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 .
The following FINAL Office Action is in response to Applicant’s communication filed 10/29/2025 regarding Application 18/654,473.
Status of Claim(s)
Claim(s) 1, 5-8, 11, and 15-18 is/are currently pending and are rejected as follows.
Response to Arguments – 112 Rejection
Applicant’s arguments and amendments in regards to the previously applied 112 rejection have been fully considered and deemed persuasive.
Examiner therefore withdraws the previously applied 112 rejections.
Response to Arguments – 101 Rejection
Applicant’s arguments and amendments in regards to the previously applied 101 rejection have been fully considered and deemed persuasive.
Examiner therefore withdraws the previously applied 101 rejection.
Response to Arguments – 103 Rejection
Applicant’s arguments in regards to the previously applied 103 rejection have been fully considered but are not deemed persuasive.
Applicant argues that the art of Krishnaswamy does not apply the to the claimed invention, as Krishnaswamy does not provide “customer-centric semantic scoring” nor “opportunity maps and structured insights for guiding product and market strategy” and is more It-centric, rather than customer-centric and innovation-focused. Therefore Krishnaswamy cannot be applied.
Examiner does not find this argument persuasive as the arguments presented are not reflective of what is present within the claim language. Applicant’s claim language is presented as “a [Jobs To Be Done] formulation modules to identify data and compile entities to determine a list of JTBD to achieve a desired metric or outcome. These actions are not limited to the scope of a market strategy and can include IT focused solutions such as the one provided by Krishnaswamy, as the data pulled form entities is also not limited exclusively to customers. Therefore due to interpretation of the claim language as currently recited both in view of the specification and under broadest reasonable interpretation, the art of Krishnaswamy is applicable to the limitations present within Applicant’s amended claims.
Applicant’s additional arguments in regards to the previously applied 103 rejection are rendered moot in view of the amended prior art rejection below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5-8, 11, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnaswamy (US 2021/0192412 A1) in view of Cao (US 2008/0167930 A1) and Glaser (US 2023/0297886 A1).
Claim(s) 1 and 11 –
Krishnaswamy discloses the following:
A) receiving input data, in an input module, in a desired format including text, CVS, JSON, voice or video, and processing the input data (Krishnaswamy: Paragraph 339, “Our Cognitive Intelligent Autonomous Transformation System (Master CIATSFABI Transformation system based on SAP Leonardo based on AI ML on RPA, NLP technologies installed on Cloud aka sidecar Transformation System) to interact with customer's systems to produce the ultimate Customer's CIATSFABI system in the following phases:”; Paragraph 340, “A. Autonomous/Semi-autonomous Pre-Discovery Phase where the Master CIATSFABI interaction with customer's existing system generates:”; Paragraph 341, “1) IndustryRef CIATSFABI for the industry the company belong to, where best practice configuration data, sample transaction data, historical data will be used, will interact with existing Customer's system to generate initial set of digital assistants (advanced computer programs that use Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML) that simulate conversation with people over internet), possible automation scenarios such as RPA scenarios, AI Scenarios.”; Paragraph 343, “3) Digital assistants are also able to access online information form the internet such as weather, stock prices, traffic conditions, schedules, news, schedule calendar events, manage emails, to do lists etc., and present the same in a clear, concise, and interesting manner to the user/system and can also act on voice inputs.”; Paragraph 346, “B. Autonomous/Semi-autonomous Discovery Phase where Master CIATSFABI interaction with customer's existing system and IndustryRef generates in-depth understanding of the goals, scope, and limitations:”; Paragraph 351, “C. Autonomous/Semi-autonomous Analysis Phase where further business intelligence and automation opportunities from outputs from Pre-Discovery and Discovery phases to decide in implementation of target system (a. Green-Field or New Implementation or b. Brown-Field or Systems Conversion or c. Global Consolidation of Regional systems)”; Paragraph 356, “5) Finally, all external events affecting the company will be analyzed and advice will be provided from the Master CIATSFABI system to customer's CIATSFABI system, each iteration of the product will provide superior intelligence than the previous one.”)
B) extracting a plurality of entities related to the approach of JTBD [Jobs To Be Done] from the processed input data, in a feature extraction module using machine learning and artificial intelligence techniques… (Krishnaswamy: Paragraph 146, "Ticket Intelligence (Self-service digital interface, single level ticket categorization, similar tickets, ticket routing completion, spam classifier, Jam article recommendations, ticket classification and entity extraction NLP, Estimated time for completion)"; Paragraph 318, "CIASFAB monitors both the internal events including company or organizational milestones, new product launches, new incentive programs, BoD/Shareholder meetings and significant external world events including impending political/economic/legal/tax rate changes global mergers & acquisitions, new technology innovation, producing Strength, weakness, opportunity, threats (SWOT) analysis reports that can significantly bring benefit to the customer company and its stakeholders."; Paragraph 341, "1) IndustryRef CIATSFABI for the industry the company belong to, where best practice configuration data, sample transaction data, historical data will be used, will interact with existing Customer's system to generate initial set of digital assistants ( advanced computer programs that use Artificial Intelligence (Al), Natural Language Processing (NLP), Machine Learning (ML) that simulate conversation with people over internet), possible automation scenarios such as RPA scenarios, AI Scenarios."; Paragraph 374, "The Cognitive Intelligent Autonomous Transformation System also is designed to understand the softer, cultural aspects of the company, the value proposition of their products & services, the emphasis provided by employees and management to handle the customer, suppliers and other stakeholders of the company, nurturing and promoting the values the company stands for."; Paragraph 401, “1. Digital Assistants that identify core business processes that have potential automation opportunities which are currently executed manually at enormous cost. Also checks how well the automation opportunity compares with best practices within same industry and across all industries.”)
C) identifying patterns and relationships in the extracted plurality of entities and grouping them in desired manner for actionability in a feature processing module… and uses a situation vs needs matrix…and emotion to identify opportunity levels…(Krishnaswamy: Paragraph 137, “Sentiment engagement score (Sentiment analysis on product reviews,”; Paragraph 158, "Intelligence sales (Relationship Intelligence, Deal intelligence, Pipeline management, Predictive forecasting"; Paragraph 497, “Master CIATSFABI (2) setup wizards in conjunction with IndustryRef CIATSFABI (37) produce opportunity matrix, solution enhancement to Ideal_virtual (latest version 28), chosen virtual_company system (can be one or more versions lower than the latest 27).”; Paragraph Paragraph 557, "Evolving Cognitive Business Intelligence (Bl) trends within the company (15)”; Paragraph 516, “The Cognitive Intelligent Autonomous Transformation System (Master CIATSFABI) (2) in conjunction with IndustryRef CIATSFABI (37) generates proposed Cognitive Intelligent Autonomous Transformation System (Customer's CIATSFABI (19)), analyzes the existing information system (e.g. SAP system—Enterprise Resource Planning, Customer Relationship Management. BW, eCommerce.) with customer's intended ultimate platform (Ideal_virtual) and finds the best versions of the software as recommended by SAP through Product Availability Matrix. If an on-premise is desired by the customer but the best versions and features suiting customer's needs are only available in Cloud offering, the same will be recommended but the customer can still insist on only on-premise implementation with the understanding less optimal ultimate system will be available and that will be the company's transformed target system (virtual_company aka Customer's CIATSFABI). “; Paragraph 520, “Proposed Cognitive Intelligent Autonomous Transformation System Setup Wizard (1), with Guidance from Artificial intelligence Master DB-Master proposed Cognitive Intelligent Autonomous Transformation System (2) in conjunction with IndustryRef CIATSFABI (37), after connection test (6) during Pre-Discovery, Discovery and Analysis Phase (7) will read from Existing information system (4) (26) will produce “identify Opportunity Matrix” which essentially identifies all solution advancement possible from current system to ultimate information system (28), Chosen Transformed target system-virtual_company (27). The Pre-Discovery, Discovery and Analysis Phase (7) also produces “platform specifics—reports (21) and roadmaps” which is also used in the possible solutions in the ultimate information system (28). Pre-Discovery, Discovery and Analysis Phase (8) also prioritize the solutions based on benefits with user's input (22) and will be used in the possible solutions in the ultimate information system (28), Chosen Transformed target system-virtual_company (27) “; Paragraph 558, “Evolving External cognitive Business Intelligence (Bl) trends"; Paragraph 569, "Release latest version of Master CIATSFABI every 3 months (30 37) with architectural platform (36) and updates from External events (34) at customer's initiation can generate Customer's CIATSFABI-Basic (21), Advanced (22), Ideal_ virtual (28 latest version) and virtual_company (27 /19/10 may be lower version(s)) and finally rework/regenerate all actionable Business Intelligence reports, checklists & roadmaps,"; Paragraph 580, "Official release of Master proposed Cognitive Intelligent Autonomous Transformation System Artificial Intelligence Database (2) is released every 3 months (28) and with external events/system (29), the Cognitive system-Basic (21), Advanced (22), Ideal_ virtual (34) and virtual_company ( 10) are reworked and all actionable Business Intelligence reports, checklists & roadmaps, are regenerated.")
D) assigning a salience value to each of the plurality of extracted entity in an opportunity prioritization/prediction module allows the user to use entities…and emotions to identify the opportunities of highest interest (Krishnaswamy: Paragraph 137, “Sentiment engagement score (Sentiment analysis on product reviews,”; Paragraph 521, “Proposed Cognitive Intelligent Autonomous Transformation System Setup Wizard (1), with Guidance from Artificial intelligence Master DB-Master proposed Cognitive intelligent automation solution/system (2), after connection test (6) during Pre-Implementation and Implementation Phase (8) will use Pre-Discovery, Discovery and Analysis Phase results (7) along with SWOT (Strength, weakness, opportunity & Threats) analysis of external events/system (9) will produce “Quick wins” (35) and solutions based on complexity (Moderate/Complex/Futuristic) (20) of solutions for ultimate information system (28), Chosen Transformed target system-virtual_company (27). The Implementation Phase (8) will refine and fine tune “platform specifics—reports (21) and roadmaps and also refine and fine tune prioritize the solutions based on benefits with user's input (22) and will be so customer 152451”; Table 1, “1. Company 2. Vision 3. Mission 4. Objectives 5. Strategy 6. Action Plan 7. Existing Systems 8. Enterprise Resource Planning, eCommerce, Business Warehouse (BW). 9. Industry Solution 10. Core Business Process, Reports, Interfaces (RICEF) 11. Automation Opportunities 12. Priorities 13. 14. Best Practice Gaps -Inter-Intra Industry 15. Most used Business Process in the industry 16. Most used Business Process across the industries 17. Core Business Processes 18. Automation Opportunities 19. User input Priorities 20. 21. User/Management Input-Prioritization 22. Infrastructure 23. Product Portfolio Choices 24. Core Business Process, Reports, Interfaces (RICEF) Choices 25. Automation Opportunities 26. User input Priorities 27. Cognitive Intelligent Automation Setup Wizard 28. Cognitive Automation Analyzer 29. Company Configuration 30. Company Knowledgebase 31. Governance 32. Master proposed Cognitive intelligent automation solution/system 33. Virtual company proposed Cognitive Intelligent Autonomous Transformation 34. License Validation Phase 35. Discovery Phase 36. Implementation Phase 37. Enterprise Resource Planning, eCommerce, Business Warehouse. 38. Manufacturing Systems Product Lifecycle Management, Big Data 39. Identify Opportunity Matrix 40. Understand Platform Specifics 41. Prioritize based on Benefits/Customer Inputs 42. Quick wins complexity 43. Understand Platform Specifics 44. Prioritize based on Benefits/Customer Inputs 45. Artificial intelligence, Machine Learning, Cognitive, Deep Learning/Neural 46. 47. 48. SAP Applications 49. 50. Moderate 51. Complex 52. Futuristic 53. VirtualBuild Analyzer 54. PreBuild_BOTS software robots/Virtual agents proposed Cognitive Intelligent 55. proposed Cognitive Intelligent Autonomous Transformation System 56. Ideal Virtual Enterprise Systems proposed Cognitive intelligent automation 57. SAP Leonardo Intelligent enterprise platform 58. Monitor 59. Implementation Approach 60. Brown-Field/System Conversion 61. Landscape transformation 62. Green-Field/New implementation 63. Digital Assistants 64. IndustryRef CIATSFABI”; Paragraph 610, "The proposed Cognitive Intelligent Autonomous Transformation System Cognitive Intelligent Automation Setup Wizard/Automation analyzer wizard (28) using Artificial Intelligence, Machine Learning/Robotic Process Automation, Neural Networks, analyze the Company profile (vision (2), mission (3), objectives (4), Strategy (5), Action Plan (6)), Existing system (Enterprise Resource Planning, eCommerce, Customer Relationship Management, BW (8), Industry Solution (9), Core Business Process, Reports, Interfaces (RICEF) (10), Automation Opportunities (11), Priorities (12)), Gaps in Best Practice for Inter and Intra Industry ( 14) (Most used business process within the industry (15), Most used business process across the industries (16), Core business process (17), Automation opportunities (18), User Input Priorities (19), User Management Input Prioritization (21) (Infrastructure (22), Product portfolio choices (23), RICEF Choices (24), Automation opportunities (25), User Input Priorities (26), to produce the ideal transformed target system called Ideal_ virtual and can also be the transformed target system for the company called Virtual_company using the process VirtualBuild, as orchestrated by the Master proposed Cognitive Intelligent Autonomous Transformation System which is hosted in a datacenter.")
E) identifying data with multiple entities, and compiling them into a list of JTBD in a JTBD formulation module, and (Krishnaswamy: Paragraph 293, "CIATSFABI autonomously/semi-autonomously transforms current COTS system to the latest recommended version supported by vendor for the industry and company while protecting configuration, master, transaction, historical data and features of current system, converting data where needed. [0294] Master CIATSFABI system hosted in cloud, transforms customer's existing COTS system (e.g., SAP) to transformed target system aka Customer CIATSFABI which can be hosted in either cloud or in their premise if possible. Master CIASFAB, produces transformation of popular COTS for all major industries world-wide and CIATSFABI rolls out a to Customer's IT semi-autonomously once customer approves target version so they can prepare target landscape and use vendor supplied upgrade tools to transform (e.g., SAP SUM 2.0) along with all the data (Configuration, Master, Transaction and Historical data). [0295] Master CIATSF ABI generates checklists, roadmaps, Alerts incl. SWOT analysis reports on current customer's business system and proposed Customer's CIATSF ABI to top management, to assist top management's interest and approval to implement target system."; Paragraph 298, "CIATSFABI produces actionable Business Intelligence reports, checklists & roadmaps, against the proposed system (Customer's CIA TSP ABI) and existing system including reports on gaps in the current initiatives on existing system and potential initiatives on proposed target system.")
F) enhancing the list of JTBD using JTBD augmentation module for predicting additional JTBD based on data elements having a few of the entities or prior knowledge or additional research that is manually curated. (Krishnaswamy: Paragraph 336, “Feedback control mechanism will be provided to fine tune Master and Customer CIATSFABI, based on target version chosen along with all user inputs/choices.”; Paragraph 372, “Feedback control mechanism will be provided by various Digital Assistants identified by Master CIATSFABI to fine tune the Cognitive Intelligent Automation systems, based on change in market place, laws, technology, etc. due to external events.”; Paragraph 553, “Analyze Past, present and future Business Intelligence reports/activities setup in existing system with the proposed from Cognitive automation system. Measure the Gaps. Guidelines, recommendations using Cognitive automation system for new infrastructure, new information system, new products and services. Get feedback from customers, grade, evaluate and finalize the transformed target system including the versions. Additional benefits of the Cognitive Intelligent Automation for Alignment of corporate mission, goals and objectives with Business Intelligence needs provided by current/proposed company initiatives enhanced by the recommendations provided by Ideal_virtual (28) and virtual_company (10) of Cognitive Intelligent Autonomous Transformation System (proposed Cognitive intelligent automation solution/system), (10), identifying the gaps in their existing initiatives and approach and provide suitable reports to close the gap.”; Paragraph 559, “This user management input is fed to Prebuild analyzer (17) to finalize company specific Virtual proposed Cognitive Intelligent Autonomous Transformation System i.e., the absolute ideal transformed target system for the specific company (10).”; Paragraph 564, “Fine tune Cognitive DB repository (3) for the Master proposed Cognitive Intelligent Autonomous Transformation System (2) with company specific information incl. details on Information system. How well did the new solution help the company—SWOT (Strength, weakness, opportunity & Threats) analysis (34), recommendations for improvements to Cognitive Automation system in the next iteration. The results are once again used to update the Master proposed Cognitive Intelligent Autonomous Transformation System (2).”)
Krishnaswamy does not explicitly disclose the following, however, in analogous art of business and enterprise management, Cao discloses the following:
…and cluster the extracted data based on a prior knowledge from domain expert or subject matter experts (SMEs) or existing groups or frameworks…and computes a desired metric by using other entities including pain, barrier, hire, fire…to identify opportunity levels and said desired metric is represented in heat maps and clusters (Cao: Paragraph 49, “one can apply advanced clustering and statistical analysis techniques to the historical data on projects, in order to find common patterns in terms of their skill and job role mix, and create a standardized taxonomy for projects on the basis of their resource requirements.”; Paragraph 81, “In this end-to-end system, the gap and glut results from various components of the planning system can be analyzed using the risk tolerances, business rules, preferences or objectives of the organization, and plans for closing the gaps and gluts through resource actions can be proposed by the imbedded decision support tools. For example, given gaps and gluts, costs, transition paths and lead times, acquisition costs and times, risk tolerances, etc., recommendations are made by the tool on how to close the gaps and reduce the gluts. More information on gap and glut analysis is discussed in the second of the above-identified co-pending applications, the contents of which are incorporated herein by reference, and which method and tool could potentially be used as a subcomponent in an integrated tool of the present invention.”; Paragraph 88, “Thus, one complex problem addressed by the tool of the present invention is, given relevant costs for resources, revenues from engagements, risks of lost engagements, and engagement bills of materials, determine the optimal usage of resources (from a profit, revenue or cost perspective). A combination of advanced probabilistic methods and advanced nonlinear (but including linear as a special case) optimization techniques are used to determine the optimal usage of resources. The objective could be to maximize revenue or profit, or to minimize overall cost. Constraints contain mutual relationships between system parameters (project risks, arrival rates, skill capacities), as well as risk tolerances. Since resources of particular skills can be contracted (or handled by other sourcing strategies) and that yields different cost than in the case of pulling all resources from IBM pools, the result of optimization represents amounts of resources that should be contracted (or handled by other sourcing strategies) in order to achieve a desirable objective.”; Paragraph 121, “From these core methodologies, a workforce system manager could also easily derive new capabilities, to address specific needs and connect different user segments, such as sales, planning and delivery organizations. It is noted that the exemplary embodiments discussed hereinbelow do incorporate a number of these capabilities.”; Paragraph 124, “For the teams involved in planning, one would address issues such as: "What are the best capacity staffing levels for each skill to maximize profits", "What are the risks of losing engagements, given the current staffing levels? How does the current staffing level deviate from what was expected? What hiring, retraining, firing, etc., actions should be taken for each skill based on demand, supply, gaps/gluts, revenues from engagements, and costs for skills?"”; Paragraph 125, “There are numerous ways of how these individual capabilities could be implemented. Examples include: 1) statistical methods and predictive modeling to compute demand and supply forecasts, 2) stochastic loss network model for general risk-based workforce management under uncertainty and a stochastic optimization framework for general risk-based capacity planning under uncertainty, including the determination of optimal planning actions, 3) linear programming to assign individual resources to existing opportunities, while respecting the business rules for staffing, 4) and the service-based workforce system structure that enables flexible solution reusability, 5) data warehousing techniques to manage and integrate different data sources, etc.”)
… including pain, barrier, hire, fire…to identify the opportunities of highest interest, and… (Cao: Paragraph 49, “one can apply advanced clustering and statistical analysis techniques to the historical data on projects, in order to find common patterns in terms of their skill and job role mix, and create a standardized taxonomy for projects on the basis of their resource requirements.”; Paragraph 81, “In this end-to-end system, the gap and glut results from various components of the planning system can be analyzed using the risk tolerances, business rules, preferences or objectives of the organization, and plans for closing the gaps and gluts through resource actions can be proposed by the imbedded decision support tools. For example, given gaps and gluts, costs, transition paths and lead times, acquisition costs and times, risk tolerances, etc., recommendations are made by the tool on how to close the gaps and reduce the gluts. More information on gap and glut analysis is discussed in the second of the above-identified co-pending applications, the contents of which are incorporated herein by reference, and which method and tool could potentially be used as a subcomponent in an integrated tool of the present invention.”; Paragraph 88, “Thus, one complex problem addressed by the tool of the present invention is, given relevant costs for resources, revenues from engagements, risks of lost engagements, and engagement bills of materials, determine the optimal usage of resources (from a profit, revenue or cost perspective). A combination of advanced probabilistic methods and advanced nonlinear (but including linear as a special case) optimization techniques are used to determine the optimal usage of resources. The objective could be to maximize revenue or profit, or to minimize overall cost. Constraints contain mutual relationships between system parameters (project risks, arrival rates, skill capacities), as well as risk tolerances. Since resources of particular skills can be contracted (or handled by other sourcing strategies) and that yields different cost than in the case of pulling all resources from IBM pools, the result of optimization represents amounts of resources that should be contracted (or handled by other sourcing strategies) in order to achieve a desirable objective.”; Paragraph 121, “From these core methodologies, a workforce system manager could also easily derive new capabilities, to address specific needs and connect different user segments, such as sales, planning and delivery organizations. It is noted that the exemplary embodiments discussed hereinbelow do incorporate a number of these capabilities.”; Paragraph 124, “For the teams involved in planning, one would address issues such as: "What are the best capacity staffing levels for each skill to maximize profits", "What are the risks of losing engagements, given the current staffing levels? How does the current staffing level deviate from what was expected? What hiring, retraining, firing, etc., actions should be taken for each skill based on demand, supply, gaps/gluts, revenues from engagements, and costs for skills?"”; Paragraph 125, “There are numerous ways of how these individual capabilities could be implemented. Examples include: 1) statistical methods and predictive modeling to compute demand and supply forecasts, 2) stochastic loss network model for general risk-based workforce management under uncertainty and a stochastic optimization framework for general risk-based capacity planning under uncertainty, including the determination of optimal planning actions, 3) linear programming to assign individual resources to existing opportunities, while respecting the business rules for staffing, 4) and the service-based workforce system structure that enables flexible solution reusability, 5) data warehousing techniques to manage and integrate different data sources, etc.”)
Krishnaswamy in view of Cao do not explicitly disclose the following, however, an analogous art of enterprise analysis, Glaser discloses the following:
…including LLM based techniques (Glaser: Paragraph 96, “In another version, a first learning model may output classification results that are problematic or undesired. For example, racist or offensive classifications could be learned from a large language model. A bias assessment for a cluster of similar scenarios resulting in such undesired could have a bias assessment applied to correct for such undesired classifications. In some variations, such groups or types of classifications could configured such that these clusters could be automatically or semi-automatically identified and/or corrected. In this way an interpretation of possible outputs of the supervised learning model may be used to identify problematic clusters and resolve these issues.”)
… and a user is allowed to modify the clustering by moving individual situations or groups of situations across clusters, combining clusters, splitting clusters, or a combination of thereof … (Glaser: Paragraph 97, “Determining bias assessments may be performed through receiving human-assisted input for one or more clusters. In this variation, the unsupervised learning process can facilitate grouping data with similar patterns. A user could review all or select clusters, and apply bias assessments. In some variations, clusters showing signs of being associated with false positives, false negatives, or a mixture of results may be prioritized for review. In addition to or as an alternative to manual review, collected feedback on performance of the learning model could also be used. For example, if a user or agent flags a particular example as being wrong, then this may trigger evaluation or assignment of a bias assessment for examples in a related cluster. Human-assisted input may be facilitated through a programmatic interface to the learning processing pipeline. For example, a user could use a command line prompt or programmatic framework to update the machine learning system. Human-assisted input may alternatively be facilitated through a user interface. For example, examples from a cluster could be presented and then various actions could be taken on that cluster.”; Paragraph 105, “In some variations, the method may use a hybrid of automating determining bias assessments and receiving human-assisted input in determining bias assessments.”)
Krishnaswamy discloses a method of analyzing data and determining actionable intelligence with regards to business opportunities. Cao discloses a method for managing enterprise resources to obtain desired results. Glaser discloses a method for improving an artificial intelligence for better application for insight generation. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Krishnaswamy with the teachings of Cao in order to improve the accuracy and analysis of staffing and resources as disclosed by Cao (Cao: Paragraph 24, “2) Improved accuracy of staffing decisions and more accurate resource analysis, including uniform, standard and up-to-date views of the workforce. Workforce tools can be managed globally.”). It would have been further obvious to one of ordinary skill in the art to combine the methods of Krishnaswamy in view of Cao with the teachings of Glaser in order improve the accuracy of models that are applied to the business data as disclosed by Glaser (Glaser: Paragraph 15, “A system and method for cluster targeting for machine learning training functions to use unsupervised learning model to inspect a supervised machine learning model and optionally inject of subjective taste to guide supervised learning with diverse datasets.”)
Claim(s) 5 and 15 –
Krishnaswamy in view of Cao and Glaser disclose the limitations of claims 1 and 11 Krishnaswamy further discloses the following:
the method allows the user to select three factors namely needs, situation, and desired outcome or motivation entity to create the final JTBDs. (Krishnaswamy: Paragraph 310, "CIATSFABI transformed target system using Deep-Learning (Neural networks with dynamic number of nodes/layers, hidden layers supported by SAP Leonardo) with each iteration f by feeding in various mini-cognitive system starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desired-moderate or complex or futuristic, with Unsupervised learning, supervised learning, Reinforcement Learning, to arrive at Customer's CIATSFABI (actual) with reports, guidelines, checklists to IT team so they can perform transformation using vendor supplied tools and the assistance provided by Master CIATSFABI."; Paragraph 320, "Master CIATSFABI fine-tunes transformation scenarios after interaction with proposed Customer's CIATSFABI starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desired-moderate or complex or futuristic."; Paragraph 330, "Customer's CIATSFABI starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desiredmoderate or complex or futuristic, is the proposed system which can be Ideal_ virtual (latest version available on operating system, database from vendor) or one version lower which may be more stable and well-tested (virtual_company aka Customer's CIATSFABI)."; Paragraph 508, "Gap Analysis of actionable Business Intelligence reports, checklists & roadmaps, of Existing vs Proposed system and finalize target versions based on customer's selection.-Ideal_ virtual (latest version 28) or virtual_company (lower versions) (19)."; Paragraph 544, "Finalize virtual proposed system aka Customer's CIATSFABI (10) using Prebuild analyzer (17) based on customer's preferences/suggestions, Cognitive Automation analyzer (20) which reads company's existing configuration (25) and company specific database (26), adapter modules (5,6,7,8,9) to produce customer's CIA TSFABI as the latest version available (Ideal_ virtual 28) or one or more versions lower than the latest (Virtual_company19)")
Claim(s) 6 and 16 –
Krishnaswamy in view of Cao and Glaser disclose the limitations of claims 1, 5, 11, and 15
Krishnaswamy further discloses the following:
allows the user to select said factors in any sequence, any combination, and any permutation of entities to create the final JTBDs. (Krishnaswamy: Paragraph 310, "CIATSFABI transformed target system using Deep-Learning (Neural networks with dynamic number of nodes/layers, hidden layers supported by SAP Leonardo) with each iteration f by feeding in various mini-cognitive system starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desired-moderate or complex or futuristic, with Unsupervised learning, supervised learning, Reinforcement Leaming, to arrive at Customer's CIATSFABI (actual) with reports, guidelines, checklists to IT team so they can perform transformation using vendor supplied tools and the assistance provided by Master CIATSFABI."; Paragraph 320, "Master CIATSFABI fine-tunes transformation scenarios after interaction with proposed Customer's CIATSF ABI starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desired-moderate or complex or futuristic."; Paragraph 330, "Customer's CIATSFABI starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desired-moderate or complex or futuristic, is the proposed system which can be Ideal_ virtual (latest version available on operating system, database from vendor) or one version lower which may be more stable and well-tested (virtual_company aka Customer's CIATSFABI)."; Paragraph 508, "Gap Analysis of actionable Business Intelligence reports, checklists & roadmaps, of Existing vs Proposed system and finalize target versions based on customer's selection.-Ideal_ virtual (latest version 28) or virtual_company (lower versions) (19)."; Paragraph 544, "Finalize virtual proposed system aka Customer's CIATSFABI (10) using Prebuild analyzer (17) based on customer's preferences/suggestions, Cognitive Automation analyzer (20) which reads company's existing configuration (25) and company specific database (26), adapter modules (5,6,7,8,9) to produce customer's CIATSFABI as the latest version available (Ideal_ virtual 28) or one or more versions lower than the latest (Virtual_company19)")
Claim(s) 7 and 17 –
Krishnaswamy in view of Cao and Glaser disclose the limitations of claims 1 and 11 Krishnaswamy further discloses the following:
aggregating the entities into two parameters namely importance level and satisfaction level using a calculation based on a quantification of the plurality of entities extracted. (Krishnaswamy: Paragraph 310, "CIATSFABI transformed target system using Deep-Learning (Neural networks with dynamic number of nodes/layers, hidden layers supported by SAP Leonardo) with each iteration f by feeding in various mini-cognitive system starting with initial first-cut Basic transformation based on customer selection of Quick win and best-in-class Advanced transformation based on customer selection of complexity desiredmoderate or complex or futuristic, with Unsupervised learning, supervised learning, Reinforcement Learning, to arrive at Customer's CIATSFABI (actual) with reports, guidelines, checklists to IT team so they can perform transformation using vendor supplied tools and the assistance provided by Master CIATSF ABI. [0311] Deep Learning with workflow-based exception handling that produces enhanced learning to the model.” Paragraph 312, “Finally, Deep Learning improves Customer's CIATSFABI each time Master CIATSFABI is released every 3 months with better transformation of customer's business system."; Paragraph 313, "CIATSFABI produces actionable business intelligence and compliance reports for top management with checklists, roadmaps, Alerts incl. SWOT analysis for existing system & proposed system and also produce gap reports on existing initiatives on existing system vs. potential initiatives on target system."; Paragraph 463, "Pre-Discovery, Discovery, Analysis (10, 11, 12), phase results and SWOT analysis will be used by Master CIATSF ABI (2) to produce Customer specific Cognitive System customers CIATSFABI system (Basic (24) (Quick wins))." Paragraph 464, "Upon further refinement of platform specifics and prioritizations produce Cognitive System (Advanced (25) (moderate complexity, complex, futuristic)."; Paragraph 470, "Proposed Cognitive Intelligent Autonomous Transformation System Setup Wizard (1), with Guidance from Artificial intelligence Master DB-Master proposed Cognitive Intelligent Autonomous Transformation System (2), after connection test (6) during PreDiscovery, Discovery and Analysis Phase (10 11 12) will read from Existing information system (4) (26) will produce "identify Opportunity Matrix" which essentially identifies all solution advancement possible from current system to ultimate information system (28), Chosen Transformed target systemvirtual_company (27). The Pre-Discovery, Discovery and Analysis Phase (10 11 12) also produces "platform specifics-reports and roadmaps" (22 20) which is also used in the possible solutions in the ultimate information system (28). PreDiscovery, Discovery and Analysis Phase (10 11 12) also prioritize the solutions based on benefits with user's input and will be used in the possible solutions in the ultimate information system (28), Chosen Transformed target systemvirtual_company (27)."; Paragraph 471, "Proposed Cognitive Intelligent Autonomous Transformation System Setup Wizard (1), with Guidance from Artificial intelligence Master DB-Master proposed Cognitive intelligent automation solution/system (2), after connection test (6) during PreImplementation and Implementation Phase (13 14) will use Pre-Discovery, Discovery and Analysis Phase results (10) (11) (12) along with SWOT (Strength, weakness, opportunity & Threats) analysis of external events/system (9) will produce "Quick wins" (35) and solutions based on complexity (Moderate/Complex/Futuristic) of solutions for ultimate information system (28), Chosen Transformed target system-virtual_company (27). The Implementation Phase (8) will refine and fine tune "platform specifics-reports and roadmaps (22 20)" and also refine and fine tune prioritize the solutions based on benefits with user's input and will be used in the possible solutions in the ultimate information system 28), Chosen Transformed target system-virtual_company (27).")
Claim(s) 8 and 18 –
Krishnaswamy in view of Cao and Glaser disclose the limitations of claims 1 and 11
Krishnaswamy does not explicitly disclose the following, however, in analogous art of business and enterprise management, Cao discloses the following:
displaying the plurality of entities, their groupings and their patterns/relationships, in a visualization/user interface (UI) module. (Cao: Paragraph 54, “Scenario Analyses (106): Advanced reporting capabilities and visualization to provide visibility into the workforce decision to all stakeholders (people who do planning, delivery, sales, executives, etc.). Examples include revenue realization/trends in the solution portfolio, relationship between planned and realized revenue by sector/solution, relationship between planned and actual staffing, correlation between staffing and project quality, and various analytical capabilities to support decision making.”; Paragraph 172, “Furthermore, the module can solve a sequence of these profit maximization problems under different risk constraints, and provide visualization of the changes of the performance metrics, such as profit, cost, etc., with respect to the risk constraints. In the simple illustrative example shown in FIG. 22, one can show the performance under 0.5%, 5%, 10%, . . . , 20% and no constraints, and it can be seen that, as the risk constraints becomes tighter, the revenue grows, since less engagement loss is allowed. Meanwhile, the labor cost will increase faster, hence, resulting in the decrease of the profit. This capability to play with constraints will enable a user to have a better understanding of the geometry of the problem space he/she is working on, hence, make better decisions based upon what-if type of analysis.”; Paragraph 192, “FIG. 30 demonstrates how reporting and visualization capabilities can also be important components of the end-to-end workforce management methodology of the present invention, particularly for executive levels and for high-level planning. Examples of the reporting/visualization capabilities include: Revenue realization/trends in the solution portfolio; Relationship between planned and realized revenue by sector/solution; Relationship between planned and actual staffing; Correlation between staffing and project quality.”)
Krishnaswamy discloses a method of analyzing data and determining actionable intelligence with regards to business opportunities. Cao discloses a method for managing enterprise resources to obtain desired results. Glaser discloses a method for improving an artificial intelligence for better application for insight generation. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Krishnaswamy with the teachings of Cao in order to improve the accuracy and analysis of staffing and resources as disclosed by Cao (Cao: Paragraph 24, “2) Improved accuracy of staffing decisions and more accurate resource analysis, including uniform, standard and up-to-date views of the workforce. Workforce tools can be managed globally.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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 at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624