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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 7-8, 10-12, 16-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhan et al. (US11610679).
Zhan discloses:
1. A computer-implemented method comprising:
obtaining data pertaining to at least one resource-related activity involving at least one resource and one or more users; (col 1, ln 55-56)
predicting one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; (col 2, ln 30-35)
predicting one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and (col 2, ln 30-35)
performing one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons; (col 7, ln 60-65)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. (col 3, ln 6)
2. The computer-implemented method of claim 1, wherein predicting one or more failures associated with the at least one resource-related activity comprises processing at least a portion of the obtained data using at least one multi-output neural network model. (fig 4: multi-task learning)
3. The computer-implemented method of claim 2, wherein predicting one or more reasons attributed to at least one of the one or more predicted failures comprises processing the at least a portion of the obtained data using the at least one multi-output neural network model. (fig 4: multi-task learning; col 19, 20-30)
7. The computer-implemented method of claim 1, wherein the at least one resource- related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more course correction activities directed at avoid the at least a portion of the one or more predicted failures and related to the at least a portion of the one or more predicted reasons. (col 7, ln 60-65)
8. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the at least a portion of the one or more predicted failures and the at least a portion of the one or more predicted reasons. (col 17, ln 40-50)
Claim(s) 10-12 is/are rejected as being the medium implemented by the method of claim(s) 1-3, and is/are rejected on the same grounds.
Claim(s) 16-18 is/are rejected as being the apparatus implemented by the method of claim(s) 1-3, and is/are rejected on the same grounds.
Allowable Subject Matter
Claim(s) 4-6, 9, 13-15, 19-20 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/KATHERINE LIN/Primary Examiner, Art Unit 2113