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 .
Response to Amendment
Applicant’s Remarks, filed September 18th, 2025, has been fully considered and entered. Accordingly, claims 1-2, 5-9, 12-16, and 19-20 are pending in the case. Claims 1, 2, 6, 8, 9, 13, 15, 16, and 19 were amended. Claims 1, 8, and 15 are the independent claims.
In light of Applicant’s Amendment, the 35 USC 112 Rejection of claims 1, 8, and 15 has been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-9, 12-16, and 19-20 are being rejected under 35 U.S.C. 103 as being unpatentable over Paulsen et al. (US 2017/0262447 A1) in view of Bly et al. (US 2023/0060252 A1) in view of Turner et al. (US 6,633,742 B1), further in view of Austin et al. (US 2021/0174130 A1).
Regarding claim 1, Paulsen teaches a method for analysis and summarization of current knowledge of data by a processor, comprising:
receiving input of a knowledge domain from one or more data sources and a query indicating a problem related to the knowledge domain (see Paulsen, Paragraphs [0026], [0027], “FIG. 1 is a block diagram illustrating an exemplary system 100 for analyzing online articles to identify popular topics and terms, as well as the lifespan of the topics and terms … the system 100 includes an article analytics engine 104 configured to process information regarding a group of online articles 102 to provide user interfaces that allow a user to investigate popular topics and terms from the group of online articles 102.” [The user may investigate popular topics and terms (i.e., receiving input of a knowledge domain) from the group of online articles (i.e., one or more data sources).]);
However, Paulsen does not explicitly teach:
receiving input of a knowledge domain from one or more data sources and a query indicating a problem related to the knowledge domain;
Bly teaches:
receiving input of a knowledge domain from one or more data sources and a query indicating a problem related to the knowledge domain (see Bly, Paragraphs [0146], [0173], “a user provides a query topic/concept and one or more parameters which may interest them in the search of related topics, such as segments of the population (age or location, as examples) they wish to investigate.” [A user may provide a query topic/concept and on or more parameters (i.e., a query indicating a problem related to the knowledge domain) related to the topic.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Paulsen (teaching topical analytics for online articles) in view of Bly (teaching systems and methods for organizing, finding, and using data), and arrived at a method that incorporates searching a knowledge domain. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of identifying relevant datasets for use in training a machine learning model related to a topic of interest (see Bly, Paragraph [0010]). In addition, both the references (Paulsen and Bly) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as topics. The close relation between both of the references highly suggests an expectation of success.
The combination of Paulsen, and Bly further teaches:
identifying and extracting a topic of the knowledge domain from the one or more data sources (see Paulsen, Paragraphs [0026], [0027], “FIG. 1 is a block diagram illustrating an exemplary system 100 for analyzing online articles to identify popular topics and terms, as well as the lifespan of the topics and terms … the system 100 includes an article analytics engine 104 configured to process information regarding a group of online articles 102 to provide user interfaces that allow a user to investigate popular topics and terms from the group of online articles 102.” [The online articles may be processed in order to identify and extract popular topics and terms (i.e., a topic of the knowledge domain).]);
executing a machine learning logic to train one or more question/answer (QA) models to link the query to the topic of the knowledge domain (see Bly, Paragraphs [0146]-[0153], [0173], “Searching for a topic to discover/identify the factors (variables or parameters) that have been shown to impact the topic or be impacted by it based on data and information indicating a statistical significance; … The data sources or data sets may be used to train a machine learning model and/or to evaluate the strength or significance of a statistically relevant relationship between two variables, between two topics, or between a topic and a variable; … As a non-limiting example, GPT-3 may be used as a basis for training one or more models to identify and extract specific information from sources that can be stored in a database and used by the system or platform to generate a Feature Graph in response to a user input; … Auto-generating a meta-analysis of the relationship between a pair of topics based on statistical evidence gathered from literature, datasets, and machine learning models; This may be used as part of answering questions for professionals that are not feasible to answer today in a systematic or continuous way (to account for new information or evidence), or which require significant investments of time and resources to produce. … a user provides a query topic/concept and one or more parameters which may interest them in the search of related topics …” [Machine learning maybe used in order to answer questions about a topic.]);
executing the machine learning logic to predict, using the one or more QA models, a workflow including a plurality of subtasks for solving the problem indicated in the query, wherein a respective subtask of the plurality of subtasks includes at least one method evaluated for performance of the respective subtask (see Bly, Paragraph [0124], “A user inputs a topic, study, or variable and wants to retrieve datasets that could be used to train a model to predict that topic, study, or variable, i.e., those that are linked to variables statistically associated with the input topic, study, or variable;” [Machine learning may be used in order to predict topics, studies or variables that are associated with input.]);
However, the combination of Paulsen, and Bly do not explicitly teach:
executing the machine learning logic to predict, using the one or more QA models, a workflow including a plurality of subtasks for solving the problem indicated in the query, wherein a respective subtask of the plurality of subtasks includes at least one method evaluated for performance of the respective subtask;
Turner teaches:
executing the machine learning logic to predict, using the one or more QA models, a workflow including a plurality of subtasks for solving the problem indicated in the query, wherein a respective subtask of the plurality of subtasks includes at least one method evaluated for performance of the respective subtask (see Turner, [Columns 13-14, Lines 48-67 & 1-17], “The Perform working mode knowledge module identifier 110 receives input from the user further specifying a classification of topics and a topic, i.e. a task to perform, within the classification that the user is interested in. For example, the user may choose to perform an installation. Within the Installation classification, they may choose the task of installing a new hard drive. Each sub-task may consist of one or more knowledge objects 104, each showing one or more steps in the sub-task. The task to perform may include a set of one or more sub-topics directed to that task. Typically, tasks within each classification are composed of a set of sub-tasks, each of which is considered a sub-topic. Since all of the sub-tasks typically need to be performed to complete the task, the knowledge module identifier 110 for the Perform working mode automatically selects all of the sub-topics for a given task rather than allow the user to pick and choose sub-topics as is allowed by the Learn and Browse knowledge module identifiers. For example the task for installing the new hard drive may consist of several sub-tasks such as removing a panel, inserting the new drive, and replacing the panel, all of which need to be performed to accomplish the task.” [User input is received in order to perform a task (i.e., workflow), which includes subtasks directed to that task. The subtasks are methods in order to perform the task.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Paulsen (teaching topical analytics for online articles) in view of Bly (teaching systems and methods for organizing, finding, and using data), further in view of Turner (teaching system and method for adaptive knowledge access and presentation), and arrived at a method that performs a task that includes subtasks. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of performing specific tasks (see Turner, [Column 8, Lines 46-51]). In addition, the references (Paulsen, Bly, and Turner) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as topics. The close relation between the references highly suggests an expectation of success.
The combination of Paulsen, Bly, and Turner further teaches:
and generating a graphical user interface (GUI) to visually represent the workflow as a leaderboard of methods for performing the plurality of subtasks, wherein, for each subtask, the at least one method of the respective subtask is ranked in the leaderboard of methods based on a score assigned to the at least one method indicating a degree of knowledge advancement in relation to the topic of the knowledge domain, wherein in response to a selection of a specific method from the leaderboard of methods, dynamically constructing a summary of the specific method by linking together different elements from the one or more data sources associated with the specific method and displaying the summary of the specific method with the workflow (see Turner, [Column 9, Lines 40-45], [Columns 18-20], “adaptive performance support system that provides guided learning and task performance instruction through a dynamic user interface.;” [The user interface can be generated to guide the user through the solution of the problem. The user information provides information (i.e., summary) regarding the task (i.e., workflow).]).
However, the combination of Paulsen, Bly, and Turner do not explicitly teach:
and generating a graphical user interface (GUI) to visually represent the workflow as a leaderboard of methods for performing the plurality of subtasks, wherein, for each subtask, the at least one method of the respective subtask is ranked in the leaderboard of methods based on a score assigned to the at least one method indicating a degree of knowledge advancement in relation to the topic of the knowledge domain, wherein in response to a selection of a specific method from the leaderboard of methods, dynamically constructing a summary of the specific method by linking together different elements from the one or more data sources associated with the specific method and displaying the summary of the specific method with the workflow.
Austin teaches:
and generating a graphical user interface (GUI) to visually represent the workflow as a leaderboard of methods for performing the plurality of subtasks, wherein, for each subtask, the at least one method of the respective subtask is ranked in the leaderboard of methods based on a score assigned to the at least one method indicating a degree of knowledge advancement in relation to the topic of the knowledge domain, wherein in response to a selection of a specific method from the leaderboard of methods, dynamically constructing a summary of the specific method by linking together different elements from the one or more data sources associated with the specific method and displaying the summary of the specific method with the workflow (see Austin, Paragraphs [0032], [0042], “The scores may be posted, for example onto a leaderboard. The best ranking overall solution is the one which ranks the best on the leaderboard. … the leaderboard rankings, along with information about ranked models, may be provided in a human readable form to a user for review and selection of a winning ML model. The selection may be based on the leaderboard ranking or other factors such as computation efficiency, explainability and understanding of the model, simplicity, etc.” [The user interface presents a leaderboard that ranks solutions based on scores (i.e., degree of knowledge advancement).]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Paulsen (teaching topical analytics for online articles) in view of Bly (teaching systems and methods for organizing, finding, and using data), further in view of Turner (teaching system and method for adaptive knowledge access and presentation), further in view of Austin (teaching a method for auto machine learning via optimal hybrid AI formulation from crowd), and arrived at a method that incorporates a leaderboard. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improving solutions (see Austin, Paragraph [0002]). In addition, the references (Paulsen, Bly, Turner, and Austin) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information systems. The close relation between the references highly suggests an expectation of success.
Regarding claim 2, Paulsen in view of Bly in view of Turner, further in view of Austin teaches all the limitations of claim 1. Paulsen further teaches:
generating a list of candidate subtopics related to the topic of the knowledge domain (see Paulsen, Paragraph [0050], “the text analysis performed at block 204 can be used to identify not only topics relevant to each online article but also subtopics relevant to the identified topics, as well as relevance scores for each subtopic. The subtopics may include any number of different levels (e.g., subtopics of topics, subtopics of subtopics, etc.).” [Subtopics may be identified that are related to the topic of the knowledge domain.]);
and assigning a relevance score indicating a degree of relevance to the topic for each candidate subtopics in the list of candidate subtopics (see Paulsen, Paragraph [0050], “the text analysis performed at block 204 can be used to identify not only topics relevant to each online article but also subtopics relevant to the identified topics, as well as relevance scores for each subtopic.” [A relevance score (i.e., a relevance score indicating a degree of relevance) may be associated with the subtopics.]).
Regarding claim 5, Paulsen in view of Bly in view of Turner, further in view of Austin teaches all the limitations of claim 2. Bly further teaches:
constructing a knowledge graph of one or more candidate subtopics from the list of candidate subtopics (see Bly, Paragraph [0101], “A Feature Graph is populated with information/data about statistical associations retrieved from (for example) journal articles, scientific and technical databases, digital “notebooks” for research and data science, experiment logs, data science and machine learning platforms, a public website where users can input observed or perceived statistical relationships, and in some cases, other sources;” [A feature graph may be populated (i.e., constructing a knowledge graph).]).
Regarding claim 6, Paulsen in view of Bly in view of Turner, further in view of Austin teaches all the limitations of claim 2. Bly, and Turner further teaches:
predicting a list of tasks related to the list of candidate subtopics (see Bly, Paragraph [0124], “A user inputs a topic, study, or variable and wants to retrieve datasets that could be used to train a model to predict that topic, study, or variable, i.e., those that are linked to variables statistically associated with the input topic, study, or variable;” Also, see Turner, [Column 13, Lines 48-60], “The Perform working mode knowledge module identifier 110 receives input from the user further specifying a classification of topics and a topic, i.e. a task to perform, within the classification that the user is interested in. For example, the user may choose to perform an installation. Within the Installation classification, they may choose the task of installing a new hard drive. Each sub-task may consist of one or more knowledge objects 104, each showing one or more steps in the sub-task. The task to perform may include a set of one or more sub-topics directed to that task. Typically, tasks within each classification are composed of a set of sub-tasks, each of which is considered a sub-topic.” [A list of task related to the list of subtopics may be predicted.]).
Regarding claim 7, Paulsen in view of Bly in view of Turner, further in view of Austin teaches all the limitations of claim 2. Paulsen further teaches:
generating one or more expandable facets for one or more of the list of candidate subtopics depicting a plurality of data from the one or more data sources via the GUI (see Paulsen, Paragraph [0058], “FIG. 4 provides a screenshot illustrating an example user interface 400 that provides an indication of relevant/popular topics and subtopics within a group of online articles and important terms within each topic. In the present example, the visitor segment selected corresponds to females, 25-40 years old, as represented in the visitor segment selectors 402, 404. Additionally, the visitor metrics used correspond to the last 30 days as shown by the time period selector 406. In embodiments, a user could employ the visitor segment selectors 402, 404 to modify the visitor segment being analyzed. A user could also alter the time period being analyzed using the time period selector 406.” [Figure 4 shows a user interface that displays relevant topics and subtopics within a group of articles, in which the user interface enables the user to modify the visitor metrics using the expandable facets.]).
Regarding claims 8-9, 12-16, 19-20, Paulsen in view of Bly in view of Turner, further in view of Austin teaches all of the limitations of claims 1-2, and 5-7 in method form, rather than in system and computer program product form. Paulsen also discloses a system [0026] and a computer program product [0069]. Therefore, the supporting rationale of the rejection to claims 1-2, and 5-7 applies equally as well to those limitations of claims 8-9, 12-16, and 19-20.
Response to Arguments
Applicant’s Arguments, filed September 18th, 2025, have been fully considered, but are not persuasive.
Applicant argues on page 11 of Applicant's Remarks that the cited references, fails to teach or suggest “wherein a respective subtask of the plurality of subtasks includes at least one method evaluated for performance of the respective subtask.” The Examiner respectfully disagrees.
As mentioned on page 11 of Applicant’s Remarks, Turner discloses in [Column 13, Lines 48-56], “The Perform working mode knowledge module identifier 110 receives input from the user further specifying a classification of topics and a topic, i.e. a task to perform, within the classification that the user is interested in. For example, the user may choose to perform an installation. Within the Installation classification, they may choose the task of installing a new hard drive. Each sub-task may consist of one or more knowledge objects 104, each showing one or more steps in the sub-task.” As shown, each subtask may consist of knowledge objects, which shows one or more steps in order to complete the task requested by the user. Therefore, Turner teaches a subtask which consists of knowledge objects showing one or more steps (i.e., method evaluated for performance of the respective subtask).
Applicant argues on page 11 of Applicant's Remarks that the cited references, fails to teach or suggest “generating a graphical user interface (GUI) to visually represent the workflow as a leaderboard of methods for performing the plurality of subtasks, wherein, for each subtask, the at least one method of the respective subtask is ranked in the leaderboard of methods based on a score assigned to the at least one method indicating a degree of knowledge advancement in relation to the topic of the knowledge domain.” The Examiner respectfully disagrees.
Austin discloses in paragraphs [0032] and [0042], “The scores may be posted, for example onto a leaderboard. The best ranking overall solution is the one which ranks the best on the leaderboard. … the leaderboard rankings, along with information about ranked models, may be provided in a human readable form to a user for review and selection of a winning ML model. The selection may be based on the leaderboard ranking or other factors such as computation efficiency, explainability and understanding of the model, simplicity, etc.” As shown, Austin discloses presenting a leaderboard that ranks solutions based on scores (i.e., degree of knowledge advancement).
Therefore, it would have been obvious to anyone with ordinary skill in the art before the effective filing of the claimed invention to have incorporated the teachings of Austin into Paulsen, Bly, and Turner in order to arrive at Applicant’s claimed invention.
Applicant argues on page 12 of Applicant's Remarks that the cited references, fails to teach or suggest “in response to a selection of a specific method from the leaderboard of methods, dynamically constructing a summary of the specific method by linking together different elements from the one or more data sources associated with the specific method and displaying the summary of the specific method with the workflow.” The Examiner respectfully disagrees.
In addition to the cited portion above, Turner discloses in [Column 9, Lines 40-51], “The embodiments described herein relate to an adaptive performance support system that provides guided learning and task performance instruction through a dynamic user interface. … Instead these embodiments dynamically adapt their presentation of information to varying users needs, relate information directly to their workflow, and contribute to the timely completion of the task at hand.” As shown, Turner discloses a dynamic user interface for presenting information.
Accordingly, Turner discloses in [Columns 18-19, Lines 57-67 & 1-8], “The Learn mode presents information oriented in a tutorial fashion such as a lesson. This may include a displayed lecture, explanatory descriptions or illustrations or background information coupled with an evaluation such as a quiz or an examination. Learn mode focuses on teaching the user about the desired sub-topic. For example, the user may specify that they wish to learn about performing an image quality test on an ultrasound machine. The Learn mode presents information in a format designed to teach the user how to perform such a test, why the test is needed, what the test actually accomplishes and other relevant knowledge. Knowledge objects 104 selected for the specified sub-topic and oriented towards the Learn mode may further contain links, as described above, to other resources such as live instructors or training personnel via chat rooms or other communications medium, or on-line learning systems, such as web sites, which provide training in subject matter pre-requisite to the specified sub-topic or have updated training materials.” As shown, knowledge objects selected for the specified sub-topic or subtask may be linked to other resources (i.e., summary). Therefore, it would have been obvious to anyone with ordinary skill in the art before the effective filing of the claimed invention to have incorporated the teachings of Austin into Paulsen, Bly, and Turner in order to arrive at Applicant’s claimed invention.
For the above reasons, the rejections are sustained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUSAM TURKI SAMARA whose telephone number is (571)272-6803. The examiner can normally be reached on Monday - Thursday, Alternate Fridays.
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/HUSAM TURKI SAMARA/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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