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 .
Claims 1-7, 10-15, 17-19, 21-24 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 10-15, 17-19, 21-24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 13 and 17 recite “automatically allocating at least a portion of the one or more prioritized resources to one or more external systems associated with at least a portion of the one or more tasks.”, however the specification fails to disclose allocation of resource to one or more external system associated with the one or more task. The specification do not disclose any external system associated with at least a portion of the one or more task and does not specifically disclose any resource allocation to the external system as claimed.
Dependent claims of that are dependent on the independent claims are rejected under the same rational of the independent claims.
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.
Claims 1-5, 7 and 12-15, 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Rath (US 2021/0014136), in view of Eisner et al. (US 2018/0063327).
As per claim 1, Rath teaches A computer-implemented method comprising:
obtaining data pertaining to multiple resources associated with at least one enterprise, wherein obtaining data pertaining to multiple resources comprises obtaining resource capability- specific
prioritizing one or more of the multiple resources in connection with one or more tasks associated with the at least one enterprise by processing, using one or more machine learning techniques, at least a portion of the data pertaining to the multiple resources and data pertaining to the one or more tasks (par. 0011, par. 0058; identifying best resource/agent for the task/ticket request); and
performing automated actions based at least in part on the prioritizing of the one or more resources, wherein performing automated action comprises: (abstract; par. 0011, lines 17-19; par. 0058; par. 0062; assigning tickets to agent based on agent score and skill):
automatically allocating at least a portion of the one or more prioritized resources to one or more external systems associated with at least a portion of the one or more tasks (abstract; par. 0014; par. 0021; support tickets tied to client system is assigned to an agent which implies allocating resource/agent to the client/task system(external systems) associated with the task/ticket); and
automatically training at least a portion of the one or more machine learning techniques based at least in part on feedback pertaining to the prioritizing of the one or more resources (par. 0080)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (par. 0103).
Rath discloses obtaining data pertaining to multiple resources comprises obtaining resource capability- specific data associated with at least a portion of the multiple resources which is skill level of the resources but does not specifically disclose the obtaining data pertaining to multiple resources comprises obtaining resource capability- specific training data associated with at least a portion of the multiple resources.
However Eisner teaches obtaining data pertaining to multiple resources comprises obtaining resource capability- specific training data associated with at least a portion of the multiple resources(par. 0095, 0097).
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the teaching of Eisner into the teaching of Rath to obtain resource capability- specific training data associated with at least a portion of the multiple resources. A person of ordinary skill of the art would have been motivated to perform the combination for being able to utilize the teaching of Eisner to identify and allocate appropriate resource for the request for being able to complete the resource efficiently and improve overall customer satisfaction.
As per claim 2, Rath teaches wherein processing at least a portion of the data pertaining to the multiple resources and data pertaining to the one or more tasks comprises processing the at least a portion of the data pertaining to the multiple resources and the data pertaining to the one or more tasks using one or more collaborative filtering techniques, wherein the one or more collaborative filtering techniques comprise at least one unsupervised machine learning-based recommendation generation technique (par. 0030; par. 0083).
As per claim 3, Rath teaches wherein using one or more collaborative filtering techniques comprises filtering information from one or more of the at least a portion of the data pertaining to the multiple resources and the data pertaining to the one or more tasks by determining one or more similarities across one or more of the at least a portion of the data pertaining to the multiple resources and the data pertaining to the one or more tasks (par. 0022; par. 0083; system uses collaborative filtering technique and also discloses performing similarity math between the task information and agent information to identify the most suitable agent which implies filtering information).
As per claim 4, Rath teaches wherein prioritizing one or more of the multiple resources in connection with the one or more tasks associated with the at least one enterprise comprises generating one or more priority scores, across each of the one or more tasks, for at least a portion of the multiple resources based at least in part on the one or more determined similarities (par. 0022; par. 0040; par. 0043; identifying similarity/match between support ticket and agent and scoring agent based on similarity to the ticket topic or information).
As per claim 5, Rath teaches wherein generating one or more priority scores, across each of the one or more tasks, for at least a portion of the multiple resources comprises implementing one or more weights associated with one or more task-related attributes (par. 0040; implementing wights to information provided with the ticket/task t).
As per claim 7, Rath teaches wherein prioritizing one or more of the multiple resources comprises processing, using the one or more machine learning techniques in conjunction with one or more factor analysis techniques, the at least a portion of the data pertaining to the multiple resources and the data pertaining to the one or more tasks (par. 0022; par. 0040; par. 0042; par. 0083; performing task/ticket and multiple agent/resource analysis using collaborative filtering process including factor analysis of the information).
As per claim 8, Rath teaches wherein performing one or more automated actions comprises automatically allocating at least a portion of the one or more prioritized resources to one or more systems associated with at least a portion of the one or more tasks (abstract; par. 0014; par. 0021; support tickets tied to client system is assigned to an agent which implies allocating resource/agent to the client/task system associated with the task/ticket).
As per claim 9, Rath teaches wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques based at least in part on feedback pertaining to the prioritizing of the one or more resources (par. 0080).
As per claim 12, Rath teaches wherein obtaining data pertaining to multiple resources comprises obtaining one or more of resource capability-related data associated with at least a portion of the multiple resources, resource training-related data associated with at least a portion of the multiple resources, resource availability data associated with at least a portion of the multiple resources, resource location data associated with at least a portion of the multiple resources, and historical resource performance data associated with at least a portion of the multiple resources (par. 0022; par. 0040; extracts information about agent skill, availability, location, time zone, workload; the cited section discloses the underlined limitations).
As per claim 13, The claim recites a program product claim that performs the method as described in claim 1. Therefore, claim 13 is rejected for the same reasons as disclosed for claim 1.
However, claim 13 includes additional limitation below:
Rath teaches a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device (par. 0103, 0107).
As per claims 14-15, it is a program product claim having similar limitations as of method claims 2 and 7 and therefore, they are rejected under the same rational as of claims 2 and 7.
As per claim 17, The claim recites an apparatus claim that performs the method as described in claim 1. Therefore, claim 17 is rejected for the same reasons as disclosed for claim 1.
However, claim 17 includes additional limitation below:
Rath teaches at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured (par. 0103; figure 5).
As per claims 18 and 19, they are apparatus claims having similar limitations as of method claims 2 and 7 and therefore, they are rejected under the same rational as of claims 2 and 7.
As per claims 21-23, they are apparatus claims having similar limitations as of method claims 3-5 and therefore, they are rejected under the same rational as of claims 3-5.
Claims 6 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Rath (US 2021/0014136), in view of Eisner et al. (US 2018/0063327) and further in view of Behera (GSO-CRS: grid search optimization for collaborative recommendation system).
As per claim 6, Rath teaches wherein using one or more collaborative filtering techniques using multiple collaborative filtering techniques (par. 0083).
Rath and Eisner do not specifically disclose using multiple collaborative filtering techniques comprising one or more user-based collaborative filters and item-based collaborative filters, in conjunction with at least one grid search-based parameter optimization process.
However Behera teaches multiple collaborative filtering techniques comprising one or more user-based collaborative filters and item-based collaborative filters, in conjunction with at least one grid search-based parameter optimization process (abstract, page 1, Left column, “Introduction”, page 4, right col. “5. Proposed Method”, paragraph 1-3, page 5, figure 3)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the teaching of Behera into the combined teaching of Rath anc Aisner to have collaborative filtering in conjunction with grid search. A person of ordinary skill of the art would have been motivated to perform the combination for being able to utilize the teaching of Behera to improve recommendation performance of the collaborative filtering technique to improve customer experience and generate more accurate recommendation (Behera: abstract).
As per claim 24, it is an apparatus claims having similar limitations as of method claim 6 above therefore, it is rejected under the same rational as of claim 6 above.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Rath (US 2021/0014136), in view of Eisner et al. (US 2018/0063327) and further in view of Merg et al. (US PGPUB 2018/0365621).
As per claim 10, Rath and Eisner do not specifically disclose wherein performing one or more automated actions comprises initiating one or more resource training schedules for at least a portion of the multiple resources based at least in part on the prioritizing of the one or more resources.
However, Merg teaches performing one or more automated actions comprises initiating one or more resource training schedules for at least a portion of the multiple resources based at least in part on the prioritizing of the one or more resources (par. 0029; par. 0097; par. 108; par. 149; assigning task including scheduling training for the resource/support agent).
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the teaching of Merg into the combined teaching of Rath and Eisner to have automated action including initiating resource training based on prioritization. A person of ordinary skill of the art would have been motivated to perform the combination for being able to utilize the teaching of Merg to improve overall profitability of the enterprise along with improving skill of the employee and improve overall employee utilization (Merg: par.0030, par. 149).
As per claim 11, Merg teaches wherein initiating one or more resource training schedules comprises implementing one or more association rule mining techniques in connection with historical data pertaining to one or more tasks handled by the at least a portion of the multiple resources, training already completed by the at least a portion of the multiple resources, and one or more future tasks expected to need at least a portion of the multiple resources (par. 0024, employee performance metrics of completion job which implies historical data; par. 0028-0029; par. 0034; par. 149; discover profitability of assigning task to lower scoring technician based on historical job completion and allocating job with training for completion; Applicant’s specification page 8 discloses rule mining as discovering information or association based on historical data collection, accordingly the cited reference teaches the limitation).
Response to Arguments
Applicant's arguments filed 12/05/2025 along with claims amendment have been fully considered. Claims rejection under 101 has been withdrawn in view of the claims amendment and presented argument. Argument regarding 103 is moot in view of the claims amendment which necessitated new ground of rejection.
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 ABDULLAH AL KAWSAR whose telephone number is (571)270-3169. The examiner can normally be reached M-F 7:30am-4:30pm.
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/ABDULLAH AL KAWSAR/ Supervisory Patent Examiner, Art Unit 2127