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 .
DETAILED ACTION
This Office Action corresponds to application 18/678,429 which was filed on 5/30/2024.
Response to Amendment
In the reply filed 2/6/2026, claims 1, 15, and 18 have been amended. Claims 2-3 and 7 have been cancelled and claims 21-23 have been added. Accordingly, claims 1, 4-6, and 8-23 are currently pending.
The 35 USC 101 rejections of claims 1-20 are withdrawn in light of the amendments and arguments
The 35 USC 112 rejections of claim 2-3 have been withdrawn in light of the amendments.
Response to Arguments
Applicant’s arguments filed 2/6/2026 have been fully considered but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1, 4, 6, 8, and 10-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swanson et al. (US2020/0034774, previously presented in ‘892), hereinafter Swanson, in view of Begel et al. (US2010/0211924, previously presented in ‘892), hereinafter Begel, and Draper et al. (US2004/0002870), hereinafter Draper.
Regarding Claim 1:
Swanson teaches:
An apparatus comprising: at least one processing device comprising a processor coupled to a memory (Swanson, figure 10, [0124], note processor and memory; the at least one processing device being configured:
to generate a first data structure between a first and one or more additional applications developed by a given entity (Swanson, figures 1-2, 6B, [0034, 0051, 0059-0062, 0093], note data analysis tools to build relationships of data to be used to develop the training plans; note this includes mapping a training hierarchy to Knowledge, skills, and tasks (KSTs) associated with a role, e.g., team member for application being developed);
to generate a second data structure characterizing mappings between the first and one or more additional applications and a training hierarchy comprising a plurality of trainings (Swanson, figures 1-2, 6B, [0034, 0051, 0059-0062, 0093], note data analysis tools to build relationships of data to be used to develop the training plans; note this includes mapping a training hierarchy to Knowledge, skills, and tasks (KSTs) associated with a role, e.g., team member for application being developed), the training hierarchy comprising multiple levels, the multiple levels including at least a first level associated with independently deployable ones of the first and one or more additional applications providing respective products offered by the given entity, a second level associated with first logical groupings of two or more of the independently deployable ones of the first and one or more additional applications providing a product line of related products offered by the given entity, (Swanson, [0043, 0059-0064], note using a precise model of the connections between curriculum elements; note training components comprise a first level of technology domains/products offered by the given entity, e.g., competencies, a second level of groups of applications within each of the domains/product line of related products, e.g. KST and role information and experience information; note the roles identify the relevant KSTs as potential training goals);
to identify a given user that is part of a group of two or more users responsible for development of the first application, the given user being associated with a given user role within the group of two or more users (Swanson, figures 1-2, [0035-0037, 0062, 0093], note the user’s role and profile are identified; note the roles may be as a team member or supervisor, which are interpreted as being a group of two or more users; note mapping a training hierarchy to KSTs mapped to a role, e.g., team member for application being developed);
to generate, utilizing one or more machine learning models that take as input the given user role of the given user and at least portions of the first data structure and the second data structure, a training plan for the given user, the training plan specifying a subset of the plurality of trainings to be completed by the given user (Swanson, figures 1 and 2, [0034-0040, 0042, 0051, 0059, 0067, 0134], note using machine learning models to develop training recommendations based on training requirements from the relationship data; note the relationships include training requirements and desired outcomes, which is interpreted as training requirements linked to application development), the subset of the plurality of trainings comprising (i) a first set of one or more trainings from the first, second and third levels of the training hierarchy which are mapped to the first application (Swanson, [0034-0037, 0043, 0059-0064, 0134], note using a precise model of the connections between curriculum elements; note training components comprise competencies, KST and role information and experience information, and role and task information which are mapped to training to generate the training plan which means the selected training, e.g., training sets, are mapped to the different levels in the hierarchy according to the first and one or more additional applications having interdependencies; note selecting the training from the highest level over lower level which means the second set of training would be less than the first);
to track a progress of the given user for different ones of the subset of the plurality of trainings included in the generated training plan (Swanson, figure 6B, [0011, 0067, 0091, 0134], note tracking user progress and updating user profile based on training); and
to dynamically update a user profile associated with the given user based at least in part on the tracked progress of the given user (Swanson, figure 6B, [0011, 0067, 0091, 0134], note tracking user progress and updating user profile based on training).
While Swanson teaches determining and tracking a user’s training plan, Swanson doesn’t specifically teach characterizing interdependencies between a first and one or more additional applications developed by a given entity. However, Begel is in the same field of endeavor, information management and relationships, and Begel teaches:
to generate a first data structure characterizing interdependencies between a first and one or more additional applications developed by a given entity (Begel, figure 4, [0050-0051], note generating a bridging data structure representing relationships between software development items, e.g., first and one or more applications);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Begel because all references are directed towards information management and relationships and because Begel would expand upon the teachings of the previously cited references in data relationships which would improve the systems awareness by improving the transparency of dependencies between items (Begel, [0306]).
While Swanson as modified teaches determining and tracking a user’s training plan, Swanson as modified doesn’t specifically teach training levels with a third level associated with second logical groupings of two or more of the first logical groupings associated with two or more related product lines of related products offered by the given entity; and a second set of one or more trainings from at least one of the second and third levels of the training hierarchy which are mapped to at least one of a subset of the first logical groupings that include the first application and a subset of the second logical groupings that include one or more of the subset of the first logical groupings. However, Draper is in the same field of endeavor, information management and relationships, and Draper teaches:
the training hierarchy comprising multiple levels, the multiple levels including at least a first level associated with independently deployable ones of the first and one or more additional applications providing respective products offered by the given entity, a second level associated with first logical groupings of two or more of the independently deployable ones of the first and one or more additional applications providing a product line of related products offered by the given entity, and a third level associated with second logical groupings of two or more of the first logical groupings associated with two or more related product lines of related products offered by the given entity (Draper, figure 2b, [0053], note learning blueprint may consist of various levels/categories for different tracts; note the blueprint may comprise tracts mapping trainings to product groups, product lines, product segments, departments, etc.; note product segment tracts include levels for products that are grouped into product lines. Product segments, e.g., third level, are interpreted as training for groups product lines, e.g., second level, which are interpreted as training for groups of products/applications, e.g., first level; note products may be applications);
to generate a training plan for the given user, the training plan specifying a subset of the plurality of trainings to be completed by the given user, the subset of the plurality of trainings comprising (i) a first set of one or more trainings from the first, second and third levels of the training hierarchy which are mapped to the first application and (ii) a second set of one or more trainings from at least one of the second and third levels of the training hierarchy which are mapped to at least one of a subset of the first logical groupings that include the first application and a subset of the second logical groupings that include one or more of the subset of the first logical groupings (Draper, figure 2b, [0053], note learning blueprint may consist of various levels/categories for different tracts; note the blueprint may comprise tracts mapping trainings to product groups, product lines, product segments, departments, etc.; note product segment tracts include levels for products that are grouped into product lines. Product segments, e.g., third level, are interpreted as training for groups product lines, e.g., second level, which are interpreted as training for groups of products/applications, e.g., first level; note products may be applications; note tracts may comprise a subset of the plurality of trainings comprising trainings from the first, second, and third levels; note a second tract may also comprise trainings from second and third levels).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Draper because all references are directed towards information management and relationships and because Draper would expand upon the teachings of the previously cited references in data relationships which would improve the systems awareness by improving the accuracy and efficiency of the training plans by utilizing data structured mapping ensuring users received only the necessary training (Draper, [0003-0004, 0010, 0015-0016]).
Regarding Claim 4:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein generating the training plan for the given user is further based at least in part on one or more initiatives of the given entity (Swanson, [0042-0043, 0059], note the training plan is generating based on the mission, e.g., initiatives, of the entity).
Regarding Claim 6:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein generating the training plan for the given user is further based at least in part on one or more training mandates associated with at least one of the given entity and the given user role (Swanson, [0009, 0034-0037, 0042-0043, 0059], note the training plan is generating based on training requirements for the role and entity).
Regarding Claim 8:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein the at least one processing device is further configured to determine one or more skill gaps of the given user based at least in part on monitoring incident data associated with the first application and the given user, wherein generating the training plan for the given user is further based at least in part on the determined one or more skill gaps of the given user (Swanson, [0009, 0036-0039, 0043, 0059, 0093], note identifying shortfalls, e.g. skill gaps, for the user based on monitoring the user’s information, e.g., experiences and roles , and assigning training based on the shortfall).
Regarding Claim 10:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein the at least one processing device is further configured to determine a balance of two or more different types of skills for a plurality of users associated with the given entity, wherein generating the training plan for the given user is further based at least in part on the determined balance of the two or more different types of skills for the plurality of users associated with the given entity (Swanson, figures 1-2, [0009, 0039, 0059-0062, 0093], note determining the different between the users current state and required state, e.g. balance of two or more skills required, to determine the training plan; note determining current skills and skills required to determine training).
Regarding Claim 11:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein dynamically updating the user profile of the given user is further based at least in part on user feedback of one or more additional users associated with the given entity responsible for managing the given user (Swanson, [0041, 0061-0064], note the instructors, e.g., responsible for managing the given user, provides input which helps determine the training selections and updates the user’s profile).
Regarding Claim 12:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein dynamically updating the user profile of the given user is further based at least in part on monitoring incident data associated with the first application and the given user subsequent to completion of different ones of the subset of the plurality of trainings included in the generated training plan by the given user (Swanson, [0009, 0036-0039, 0043, 0059-0062, 0093,], note continually comparing the trainee’s current state against the desired state to determine updated training plan; note determining current skills and skills required to determine training).
Regarding Claim 13:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein dynamically updating the user profile of the given user is further based at least in part on tracking a number of defects associated with the first application (Swanson, [0009, 0036-0039, 0043, 0059-0062, 0093,], note dynamically updating the user’s profile based on current experiences and skills) (Begel, [0060, 0063], note tracked items may be software bugs, e.g., defects).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Begel because all references are directed towards information management and relationships and because Begel would expand upon the teachings of the previously cited references in data relationships which would improve the systems awareness by improving the transparency of dependencies between items (Begel, [0306]).
Regarding Claim 14:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein dynamically updating the user profile of the given user is further based at least in part on tracking a delivery time for code updates to the first application authored by the given user (Swanson, [0009, 0036-0039, 0043, 0059-0062, 0093,], note dynamically updating the user’s profile based on current experiences and skills) (Begel, figures 15A-16B, [0130-0141], note the code update time by the developer was tracked).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Begel because all references are directed towards information management and relationships and because Begel would expand upon the teachings of the previously cited references in data relationships which would improve the systems awareness by improving the transparency of dependencies between items (Begel, [0306]).
Claim 15 discloses substantially the same limitations as claim 1 respectively, except claim 15 is directed to a non-transitory processor-readable storage medium while claim 1 is directed to an apparatus. Therefore claim 15 is rejected under the same rationale set forth for claim 1.
Claim 16 discloses substantially the same limitations as claim 8 respectively, except claim 16 is directed to a non-transitory processor-readable storage medium while claim 8 is directed to an apparatus. Therefore claim 16 is rejected under the same rationale set forth for claim 8.
Claim 17 discloses substantially the same limitations as claim 10 respectively, except claim 17 is directed to a non-transitory processor-readable storage medium while claim 10 is directed to an apparatus. Therefore claim 17 is rejected under the same rationale set forth for claim 10.
Claim 18 discloses substantially the same limitations as claim 1 respectively, except claim 18 is directed to a method while claim 1 is directed to an apparatus. Therefore claim 18 is rejected under the same rationale set forth for claim 1.
Claim 19 discloses substantially the same limitations as claim 8 respectively, except claim 19 is directed to a method while claim 8 is directed to an apparatus. Therefore claim 19 is rejected under the same rationale set forth for claim 8.
Claim 20 discloses substantially the same limitations as claim 10 respectively, except claim 20 is directed to a method while claim 10 is directed to an apparatus. Therefore claim 20 is rejected under the same rationale set forth for claim 10.
Regarding Claim 21:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein the second logical groupings of the third level are associated with different technology domains, and the first logical groupings of the second level are associated with groups of applications within each of the different technology domains (Swanson, [0043, 0059-0064], note using a precise model of the connections between curriculum elements; note training components comprise technology domains, e.g., competencies, groups of applications within each of the domains, e.g. KST and role information and experience information, the first and one or more additional applications within each of the group of applications, e.g., role and task information. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight) (Draper, figure 2b, [0052-0053], note learning blueprint may consist of various levels/categories for different tracts; note the blueprint may comprise tracts mapping trainings to product groups, product lines, product segments, departments, etc. Product segments, e.g., third level, are interpreted as training for groups product lines, e.g., second level, which are interpreted as training for groups of products/applications, e.g., first level; note products may be applications; note that the third level groupings may be associated with different technology domains, e.g., product segments, and the second level groupings may comprise applications within the technology domains, e.g., product lines).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Draper because all references are directed towards information management and relationships and because Draper would expand upon the teachings of the previously cited references in data relationships which would improve the systems awareness by improving the accuracy and efficiency of the training plans by utilizing data structured mapping ensuring users received only the necessary training (Draper, [0003-0004, 0010, 0015-0016]).
Claim 22 discloses substantially the same limitations as claim 21 respectively, except claim 22 is directed to a computer program product while claim 21 is directed to an apparatus. Therefore claim 22 is rejected under the same rationale set forth for claim 21.
Claim 23 discloses substantially the same limitations as claim 21 respectively, except claim 23 is directed to a method while claim 21 is directed to an apparatus. Therefore claim 23 is rejected under the same rationale set forth for claim 21.
Claim Rejections - 35 USC § 103
Claim(s) 5 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swanson in view of Begel, Draper, and Grillo et al. (US2025/0063140), hereinafter Grillo.
Regarding Claim 5:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein the one or more initiatives of the given entity are determined based at least in part utilizing a large language model that takes as input a textual description of the one or more initiatives of the given entity and the plurality of trainings (Swanson, [0042-0043, 0059], note the training plan is generating based on the mission, e.g., initiatives, of the entity; note the machine learning model is adjusted based on the missions).
While Swanson as modified teaches generating training based on the missions, Swanson as modified doesn’t specifically teach it as a textual input. However, Grillo is in the same field of endeavor, information management, and Grillo teaches:
wherein the one or more initiatives of the given entity are determined based at least in part utilizing a large language model that takes as input a textual description of the one or more initiatives of the given entity and the plurality of trainings (Grillo, [0064, 0131], note providing related subject matter, e.g., training information, and textual input from a user into a large language model indicating an objective, e.g., initiative, determine related portions in the related subject matter. When combined with the previously cited references this would be for the missions and training plan as taught by Swanson).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Grillo because all references are directed towards information management and relationships and because Grillo would expand upon the teachings of the previously cited references in data relationships which would improve the systems efficiency by improving accuracy in identifying relevant content (Grillo, [0033]).
Regarding Claim 9:
Swanson as modified shows the apparatus as disclosed above;
Swanson as modified further teaches:
wherein monitoring the incident data associated with the first application and the given user is based at least in part on utilizing a natural language processing machine learning model to determine a mapping between textual descriptions of the incident data and one or more of the plurality of trainings (Swanson, [0009, 0036-0039, 0043, 0059, 0067], note identifying shortfalls, e.g. skill gaps, for the user based on monitoring the user’s information, e.g., experiences and roles , and assigning training based on the shortfall).
While Swanson as modified teaches monitoring the user to determine training, Swanson as modified doesn’t specifically teach utilizing a natural language processing machine. However, Grillo is in the same field of endeavor, information management, and Grillo teaches:
wherein monitoring the incident data associated with the first application and the given user is based at least in part on utilizing a natural language processing machine learning model to determine a mapping between textual descriptions of the incident data and one or more of the plurality of trainings (Grillo, [0064, 0131], note providing related subject matter, e.g., training information, and textual input from a user into a large language model, e.g., natural language processing machine, indicating an objective and determine related portions in the related subject matter. When combined with the previously cited references this would be for the incident data and training plan as taught by Swanson).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Grillo because all references are directed towards information management and relationships and because Grillo would expand upon the teachings of the previously cited references in data relationships which would improve the systems efficiency by improving accuracy in identifying relevant content (Grillo, [0033]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tiwari et al. (US2024/0330834) teaches using machine learning and technology domains to determine training plans.
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 JOHN J MORRIS whose telephone number is (571)272-3314. The examiner can normally be reached M-F 6:00-2:00 PM EST.
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, James Trujillo can be reached at 571-272-3677. 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.
/JOHN J MORRIS/Examiner, Art Unit 2151 5/26/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
Prosecution Insights