DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 12/5/2025, Applicant, on 3/5/2026, amended claims 1 and 11, and cancelled claims 6-8, 14, and 19. Claims 1-20 are pending in the present application and are under examination on the merit.
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 amendments are acknowledged.
The Double Patenting rejections of claims 1-20 are withdrawn in light of Applicant’s amendments.
The 35 USC 101 rejections of claims 1-20 are withdrawn in light of Applicant’s amendments and explanations.
New 35 USC 103 rejections of claims 1- 20 are applied in light of Applicant’s amendments and explanations.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2018/0150562 to Gundimeda et al. (hereafter referred to as Gundimeda) in view of U.S. Patent Application Publication Number 2022/0300881 to Singh et al. (hereafter referred to as Singh) and in further view of U.S. Patent Application Publication Number 2015/0019463 to Simard et al. (hereafter referred to as Simard).
As per claim 1, Gundimeda teaches:
An apparatus for generating system improvement data, wherein the apparatus comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to: (Paragraph Number [0068] teaches the computer system 502 comprises a processor 504 and a memory 506. The processor 504 executes program instructions and may be a real processor. The processor 504 may also be a virtual processor. The computer system 502 is not intended to suggest any limitation as to scope of use or functionality of described embodiments. For example, the computer system 502 may include, but not limited to, a general-purpose computer, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, and other devices or arrangements of devices that are capable of implementing the steps that constitute the method of the present invention. In an embodiment of the present invention, the memory 506 may store software for implementing various embodiments of the present invention. The computer system 502 may have additional components. For example, the computer system 502 includes one or more communication channels 508, one or more input devices 510, one or more output devices 512, and storage 514. (See also Fig. 1)).
receive system data relating to an organizational identifier, wherein receiving the system data comprises: (Paragraph Number [0046] teaches the system 100 facilitates categorizing information based on domains to provide relevant information related to a specific domain. In an exemplary embodiment of the present invention, the analysis module 112 facilitates extracting and analyzing information related to one or more companies. The system 100 extracts company information and its financials from various data sources 108 such as, but not limited to, company websites, government regulatory filings, security filings and news. Further, the system 100 may also provide information related to a company's products, market segments, services and employees. Paragraph Number [0062] teaches the extracted data is analyzed by performing one or more analytical operations on the extracted data. In an embodiment of the present invention, the one or more analytical operations include, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning that facilitate in making the extracted data more meaningful for one or more end users. (See also Paragraph Number [0047])).
training and utilizing a web crawler to generate a web index (Paragraph Number [0033] teaches the web scraping and crawling module 106 comprise a content value extractor configured to extract data from the one or more data sources 108 and aggregate the extracted data. The aggregated data is then indexed and stored for use by one or more end users and downstream enterprise applications. In an embodiment of the present invention, the web scraping and crawling module 106 ranks the extracted data based on at least one of: keyword priorities and priorities assigned to the one or more data sources 108 associated with the one or more data extraction jobs. In an embodiment of the present invention, the extracted data from higher priority sources is considered more relevant. Paragraph Number [0059] teaches the data from the one or more data sources is extracted by a crawler. The crawler is configured to search the one or more data sources and detect one or more documents and one or more hyperlinks based on the one or more configured rules. The crawler is further configured to analyze the detected documents and the detected hyperlinks based on navigational context and context of the search. (See also Examples in Paragraph Numbers [0034], [0037], and [0057])).
generating a query as a function of the organizational identifier (Paragraph Number [0037] teaches the analysis module 112 is configured to decipher at least one of: the extracted data and the analyzed data using pre-stored vocabularies and classify into domain based information. In an exemplary embodiment of the present invention, the pre-stored vocabularies are stored in a triplestore. Further, the triplestore is queried by the analysis module 112 for deciphering the extracted data and the analyzed data. In an embodiment of the present invention, the analysis module 112 also indexes and catalogues the extracted data and the analyzed data. Indexing and cataloguing facilitates in efficient querying and retrieving of the data. Paragraph Number [0062] teaches the extracted data is analyzed by performing one or more analytical operations on the extracted data. In an embodiment of the present invention, the one or more analytical operations include, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning that facilitate in making the extracted data more meaningful for one or more end users. The one or more analytical operations also include deduplication process to filter duplicated data within the extracted data. In an embodiment of the present invention, the one or more analytical operations are performed using a Named Entity Recognizer (NER), a rule processing engine, a set of machine learning classification libraries and a thesaurus for handling libraries. (See also Paragraph Number [0060])).
retrieving the system data as a function of the web index and the organizational identifier (Paragraph Number [0037] teaches the analysis module 112 is configured to decipher at least one of: the extracted data and the analyzed data using pre-stored vocabularies and classify into domain based information. In an exemplary embodiment of the present invention, the pre-stored vocabularies are stored in a triplestore. Further, the triplestore is queried by the analysis module 112 for deciphering the extracted data and the analyzed data. In an embodiment of the present invention, the analysis module 112 also indexes and catalogues the extracted data and the analyzed data. Indexing and cataloguing facilitates in efficient querying and retrieving of the data. Paragraph Number [0062] teaches the extracted data is analyzed by performing one or more analytical operations on the extracted data. In an embodiment of the present invention, the one or more analytical operations include, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning that facilitate in making the extracted data more meaningful for one or more end users. The one or more analytical operations also include deduplication process to filter duplicated data within the extracted data. In an embodiment of the present invention, the one or more analytical operations are performed using a Named Entity Recognizer (NER), a rule processing engine, a set of machine learning classification libraries and a thesaurus for handling libraries. (See also Paragraph Number [0060])).
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
receive user data related to a plurality of users (Paragraph Number [0018] teaches the data retriever 130 may correspond to a component for receiving input data including one or more input parameters corresponding to an active project and/or a historical project. The input parameters, and values thereof, corresponding to the active project may define live data. Similarly, the input parameters, and values thereof, corresponding to the historical project may define historical data. As illustrated in FIG. 2, the input data may include project data 204 and recommendation feedback data 206. Paragraph Number [0033] teaches the historical data 335 may also include historical recommendation data including a set of one or more historical recommendations. Each such historical recommendation may relate to a specific input parameter, or values thereof, which may have yielded an intended outcome, such as the KPI benefit value and initiative cost performance being equal to or greater than a corresponding target value, for the historical project. In some examples, the historical data 335 may also include a live recommendation data corresponding to an active project and provided as feedback to the data retriever 130. The recommendations may indicate a suggestive change to corresponding input parameters related to an active project. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. (See also Paragraph Number [0019])).
classify the system data and user data to a performance range category (Paragraph Number [0043] teaches the data status provider 320 may correspond to a component for forecasting a future value of the KPI, predict a possibility of failure of the KPI, categorize the KPI based on the prediction, calculate KPI benefit value for the KPI, calculate a net present value of an initiative related to the KPI, categorize the initiative based on the calculated net present value. In some instances, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI. In one example, the data status provider 320 may access a first trained data model such as the first trained data model 345-1 (e.g., trained multivariate regression model) to forecast the future value. In another example, the data status provider 320 may use the influencing factors, or key attributes and features, of the identified relevant KPI cluster to train a new data model or retrain the first trained data model 345-1, such as the multivariate regression model., 0047, Paragraph Number [0073] teaches if the future value may be equal to or greater than the target KPI value while the KPI being pre-classified as failure, the data status provider 320 may continue to monitor the KPI until the end of the KPI period or the project closure date, whichever may be set. Similarly, upon being classified as success, the data status provider 320 may add such KPI to a list of KPIs associated with the “success” category in the historical data 335 for retraining the second trained data model 345-2 or the third trained data model 345-3 for improving the relevance of recommendations generated using these models 345-2 and 345-3. (See also Paragraph Numbers [0030], [0032], [0034], [0035], [0046], [0047], and [0049])).
generate, as a function of the performance range category, improvement data (Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. Paragraph Number [0057] teaches the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. An entity (e.g., a person, device, or AI system) may assist to provide a recommendation to adjust the −KPI target value in scenarios where the KPI may be classified as failure primarily because the target values may be not in-line with expected improvements, i.e., the target value being different from a historical target value set for the same KPI in the KPI cluster. Such target data adjustment may help to expand the scope of the recommendation to similar projects or initiatives and KPIs with a higher confidence (See also Paragraph Numbers [0020], [0033], [0034], [0076], and [0077])).
create an improvement plan as a function of the improvement data (Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. Paragraph Number [0057] teaches the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. An entity (e.g., a person, device, or AI system) may assist to provide a recommendation to adjust the −KPI target value in scenarios where the KPI may be classified as failure primarily because the target values may be not in-line with expected improvements, i.e., the target value being different from a historical target value set for the same KPI in the KPI cluster. Such target data adjustment may help to expand the scope of the recommendation to similar projects or initiatives and KPIs with a higher confidence. (See also Paragraph Numbers [0013], [0016], [0020], [0033], [0034], [0059] and [0078])).
wherein generating the improvement plan further comprises: generating a machine learning model, wherein the machine learning model inputs improvement data and outputs improvement plans (Paragraph Number [0043] teaches the data status provider 320 may correspond to a component for forecasting a future value of the KPI, predict a possibility of failure of the KPI, categorize the KPI based on the prediction, calculate KPI benefit value for the KPI, calculate a net present value of an initiative related to the KPI, categorize the initiative based on the calculated net present value. In some instances, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI. In one example, the data status provider 320 may access a first trained data model such as the first trained data model 345-1 (e.g., trained multivariate regression model) to forecast the future value. In another example, the data status provider 320 may use the influencing factors, or key attributes and features, of the identified relevant KPI cluster to train a new data model or retrain the first trained data model 345-1, such as the multivariate regression model., 0047, Paragraph Number [0073] teaches if the future value may be equal to or greater than the target KPI value while the KPI being pre-classified as failure, the data status provider 320 may continue to monitor the KPI until the end of the KPI period or the project closure date, whichever may be set. Similarly, upon being classified as success, the data status provider 320 may add such KPI to a list of KPIs associated with the “success” category in the historical data 335 for retraining the second trained data model 345-2 or the third trained data model 345-3 for improving the relevance of recommendations generated using these models 345-2 and 345-3. (See also Paragraph Numbers [0030], [0032], [0034], [0035], [0046], [0047], and [0049])).
update the improvement plan as a function of user feedback. (Paragraph Number [0043] teaches the data status provider 320 may correspond to a component for forecasting a future value of the KPI, predict a possibility of failure of the KPI, categorize the KPI based on the prediction, calculate KPI benefit value for the KPI, calculate a net present value of an initiative related to the KPI, categorize the initiative based on the calculated net present value. In some instances, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI. In one example, the data status provider 320 may access a first trained data model such as the first trained data model 345-1 (e.g., trained multivariate regression model) to forecast the future value. In another example, the data status provider 320 may use the influencing factors, or key attributes and features, of the identified relevant KPI cluster to train a new data model or retrain the first trained data model 345-1, such as the multivariate regression model., 0047, Paragraph Number [0073] teaches if the future value may be equal to or greater than the target KPI value while the KPI being pre-classified as failure, the data status provider 320 may continue to monitor the KPI until the end of the KPI period or the project closure date, whichever may be set. Similarly, upon being classified as success, the data status provider 320 may add such KPI to a list of KPIs associated with the “success” category in the historical data 335 for retraining the second trained data model 345-2 or the third trained data model 345-3 for improving the relevance of recommendations generated using these models 345-2 and 345-3. (See also Paragraph Number [0047])).
Both Gundimeda and Singh are directed to analysis of performance metrics. Gundimeda discloses generating system improvement data. Singh improves upon Gundimeda by disclosing using a machine learning model to take improvement data and determine a improvement plan. One of ordinary skill in the art would be motivated to further include using a machine learning model to take improvement data and determine a improvement plan, to efficiently utilize machine learning to be able to analyses and automatically create actionable plans.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of generating system improvement data in Gundimeda to further utilize a machine learning model to take improvement data and determine a improvement plan as disclosed in Singh, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gundimeda teaches generating system improvement data but does not explicitly teach training a machine learning model to determine inconsistent data and correct the inconsistent data by generating secondary prompts which is taught by the following citations from Simard:
train a first machine learning model using a first training data comprises inconsistent system data correlated to the organizational identifier (Paragraph Number [0042] teaches user-selectable features may be stored, and an option to select one or more of the user-selectable features may be presented to the user, where the one or more user-selected features include one or more features selected from among the user-selectable features. The user-selectable features may include one or more of a built-in feature, a user-generated feature, a trained classifier, a trained segment extractor, or a dictionary. The user-selectable features may include features generated by a plurality of users. The user-selectable features may be stored on a commonly accessible system shared by multiple users. Paragraph Number [0044] teaches computer-readable media embodying computer-usable instructions are provided for facilitating a method of interactive feature selection for machine learning. A first set of data items is provided, where one or more of the data items have been previously labeled as examples of a particular class of data item. A classifier is utilized to determine predicted labels for one or more of the data items. One or more data items having a discrepancy between a previous label and a predicted label are identified. Via a user interface, an indication is presented of the one or more data items having the discrepancy between the previous label and the predicted label. The user interface includes a feature-selection interface configured to receive a user selection of one or more features that are usable as input features to train the classifier).
identify the inconsistent system data as a function of the trained first machine learning model (Paragraph Number [0044] teaches computer-readable media embodying computer-usable instructions are provided for facilitating a method of interactive feature selection for machine learning. A first set of data items is provided, where one or more of the data items have been previously labeled as examples of a particular class of data item. A classifier is utilized to determine predicted labels for one or more of the data items. One or more data items having a discrepancy between a previous label and a predicted label are identified. Via a user interface, an indication is presented of the one or more data items having the discrepancy between the previous label and the predicted label. The user interface includes a feature-selection interface configured to receive a user selection of one or more features that are usable as input features to train the classifier).
generate a second query as a function of the identified inconsistent system data (Paragraph Number [0044] teaches a search query is received via the search interface. The search query is executed on the first set of data items, where search results are generated. The search results are presented to the user. A user input is received that selects the search query as a first feature for training the classifier. Via the user interface, a user input is received that selects a dictionary as a second feature for training the classifier, where the dictionary includes words that define a concept that corresponds to the second feature. The classifier is trained with the search query and the dictionary as input features. The classifier is utilized to determine new predicted labels for one or more of the data items. One or more data items having a discrepancy between a previous label and a new predicted label are identified. Via the user interface, an indication is presented of the one or more data items having the discrepancy between the previous label and the new predicted label. Via the user interface, a user selection of one or more features is received. The classifier is trained with the one or more user-selected features as input features).
automatically correct the identified inconsistent system data as a function of the second query (Paragraph Number [0348] teaches classification as used herein is the task of predicting a class label given an input item. For that purpose, use is made of a supervised learning algorithm, which can automatically infer a function that maps an input feature representation to a class label from a set of labeled items, i.e., items for which the correct class has been identified by a human labeler. A label item (x,y) is denoted, where x denotes its raw representation x and y denotes its label. Paragraph Number [0405] teaches each click on the bounding box of a visible token (e.g., word) toggles the state of the token. The distinction between Begin and Continue is a rather subtle one; it allows the distinction between a long segment and two adjacent ones. This is a UX challenge. Once a visible token has been clicked, it is constrained. Tokens that have never been clicked are unconstrained. For every operator click on a visible token, a constraint has been added/changed/removed. This triggers a dynamic programming optimization on the trellis to find the new resulting optimal path in O(n) steps. This will likely change the default labels of the remaining unconstrained tokens. In other words, the system is working with the operator to always display the best solution given the operator constraints. For instance, one click anywhere on a missed address is likely to trigger the whole address to be labeled as a segment correctly).
Both the combination of Gundimeda and Singh and Simard are directed to analysis of performance metrics. The combination of Gundimeda and Singh discloses generating system improvement data. Simard improves upon the combination of Gundimeda and Singh by disclosing training a machine learning model to determine inconsistent data and correct the inconsistent data by generating secondary prompts. One of ordinary skill in the art would be motivated to further include training a machine learning model to determine inconsistent data and correct the inconsistent data by generating secondary prompts, to efficiently identify data that is incorrect or in conflict and unify the data through identification of the correct data and utilize the correct data as input to train a machine learning process.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of generating system improvement data in the combination of Gundimeda and Singh to further utilize training a machine learning model to determine inconsistent data and correct the inconsistent data by generating secondary prompts as disclosed in Simard, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, claim 11 recites a method that performs steps substantially similar to those found in claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claims 2 and 12, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 11 respectively.
In addition, Gundimeda teaches:
wherein receiving the system data further comprises identifying inconsistencies contained in the system data utilizing a language processing model (Paragraph Number [0036] teaches the information extraction engine 110 is configured to receive the extracted data from the web scraping and crawling module 106 and communicate with the analysis module 112 to facilitate analyzing the received data. The analysis module 112 include, but not limited to, a Named Entity Recognizer (NER), a rule processing engine, a set of machine learning classification libraries and a thesaurus for handling pre-stored vocabularies. The analysis module 112 performs one or more analytical operations such as, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning on the extracted data to make it more meaningful for the one or more end users. The analysis module 112 also performs deduplication process to filter duplicated data within the extracted data. Further, the analysis module 112 classifies the extracted data particularly if the extracted data is bulky. In an embodiment of the present invention, maximum entropy algorithm is used by the analysis module 112 for classifying and determining topic of the extracted data. Paragraph Number [0062] teaches the extracted data is analyzed by performing one or more analytical operations on the extracted data. In an embodiment of the present invention, the one or more analytical operations include, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning that facilitate in making the extracted data more meaningful for one or more end users. The one or more analytical operations also include deduplication process to filter duplicated data within the extracted data. (See also Paragraph Number [0045])).
As per claims 3 and 13, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 2, and 11 and 12 respectively.
In addition, Gundimeda teaches:
wherein the memory contains instructions further configuring the at least a processor to automatedly correct the identified inconsistencies (Paragraph Number [0036] teaches the information extraction engine 110 is configured to receive the extracted data from the web scraping and crawling module 106 and communicate with the analysis module 112 to facilitate analyzing the received data. The analysis module 112 include, but not limited to, a Named Entity Recognizer (NER), a rule processing engine, a set of machine learning classification libraries and a thesaurus for handling pre-stored vocabularies. The analysis module 112 performs one or more analytical operations such as, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning on the extracted data to make it more meaningful for the one or more end users. The analysis module 112 also performs deduplication process to filter duplicated data within the extracted data. Further, the analysis module 112 classifies the extracted data particularly if the extracted data is bulky. In an embodiment of the present invention, maximum entropy algorithm is used by the analysis module 112 for classifying and determining topic of the extracted data. Paragraph Number [0062] teaches the extracted data is analyzed by performing one or more analytical operations on the extracted data. In an embodiment of the present invention, the one or more analytical operations include, but not limited to, text analysis, indexing, entity recognition, Part-Of-Speech (POS) tagging, classification and correction, co-reference resolution, automatic linking of phrases and words, auto-reviewing, natural language processing and machine learning that facilitate in making the extracted data more meaningful for one or more end users. The one or more analytical operations also include deduplication process to filter duplicated data within the extracted data. (See also Paragraph Number [0045])).
As per claims 4 and 14, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 11 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein classifying the system data and user data comprises: receiving performance category training data correlating the system data and user data to a plurality of ideal performance metrics (Paragraph Number [0030]-[0035] teach during the training mode, the data retriever 130 may receive the input data such as the historical data 335 corresponding a historical project from the data source. The historical data 335 may include the project data 204 having the project set-up data, the process hierarchy, the KPI and initiative data, and the benchmarking data related to the historical project. The project set-up data may include a project type, industry/function, a geographical indicator (e.g., region, country, etc.), location type (e.g., manufacturing unit, retail store, etc.), a project start date, a project closure date, and a period extending therebetween defining an operational duration (or project period) of such project. The KPI data may include KPI name, KPI improvement trend, KPI user inputs (e.g., values and additional parameters related to a KPI), KPI formulas, KPI values (e.g., current KPI values, target KPI values, cumulative values, etc. at various intervals during the project period, and initiative(s) related to the KPI. Each of the selected KPIs may be associated with multiple set labels during the respective project periods in the training data or historical data 335. In one example, the set labels may include at least one label indicating “failure” and a most recent label indicating “success” in the historical data 335).
training a performance classifier as a function of the performance category training data (Paragraph Number [0046] teaches the data status provider 320 may predict a possibility of failure of the KPI based on the historical data 335 to pre-classify the KPI. The data status provider 320 may predict the possibility of failure using the second trained data model 345-2 based on a comparison between attributes/features of the KPI and those of a list of KPIs associated with a “failed” category in the historical data 335. Upon comparison, the data status provider 320 may pre-classify the KPI as failure based on the KPI belonging or similar to any KPI that has been labelled as failure in the list. (See also Paragraph Number [0047])).
outputting the performance range category as a function of the performance classifier (Paragraph Number [0051] teaches the data status provider 320 may send the initiative, the related KPI, and their respective categorizations to the recommender 325 for providing relevant recommendations. In some examples, the data status provider 320 may display the calculated values, related attributes, as well as categorizations on the output device 725 of the system 110. For example, the data status provider 320 may display on a dashboard of the system 110 the forecasted KPI values, the initiative cost values, the initiative cost benefit (or cost performance) at different intervals during the corresponding KPI period and initiative period filtered by, for example, (i) a timeline as illustrated in FIG. 6J, (ii) a business function as indicated in FIG. 6K, (iii) a change in KPI value, as indicated in FIG. 6L, to showcase KPI values and costs trends until the project closure date, and (iv) initiatives related to the KPI, as indicated in FIG. 6M, to showcase an initiative-level value and costs trends until the project closure date. In some instances, the data status provider 320 may also display cumulative KPI values and the KPI benefit value on the output device 725 of the system. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. (see also 0033, 0057-0059, 0077)).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claims 5 and 15, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 11 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein the memory contains instructions configuring further the at least a processor to generate a performance report comprising a plurality of inadequate performance metrics. (Paragraph Number [0047] Subsequently, the data status provider 320 may categorize the KPI into a set category, for example, namely “success” and “failure” based on one of the future value and the pre-classification of the KPI. For instance, the data status provider 320 may categorize the KPI as failure based on at least the future KPI value being less than the target KPI value at the end of the KPI period or at the project closure date. Else, the data status provider 320 may categorize the KPI as success. In another example, the data status provider 320 may categorize the KPI based on a combination of the future value and the pre-classification. For instance, the data status provider 320 may categorize the KPI as failure based on both the future value being less than the target KPI value at the end of the KPI period or at the project closure date as well as the KPI being pre-classified as failure. Else, the data status provider 320 may categorize the KPI as success. Upon being categorized as failure, the data status provider 320 may add such KPI to the list of KPIs associated with the “failed” category in the historical data 335 for retraining the second trained data model 345-2. However, if the future value may be equal to or greater than the target KPI value while the KPI being pre-classified as failure, the data status provider 320 may continue to monitor the KPI until the end of the KPI period or the project closure date, whichever may be set. Similarly, upon being classified as success, the data status provider 320 may add such KPI to a list of KPIs associated with the “success” category in the historical data 335 for retraining the second trained data model 345-2. If the KPI may be pre-classified as success based on a change in value related to the KPI due to a received recommendation, the data status provider 320 may record such recommendation or a value related thereto as an ideal recommendation for similar type of KPIs in future projects. Moreover, the retrained second data model 345-2 may be utilized by the recommender 325 to improve the relevance of recommendations for improving the KPI performance. In other instances, the list of KPIs associated with the “success” category may also be used by the data classifier 310 for training or retraining a third data model 345-3 for recommending one or more relevant KPIs that may be selected or tracked based on the input data such as a project type and an expected outcome (e.g., a KPI benefit value greater than a threshold target KPI benefit value). The third trained data model 345-3 may include any suitable classification data model known in the art including, but not limited to, SVM model and Naïve Bayes model; however, other instances may include a statistical data model or a combination of statistical and classification data model. (See also Paragraph Numbers [0030]-[0034], [0039], and [0046])).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claims 6 and 16, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 5, and 11 and 15 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein generating a performance report comprises ranking the plurality of inadequate performance metrics based on a level of underperformance (Fig. 6L teaches a representation of the dashboard and contains representations of arrows above column indicating ordering high to low).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claims 8 and 18, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 11 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein generating the improvement data comprises incorporating a performance report. (Paragraph Number [0051] teaches the data status provider 320 may send the initiative, the related KPI, and their respective categorizations to the recommender 325 for providing relevant recommendations. In some examples, the data status provider 320 may display the calculated values, related attributes, as well as categorizations on the output device 725 of the system 110. For example, the data status provider 320 may display on a dashboard of the system 110 the forecasted KPI values, the initiative cost values, the initiative cost benefit (or cost performance) at different intervals during the corresponding KPI period and initiative period filtered by, for example, (i) a timeline as illustrated in FIG. 6J, (ii) a business function as indicated in FIG. 6K, (iii) a change in KPI value, as indicated in FIG. 6L, to showcase KPI values and costs trends until the project closure date, and (iv) initiatives related to the KPI, as indicated in FIG. 6M, to showcase an initiative-level value and costs trends until the project closure date. In some instances, the data status provider 320 may also display cumulative KPI values and the KPI benefit value on the output device 725 of the system. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claims 9 and 19, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 11 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein generating the improvement data comprises: receiving improvement training data correlating a plurality of elements of a performance report to a plurality of improvement features (Paragraph Number [0034] teaches the data refiner 140 may receive the historical data 335 as training data from the data retriever 130. The data refiner 140 may be coupled to a data classifier 310 corresponding to a component for training one or more data models using suitable supervised learning techniques known in the art. For example, the data refiner 140 or the data classifier 310 may train a first data model for forecasting a future value of a KPI during the live mode. In one instance, the first data model may be a statistical data model such as a multivariate regression model being trained based on the attributes and/or features of KPIs in the historical data 335, which may be used to identify the key influencing factors (e.g., KPI attributes/features including initiatives related thereto) impacting a KPI and forecast the values based on those influencing factors. Paragraph Number [0035] teaches the data classifier 310 may train the first data model (e.g., multivariate regression model), the second data model (e.g., SVM model), and the third data model (e.g., SVM model) based on the features of the KPIs in the KPI clusters and the corresponding outcome categories/labels (e.g., success, failure, etc.) related thereto to provide trained data models for use during the live mode. The trained data models may be sent to the project value predictor 150 by the data classifier 310, or stored in the data repository such as the database 414 for access by the project value predictor 150 or any other component operationally coupled to the system 110. Paragraph Number [0058] teaches the recommender 325 may provide a recommendation D based inputs from an entity (e.g., a person, device, or AI system) on what corrective actions were taken in other similar historical projects where that KPI or similar KPI(s) may be classified as failure but was eventually updated to success during the project period or at the project closure date. The recommender 325 may provide the one or more of the generated recommendations using on the second trained data model 345-2 or the third trained data model 345-3 based on attributes/features of the KPI and/or project type. The generated recommendations may be sent to the recalibrator 330 or stored in the database 414 for future access. (See also Paragraph Numbers [0016], [0018], [0033], [0040], [0043], [0046], [0047], and [0051])).
training an improvement classifier as a function of the improvement training data (Paragraph Number [0047] teaches, the data status provider 320 may categorize the KPI into a set category, for example, namely “success” and “failure” based on one of the future value and the pre-classification of the KPI. For instance, the data status provider 320 may categorize the KPI as failure based on at least the future KPI value being less than the target KPI value at the end of the KPI period or at the project closure date. Else, the data status provider 320 may categorize the KPI as success. In another example, the data status provider 320 may categorize the KPI based on a combination of the future value and the pre-classification. For instance, the data status provider 320 may categorize the KPI as failure based on both the future value being less than the target KPI value at the end of the KPI period or at the project closure date as well as the KPI being pre-classified as failure. Else, the data status provider 320 may categorize the KPI as success. Upon being categorized as failure, the data status provider 320 may add such KPI to the list of KPIs associated with the “failed” category in the historical data 335 for retraining the second trained data model 345-2. However, if the future value may be equal to or greater than the target KPI value while the KPI being pre-classified as failure, the data status provider 320 may continue to monitor the KPI until the end of the KPI period or the project closure date, whichever may be set. Similarly, upon being classified as success, the data status provider 320 may add such KPI to a list of KPIs associated with the “success” category in the historical data 335 for retraining the second trained data model 345-2. If the KPI may be pre-classified as success based on a change in value related to the KPI due to a received recommendation, the data status provider 320 may record such recommendation or a value related thereto as an ideal recommendation for similar type of KPIs in future projects. Moreover, the retrained second data model 345-2 may be utilized by the recommender 325 to improve the relevance of recommendations for improving the KPI performance. In other instances, the list of KPIs associated with the “success” category may also be used by the data classifier 310 for training or retraining a third data model 345-3 for recommending one or more relevant KPIs that may be selected or tracked based on the input data such as a project type and an expected outcome (e.g., a KPI benefit value greater than a threshold target KPI benefit value). The third trained data model 345-3 may include any suitable classification data model known in the art including, but not limited to, SVM model and Naïve Bayes model; however, other instances may include a statistical data model or a combination of statistical and classification data model. (See also Paragraph Numbers [0033], [0034], [0039], and [0058])).
outputting the improvement data as a function of the improvement classifier (Paragraph Number [0051] teaches the data status provider 320 may send the initiative, the related KPI, and their respective categorizations to the recommender 325 for providing relevant recommendations. In some examples, the data status provider 320 may display the calculated values, related attributes, as well as categorizations on the output device 725 of the system 110. For example, the data status provider 320 may display on a dashboard of the system 110 the forecasted KPI values, the initiative cost values, the initiative cost benefit (or cost performance) at different intervals during the corresponding KPI period and initiative period filtered by, for example, (i) a timeline as illustrated in FIG. 6J, (ii) a business function as indicated in FIG. 6K, (iii) a change in KPI value, as indicated in FIG. 6L, to showcase KPI values and costs trends until the project closure date, and (iv) initiatives related to the KPI, as indicated in FIG. 6M, to showcase an initiative-level value and costs trends until the project closure date. In some instances, the data status provider 320 may also display cumulative KPI values and the KPI benefit value on the output device 725 of the system. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. (see also 0033, 0057-0059, 0077)).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claims 10 and 20, the combination of Gundimeda, Singh, and Simard teaches each of the limitations of claims 1 and 9, and 11 and 19 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model to take improvement data and determine a improvement plan which is taught by the following citations from Singh:
wherein generating the improvement data further comprises: transmitting the performance report to a user device (Paragraph Number [0051] teaches the data status provider 320 may send the initiative, the related KPI, and their respective categorizations to the recommender 325 for providing relevant recommendations. In some examples, the data status provider 320 may display the calculated values, related attributes, as well as categorizations on the output device 725 of the system 110. For example, the data status provider 320 may display on a dashboard of the system 110 the forecasted KPI values, the initiative cost values, the initiative cost benefit (or cost performance) at different intervals during the corresponding KPI period and initiative period filtered by, for example, (i) a timeline as illustrated in FIG. 6J, (ii) a business function as indicated in FIG. 6K, (iii) a change in KPI value, as indicated in FIG. 6L, to showcase KPI values and costs trends until the project closure date, and (iv) initiatives related to the KPI, as indicated in FIG. 6M, to showcase an initiative-level value and costs trends until the project closure date. In some instances, the data status provider 320 may also display cumulative KPI values and the KPI benefit value on the output device 725 of the system. Paragraph Number [0035] teaches the data classifier 310 may train the first data model (e.g., multivariate regression model), the second data model (e.g., SVM model), and the third data model (e.g., SVM model) based on the features of the KPIs in the KPI clusters and the corresponding outcome categories/labels (e.g., success, failure, etc.) related thereto to provide trained data models for use during the live mode. The trained data models may be sent to the project value predictor 150 by the data classifier 310, or stored in the data repository such as the database 414 for access by the project value predictor 150 or any other component operationally coupled to the system 110. (See also Paragraph Numbers [0034] and [0043]-[0047])).
receiving user feedback comprising prioritization of improving an inadequate performance metric of a plurality of inadequate performance metrics (Paragraph Number [0058] teaches the recommender 325 may provide a recommendation D based inputs from an entity (e.g., a person, device, or AI system) on what corrective actions were taken in other similar historical projects where that KPI or similar KPI(s) may be classified as failure but was eventually updated to success during the project period or at the project closure date. The recommender 325 may provide the one or more of the generated recommendations using on the second trained data model 345-2 or the third trained data model 345-3 based on attributes/features of the KPI and/or project type. The generated recommendations may be sent to the recalibrator 330 or stored in the database 414 for future access. Paragraph Number [0018] teaches the data retriever 130 may correspond to a component for receiving input data including one or more input parameters corresponding to an active project and/or a historical project. As illustrated in FIG. 2, the input data may include project data 204 and recommendation feedback data 206. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. (See also Paragraph Number [0020])).
inputting the user feedback into the improvement classifier as an input (Paragraph Number [0018] teaches the data retriever 130 may correspond to a component for receiving input data including one or more input parameters corresponding to an active project and/or a historical project. As illustrated in FIG. 2, the input data may include project data 204 and recommendation feedback data 206. Paragraph Number [0052] teaches the recommender 325 may correspond to a component for providing a recommendation to improve the financial performance of the KPI (or the KPI benefit value). In one embodiment, the recommender 325 may provide a recommendation in response to both the KPI and the initiative being categorized as failure by the data status provider 320. The recommendation may be provided to assist in initiating a process to optimize the financial performance of the KPI (or the KPI benefit value) and/or the net present value, to enhance the overall project value. The recommendation may be based on one of a user feedback, a set formula for the KPI, and a historical recommendation for the KPI. (See also Paragraph Number [0020])).
outputting the improvement data incorporating the user feedback (Paragraph Number [0057] teaches the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. For instance, an entity (e.g., a person, device, or AI system) operating as a subject matter expert may assist to provide a standard set of recommendations to start new initiatives/take additional actions which could help improve the KPI performance based on the KPI name, project type etc. Paragraph Number [0077] teaches a recommendation is provided in response to both the KPI and the initiative being categorized as failure. The recommendation may initiate a process to optimize the KPI performance, where the initiated process may recalibrate a parameter including at least one of the KPI, the initiative, the KPI period, the target KPI value, and the predefined closure date. In one example, a recommendation A may be provided based on a set benefit formula associated with the KPI. Paragraph Number [0059] teaches the recalibrator 330 may correspond to a component for initiating a recalibration process for improving the financial performance of the KPI (or KPI benefit value) and/or the net present value of the related initiatives based on the provided recommendations. For example, the recalibrator 330 may select one of the recommendations A, B, C, and D based on the maturity of the recommendation model such as the second trained data model 345-2 and the third trained data model 345-3).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2018/0150562 to Gundimeda et al. (hereafter referred to as Gundimeda) in view of U.S. Patent Application Publication Number 2022/0300881 to Singh et al. (hereafter referred to as Singh) in further view of U.S. Patent Application Publication Number 2015/0019463 to Simard et al. (hereafter referred to as Simard) and in even further view of U.S. Patent Application Publication Number 2015/0309506 to Naduthota et al. (hereafter referred to as Naduthota).
As per claims 7 and 17, the combination of Gundimeda, Singh, and Simard teaches each of the claim limitations of claims 1, 5, and 6, and 11, 15, and 16 respectively.
Gundimeda teaches generating system improvement data but does not explicitly teach using a machine learning model that uses fuzzy logic to take improvement data and determine a improvement plan which is taught by the following citations from Naduthota:
wherein ranking the plurality of inadequate performance metrics comprises utilizing a fuzzy set inference system (Paragraph Numbers [0064]-[0067] teach an example continuous performance index (CPI) system 500 according to this disclosure. As shown in FIG. 5A, the CPI system 500 includes inputs 505-515, a fuzzy logic block 520, and an output 525. The inputs 505-515 may be KPIs, such as RPI, OSI, and error standard deviation values. The inputs 505-515 are processed by the fuzzy logic block 520. Each input can be processed according to its predefined acceptable range. For example, as shown above, the OSI can be acceptable between 0 and 0.5. The fuzzy logic block 520 operates to generate a generalized CPI value as the output 525. The generalized CPI can be defined based upon the existing discrete levels of the overall performance rating. The types of input and output functions (such as triangular, Gaussian, and the like) can be selected to suit the properties of the corresponding index. Any number of KPIs can be associated with a single generalized CPI. The fuzzy logic block 520 can use a Fuzzy Associative Memory (FAM) table 530, which is shown in FIG. 5B. The FAM table 530 defines the rules governing the overall implementation logic, such as “if-then” rules. For example, if the RPI 535 is good, the OSI 540 is good, and the std-dev 545 is good, the generalized CPI 550 is Excellent. In some embodiments, the importance of various input indices towards the overall performance rating can be considered while defining the fuzzy rules. For example, rules 536 indicate that the OSI 540 index is more critical when defining the overall performance of the control loop compared to the other two indices. In other examples, other KPIs may be more important. The fuzzy logic block 520 uses Mamdani-based fuzzy logic based on a min-max implication scheme to combine inputs 560-570 for each rule 1-8 as shown in FIG. 5C. Once the inputs 560-570 are combined for each rule 1-8, each of the results 575 may be combined, and the fuzzy logic block 520 can use a defuzzification method of Mean of Maximum (MOM) to derive a generalized CPI 576. (See also Paragraph Numbers [0058] and [0059])).
Both the combination of Gundimeda, Singh, and Simard and Naduthota are directed to analysis of performance metrics. The combination of Gundimeda, Singh, and Simard discloses generating system improvement data. Naduthota improves upon the combination of Gundimeda, Singh, and Simard by disclosing using a machine learning model that uses fuzzy logic to take improvement data and determine a improvement plan. One of ordinary skill in the art would be motivated to further include using a machine learning model that uses fuzzy logic to take improvement data and determine a improvement plan, to efficiently utilize machine learning to be able to analyses and automatically create actionable plans.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of generating system improvement data in the combination of Gundimeda, Singh, and Simard to further utilize a machine learning model that uses fuzzy logic to take improvement data and determine a improvement plan as disclosed in Naduthota, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Argument
Applicant’s arguments filed 3/5/2026 have been fully considered but they are not fully persuasive.
Applicant argues that the previously cited reference does not teach the newly amended portions including the new limitations recited by the independent claims. (See Applicant’s Remarks, 3/5/2026, pgs. 8-10). Examiner notes that new citations from the previously cited references and the new Simard reference have been applied to the newly presented claim limitations as indicated in the above in the new 35 USC 103 rejection. Examiner has added and emphasized specific portions of the Simard reference to read on the new independent claim language. As such, Applicant’s arguments directed towards the previous rejection are moot. In response to Applicant’s arguments, Examiner directs Applicant to review the new citations and explanations provided in the new 35 USC 103 rejection presented above.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787.
/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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