, “digital knowledge base”, “manager module”, “AI agent in charge”, “user interface”, “computer system”, “processors”, and “digital memory storage”. The functions of these additional elements include examples such as “identify and extract raw digital data”, “receive the raw digital data”, “transform the raw digital data into transformed digital data”, “integrate the transformed data… into a digital knowledge base”, and “coordinates workflow of and dataflow between [the modules]”, “receive queries from the user interface”, “retrieve relevant information responsive to the queries”, and “deliver the relevant information to the user”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of collecting, transforming, standardizing, formatting, organizing, storing, retrieving, and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claim 6 recites the additional elements “internet sites”, “application programming interfaces”, and “digital databases”. Dependent claim 14 recites the additional element “data storage systems”. Dependent claims 18-20 recite the additional element “database”. Dependent claims 21-22 recites the additional element “query intelligent query module”. The functions of these additional elements include examples such as providing source data, storing data, receiving user queries, and retrieving and delivering relevant information. The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of collecting, organizing, storing, retrieving, and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components.
Further, dependent claims 2-5, 7-13, 15 merely incorporate the additional elements recited in claims 1, 16, 17 along with further narrowing of the abstract idea of claims 1, 16, 17 and their execution of the abstract idea. Specifically, the dependent claims narrow the “artificial intelligence (AI) assistant”, “computer-implemented educational platform”, “extraction module”, “AI data extraction agent”, “transform module”, “AI data transformation agent”, “integration module”, “AI data integration agent”, “digital knowledge base”, “manager module”, “AI agent in charge”, “user interface”, “computer system”, “processors”, and “digital memory storage” to capabilities such as automate processing, comprise, supervise, monitor, coordinate, validate, verify, cross-check, error-check, clean, enrich, aggregate, standardize, map, store, deliver, and load various forms of data such as knowledge and queries related to knowledge etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-22 are reasoned to be patent ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Ameri, Abbas US 20210073654 A1, hereinafter Ameri in view of
Cooley et al. WO 2022103820 A1, hereinafter Cooley. As per,
Regarding claim 1: Ameri teaches:
A computer-implemented artificial intelligence (AI) assistant for a computer-implemented educational platform for an educational organization, the computer-implemented educational platform formed by one or more sets of software instructions executed by a computer system (See Ameri Fig. 10 element 2630 ‘Education Advisor Agent/App’ and related text. Ameri ¶ [0059]: In general other content-intensive professional service organizations like educational institutions… who are faced with ever growing client demands from their limited supply of staff, domain experts and customer support centers, as part of their routine operation produce and consume content within their domain to offer their high value expertise and knowledge services to their clients. Ameri ¶ [0005]: … a user is assisted with an Artificial Intelligence (AI) enabled digital assistant ("agent") (300), searching for information (350)), the AI assistant comprising:
[..]
[..] integrate the transformed data from the transform module into a digital knowledge base (Ameri ¶ [0084]: This enhanced digital knowledge exploration method changes the way computers process raw content [EN: integrate] into a refined well annotated knowledge base that can be indexed and search with relevant highly connected topics resulting search engine optimization plans, reflecting core topics and relationships in a specific knowledge domain, reflecting needs and languages of members of target communities. [0150] Conclude this instance of continuous improvement and deployment of the knowledge refinery as a new released version of adapted, refined and enriched knowledge bases (3750)); and
a manager module comprising an AI agent in charge that coordinates workflow of and dataflow between the extraction module, the transform module, and the integration module (See Ameri Digital Knowledge Refinery and KCS Workflow Algorithm [EN: agent in charge] Figs. 16-19 and related text. ¶ [0131]: “Knowledge Refinery" (FIG. 10, 2680) is an online automated pipelined workflow, collaborative, decentralized data processing service distributing work packages among human and digital actors are automatically deciphered and processed into digital packages of auto classified, annotated, meaningful machine-readable units with unique universal identifiers (see FIGS. 17 and 18)).
Although Ameri teaches an education-related AI assistant system which extracts, transforms, and integrates data into a searchable knowledge base, Ameri does not specifically teach individual extraction, transform, and integration modules.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
an extraction module comprising an AI data extraction agent configured to identify and extract raw digital data relevant to the educational organization from multiple digital data sources (Cooley ¶ [014]: …a data extraction module retrieving data from at least one of the identified data sources…. In some embodiments, the data extraction module retrieves the data on a schedule, in response to an event, or a combination thereof. In some embodiments, the first algorithm comprises, a logic schema, a set of rules, a machine learning model, a statistical model, or a combination thereof);
a transform module comprising an AI data transformation agent configured to receive the raw digital data from the extraction module and transform the raw digital data into transformed digital data having a usable form within the computer-implemented educational platform (Cooley ¶ [005]: Described herein are platforms, systems, and methods that automatically discover, extract, map, merge [EN: transform], and enrich data found in systems on-premises in automated industrial and commercial environments and cloud systems for purposes of providing developers access to normalized, merged, and enriched [EN: usable] data through an API. (Cooley Abstract: The platforms, systems, and methods … apply a first algorithm [EN: transform module] to map the retrieved data to a predetermined ontology; merge the mapped data into a data store comprising timeseries of the mapped data);
an integration module comprising an AI data integration agent configured to integrate the transformed data [..] (Cooley Abstract: The platforms, systems, and methods… apply a second algorithm [EN: integration module] to identify patterns in the merged data and enriching the data based on one or more identified patterns…).
Cooley and Ameri are found as analogous art of data integration and knowledge refinement. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Ameri’s knowledge currency system and method to have included Cooley’s teachings around individual extraction, transform, and integration modules. The benefit of these additional features would have increased in productivity, accuracy, flexibility, and reduced cost with automation (Cooley ¶ [002]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Ameri in view of Cooley (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of data integration and knowledge refinement. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Ameri in view of Cooley above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 2: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein [..] the manager module have a hierarchical structure that automates data processing from extraction to transformation to integration (See Ameri Digital Knowledge Refinery and KCS Workflow Algorithm [EN: manager module] Figs. 16-19 and related text. ¶ [0131]: “Knowledge Refinery" (FIG. 10, 2680) is an online automated pipelined workflow, collaborative, decentralized data processing service distributing work packages among human and digital actors are automatically deciphered and processed into digital packages of auto classified, annotated, meaningful machine-readable units with unique universal identifiers (see FIGS. 17 and 18)).
Although Ameri teaches a central algorithm which hierarchically manages extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach individual extraction, transform, and integration modules.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the extraction module, the transform module, the integration module… automates data processing from extraction to transformation to integration (Cooley ¶ [014]: …a data extraction module retrieving data from at least one of the identified data sources…. Abstract: The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; retrieve data from at least one of the identified data sources; apply a first algorithm [EN: transform module] to map the retrieved data to a predetermined ontology; merge the mapped data into a data store comprising timeseries of the mapped data; apply a second algorithm [EN: integration module] to identify patterns in the merged data and enriching the data based on one or more identified patterns…).
Rationales to have modified / combined Ameri / Cooley are above and reincorporated.
Regarding claim 3: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein at least one of the extraction module, the transform module, and the integration module comprises a plurality of AI data extraction agents, AI data transformation agents, or AI data integration agents, respectively (Ameri ¶ [0033]: Sub-figure (2)-a more sophisticated scenario, where a human is assisted with an Artificial Intelligence (AI) enabled digital assistant ("agent") (300), searching for [EN: extracting] information (350)).
Regarding claim 4: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein [..] the manager module are distinct from each other and do not have overlapping tasks relative to each other (See Ameri Digital Knowledge Refinery and KCS Workflow Algorithm [EN: manager module] Figs. 16-19 and related text. ¶ [0131]: “Knowledge Refinery" (FIG. 10, 2680) is an online automated pipelined workflow, collaborative, decentralized [EN: distinct from the other modules] data processing service).
Although Ameri teaches a decentralized algorithm which hierarchically manages extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach individual extraction, transform, and integration modules with distinct tasks.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the extraction module, the transform module, the integration module [..] are distinct from each other and do not have overlapping tasks relative to each other (See Cooley description of separate tasks for each component: ¶ [014]: …a data extraction module retrieving data from at least one of the identified data sources…. Abstract: … apply a first algorithm [EN: transform module] to map the retrieved data to a predetermined ontology; merge the mapped data into a data store comprising timeseries of the mapped data; apply a second algorithm [EN: integration module] to identify patterns in the merged data and enriching the data based on one or more identified patterns…).
Rationales to have modified / combined Ameri / Cooley are above and reincorporated.
Regarding claim 5: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the manager module supervises, monitors, and coordinates functioning of the extraction module, the transform module, and the integration module to ensure a smooth and continuous workflow, with each of the extraction module, the transform module, and the integration module passing on data in a streamlined manner (See Ameri Digital Knowledge Refinery and KCS Workflow Algorithm [EN: manager module] Figs. 16-19 and related text. ¶ [0131]: “Knowledge Refinery" (FIG. 10, 2680) is an online automated pipelined [EN: streamlined] workflow, collaborative, decentralized data processing service distributing work packages among human and digital actors are automatically deciphered and processed into digital packages of auto classified, annotated, meaningful machine-readable units with unique universal identifiers (see FIGS. 17 and 18)).
Regarding claim 6: Ameri / Cooley teaches all the limitations of claim 1 above.
Although Ameri teaches managing extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach collecting data in real time and/or at scheduled intervals and doing so from internet sites, APIs, or digital databases.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the extraction module is configured to collect data in real-time and/or at scheduled intervals from the digital data sources comprising any one or more internet sites, application programming interfaces, and digital databases (Cooley ¶ [014]: a data access module providing one or more APIs or one or more real-time streams comprising the enriched data… a data extraction module retrieving data from at least one of the identified data sources…. In some embodiments, the data extraction module retrieves the data on a schedule, in response to an event, or a combination thereof. ¶ [032]: …In these embodiments, the data integration pipeline discovers and/or extracts data from cloud APis. In some embodiments, the data integration pipeline processes data both from on-premise systems and cloud systems).
Cooley and Ameri are found as analogous art of data integration and knowledge refinement. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Ameri’s knowledge currency system and method to have included Cooley’s teachings around collecting data in real time and/or at scheduled intervals and doing so from internet sites, APIs, or digital databases. The benefit of these additional features would have increased in productivity, accuracy, flexibility, and reduced cost with automation (Cooley ¶ [002]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Ameri in view of Cooley (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of data integration and knowledge refinement. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Ameri in view of Cooley above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 7: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the extraction module is configured to provide data validation including verifying integrity and accuracy of collected data by cross-referencing and error-checking (Ameri ¶ [0038]: In an embodiment, "metadata" is stored in a database such as My SQL in data structures that insure its consistency and integrity. The metadata includes lists and tables helpful to or necessary for categorizing and managing knowledge and "smart content" (1630). ¶ [0141]: Following the workflow chart, the nest step in this embodiment is to conduct iterations of user validations, identify areas that knowledge graph does not have adequate depth and breadth (knowledge gaps). Based on recommendation from Domain experts and Community of practice identify the content that needs to be analyzed further to address the knowledge gaps. (3540) The next sept continues to FIG. 18. ¶ [0145]: Iterate steps 1 to 10 with user validation and feedback until the level of accuracy and certainty from user experience is near 95% (3680)).
Regarding claim 8: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the extraction module is configured to share validated data with the transform module in a standardized format (Ameri ¶ mid-[0108]: The challenge of developing appropriate adaptors for various formats, like OCR files convert to pdf convert to XML convert to relational database structures, has been overcome and achieved, as described herein. This result produces a unique fine grain data elements in bulk and war content files (multi media archives) into easily scored, indexed and profiled data records in the knowledge refinery database. The steps taken to automatically apply universal machine-readable format makes it much simpler to compute semantic meaning (an example of which is FIG. 20), domain of knowledge relevancy and rank scoring (an example of which is FIG. 8). Depending on the industry (for example legal domain (FIG. 10, 2610), or career training topic (FIG. 10, 2630), or industry domain like Pharmaceuticals or others (FIG. 10, 2620) additional set of desired knowledge structure standards (FIG. 6, 2250), and properties can be applied to further enrich the value associated to fine grain knowledge objects with a universally identifiable and exchangeable AI enabled data units (FIG. 12, 2970). ¶ [0145]: Iterate steps 1 to 10 with user validation and feedback until the level of accuracy and certainty from user experience is near 95% (3680)).
Regarding claim 9: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the extraction module is configured to coordinate with the transform module to ensure the raw digital data extracted meets predefined minimum standards for further processing within the computer-implemented educational platform (Ameri ¶ [0104]: In further detail, and explained in even greater detail to follow, BestFriend service enables access to a Customer knowledge base (digitized, machine readable, standardized & curated) enabling Search, Discovery, Question & Answers, Topic-Specific notification and Smart Advisory services (2640). ¶ [0145]: The in-depth content structuring, enriching, refining, interlinking and annotating of target content to the extent it meets the search query criteria [standards] results in a much more practical and smaller highly classified, categorized, relevant, purposeful and personalized search results (1630)).
Regarding claim 10: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the transform module is configured to perform data cleaning, data enrichment, and data aggregation on the raw digital data received from the extraction module (Ameri ¶ [0145]: Iterate steps 1 to 10 with user validation and feedback until the level of accuracy and certainty from user experience is near 95% [EN: clean] (3680). ¶ [0065]: The in-depth content structuring, enriching, refining, interlinking and annotating of target content to the extent it meets the search query criteria results in a much more practical and smaller highly classified, categorized, relevant, purposeful and personalized search results (1630).¶ mid-[0047]: The "Community Audience Attention" level (FIG. 3, Column C for reference) is the fifth stage (1100) and represents capturing the community's current and over time aggregated community voice, feedback and perspective regarding specific content, representing them in terms of usage, usefulness, with additional annotation, comments, side notes or tags. This can be part of a manual or fully automated sentiment analysis a process of clustering, normalizing, standardizing those tags and adding them to customer's growing knowledge refinery, the growing dictionary).
Regarding claim 11: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the transform module is configured to standardize the raw digital data by applying consistent transformation protocols to ensure data uniformity (Ameri ¶ mid-[0108]: This result produces a unique fine grain data elements in bulk and war content files (multi media archives) into easily scored, indexed and profiled data records in the knowledge refinery database. The steps taken to automatically apply universal machine-readable format makes it much simpler to compute semantic meaning (an example of which is FIG. 20), domain of knowledge relevancy and rank scoring (an example of which is FIG. 8). Depending on the industry (for example legal domain (FIG. 10, 2610), or career training topic (FIG. 10, 2630), or industry domain like Pharmaceuticals or others (FIG. 10, 2620) additional set of desired knowledge structure standards (FIG. 6, 2250), and properties can be applied to further enrich the value associated to fine grain knowledge objects with a universally identifiable and exchangeable AI enabled data units (FIG. 12, 2970)).
Regarding claim 12: Ameri / Cooley teaches all the limitations of claim 1 above.
Although Ameri teaches managing extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach an integration module which provides at least one of data mapping, storage, or accessibility.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the integration module is configured to provide at least one of data mapping, data storage, and data accessibility (Cooley ¶ [006]: …The following components, in various embodiments, are needed to implement the platforms, systems, and methods described herein: [007] Data or data source discovery [EN: accessibility] mechanism;… [009] Data mapping mechanism; [010] Data storage system).
Rationales to have modified / combined Ameri / Cooley are above and reincorporated.
Regarding claim 13: Ameri / Cooley teaches all the limitations of claim 1 above.
Although Ameri teaches managing extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach individual modules interfacing together to maintain a seamless data pipeline.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the integration module is configured to interface with the transform module and the extraction module to maintain a seamless data pipeline (Cooley ¶ [050]: Referring to Fig. 4, in a particular embodiment, a process for providing an automated data integration pipeline with storage and enrichment 400 begins with discovery of one or more data and/or data sources 410. Discovery, in this embodiment, comprises one or more of passive discovery, active discovery, and target interrogation methodologies. The next step is to extract data 420 from some or all of the discovered data…. Next, extracted data is mapped 430 from the source format to an ontology, according to a schema and a plurality of mapping profiles. Once mapped to the known ontology, the data is merged 440 (not just loaded or added) to a data storage mechanism, such as a graph database, which contains a living representation of the data, the relationships among the data, and any enrichments that were introduced by subsequent components. Next, the merged data is enriched 450 by monitoring real-time streams of data (e.g., streams of graph and timeseries data in a graph database) to identify patterns utilizing pattern matching, statistical analysis, machine learning, or the like. In this embodiment, Finally, access to the normalized, merged, and enriched data is provided 460 utilizing APIs, data streams, and/or data feeds, and the like. In this embodiment, the processed data is consumed by other applications, running locally, remotely, or both, that will access the data in real-time to provide information and services not otherwise available).
Rationales to have modified / combined Ameri / Cooley are above and reincorporated.
Regarding claim 14: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
wherein the integration module is configured to continuously monitor one or more data storage systems of the digital knowledge base to ensure the one or more data storage systems are operating efficiently and/or that data in the one or more data storage systems are readily available for querying (Ameri ¶ [0038]: In an embodiment, "metadata" is stored in a database such as My SQL in data structures that insure its consistency and integrity. The metadata includes lists and tables helpful to or necessary for categorizing and managing [EN: monitoring] knowledge and "smart content" (1630)).
Regarding claim 15: Ameri / Cooley teaches all the limitations of claim 1 above.
Although Ameri teaches managing extraction, transformation, and integration of data into a searchable knowledge base, Ameri does not specifically teach the delivery of data between individual modules at each stage of the pipeline.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
wherein the dataflow is from the extraction module to the transform module to the integration module, whereby the extraction module delivers the raw digital data obtained from the digital data sources to the transform module, and the transform module delivers the transformed data to the integration module for loading into the digital knowledge base (See Cooley discussion of data delivery from raw form at the source to various stages of the pipeline at ¶ [061]: Another component of the platforms and systems described herein, and utilized by the methods described herein is one or more APis and/or real-time data streams and/or live data feeds. See, e.g., Fig. 1 at 160, Fig. 2 at 237, and Fig. 3 at 333. The last component needed for the platforms, systems, and methods described herein is some combination of APIs and streams for accessing the normalized, merged, and enriched data. While this data pipeline adds immense value to the original raw data that entered the pipeline, the resulting data would be useless if it couldn't be accessed. In various embodiments, the final destination of the processed data is other applications, running either locally or remotely, that will access the data either by polling an API for new data or using a callback, webhook, or web socket type mechanism to receive a stream of data in real-time. These applications must also be aware of any enrichments that came into existence after the initial delivery of data, so all delivered data must be uniquely identifiable so subsequent updates can be correlated).
Cooley and Ameri are found as analogous art of data integration and knowledge refinement. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Ameri’s knowledge currency system and method to have included Cooley’s teachings around the delivery of data between individual modules at each stage of the pipeline. The benefit of these additional features would have increased in productivity, accuracy, flexibility, and reduced cost with automation (Cooley ¶ [002]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Ameri in view of Cooley (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of data integration and knowledge refinement. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Ameri in view of Cooley above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claim 16: Ameri / Cooley teaches all the limitations of claim 1 above.
Although Ameri teaches an education-related AI assistant system which extracts, transforms, and integrates data into a searchable knowledge base, Ameri does not specifically teach individual extraction, transform, and integration modules.
However, Cooley in analogous art of data integration and knowledge refinement teaches or suggests:
A method of hierarchically processing data using the computer-implemented AI assistant of claim 1, the method comprising:
extracting with the extraction module raw digital data from a digital data source (Cooley ¶ [014]: …a data extraction module retrieving data from at least one of the identified data sources…. In some embodiments, the data extraction module retrieves the data on a schedule, in response to an event, or a combination thereof. In some embodiments, the first algorithm comprises, a logic schema, a set of rules, a machine learning model, a statistical model, or a combination thereof);
delivering the raw digital data from the extraction module to the transform module (See Cooley discussion of data delivery from raw form at the source to various stages of the pipeline at ¶ [061]);
transforming the raw digital data with the transform module into transformed digital data (Cooley ¶ [005]: Described herein are platforms, systems, and methods that automatically discover, extract, map, merge [EN: transform], and enrich data found in systems on-premises in automated industrial and commercial environments and cloud systems for purposes of providing developers access to normalized, merged, and enriched [EN: usable] data through an API. (Cooley Abstract: The platforms, systems, and methods … apply a first algorithm [EN: transform module] to map the retrieved data to a predetermined ontology; merge the mapped data into a data store comprising timeseries of the mapped data);
delivering the transformed digital data from the transform module to the integration module (See Cooley discussion of data delivery from raw form at the source to various stages of the pipeline at ¶ [061]; and
mapping and storing with the integration module the transformed digital data received from the transform agent into the digital knowledge base (Cooley ¶ [006]: …The following components, in various embodiments, are needed to implement the platforms, systems, and methods described herein: [007] Data or data source discovery [EN: accessibility] mechanism;… [009] Data mapping mechanism; [010] Data storage system).
Rationales to have modified / combined Ameri / Cooley are above and reincorporated.
Regarding claim 17: Ameri / Cooley teaches all the limitations of claim 1 above.
Ameri further teaches:
A computer-implemented educational platform implementing software instructions executed by a computer system comprising one or more processors and digital memory storage (See Ameri Fig. 10 element 2630 ‘Education Advisor Agent/App’ and related text. Ameri ¶ [0059]: In general other content-intensive professional service organizations like educational institutions… who are faced with ever growing client demands from their limited supply of staff, domain experts and customer support centers, as part of their routine operation produce and consume content within their domain to offer their high value expertise and knowledge services to their clients. Ameri ¶ [0005]: … a user is assisted with an Artificial Intelligence (AI) enabled digital assistant ("agent") (300), searching for information (350)), the computer-implemented educational platform comprising;
a user interface (Ameri ¶ [0037]: An embodiment allows users (400) to select an entity by entering a search request in a user interface and interacting with agents (450));
a plurality of sources of raw digital data related to an educational organization (See Ameri Fig. 10 element 2630 ‘Education Advisor Agent/App’ and related text. Ameri ¶ [0059]: In general other content-intensive professional service organizations like educational institutions…. Ameri ¶ [0043]: The "Raw Content Acquisition" is the first stage (stage one), (700) and represents processes involved in retrieving the raw content from documents in specific public or private repository ("knowledge database"));
a digital knowledge base (Ameri ¶ [0043]: The "Raw Content Acquisition" is the first stage (stage one), (700) and represents processes involved in retrieving the raw content from documents in specific public or private repository ("knowledge database")); and
the computer-implemented AI assistant of claim 1;
wherein the computer-implemented AI assistant interacts with each of the user interface, the plurality of sources of raw digital information, and the digital knowledge base to:
extract raw digital data from the plurality of sources (Ameri ¶ [0043]: The "Raw Content Acquisition" is the first stage (stage one), (700) and represents processes involved in retrieving the raw content from documents in specific public or private repository ("knowledge database"));
transform the raw digital data into transformed digital data (Ameri ¶ [0044]: "Closed Domain Classifier" (FIG. 3, Column D for reference) is the second stage (stage one), (800) represents extracting, identifying, and adding to the growing dictionary of domain vocabulary topics (meaningful and clear concepts generally recognized as uniquely understandable and useful concepts) and associating them (labeling) to the specific content piece in the raw content data set. ¶ [0045]: The "Industry Specific Classifier" (FIG. 3, Column I for reference) is the third stage (900) and represents extracting, identifying, and adding to the growing dictionary of industry relevant vocabulary topics and associating them (industry specific classifier labeling) to the specific content piece that is now one level classified with domain vocabulary in the second stage (800) as additional metadata that now recognized as a knowledge nugget);
store the transformed digital data in the digital knowledge base (Ameri ¶ [0084]: This enhanced digital knowledge exploration method changes the way computers process raw content [EN: integrate] into a refined well annotated knowledge base that can be indexed and search with relevant highly connected topics resulting search engine optimization plans, reflecting core topics and relationships in a specific knowledge domain, reflecting needs and languages of members of target communities. [0150] Conclude this instance of continuous improvement and deployment of the knowledge refinery as a new released version of adapted, refined and enriched knowledge bases (3750));
receive queries f