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 § 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) 1-8, 10-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaber US 2020/0387584 in view of Borthakur US 2018/0321928
As per claim 1. A system comprising: at least one hardware processor; and a memory storing instructions that cause the at least one hardware processor to perform operations comprising:
Gaber teaches analyzing a set of authentication logs of users of an application; [0004][0017][0020] (monitoring KPI including login and logout times)
Gaber teaches generating a baseline of activity for the application based at least in part on the analyzing; [0004][0017][0020] [0038][0039] (clustering user activity based on KPI)
Gaber teaches training, using the baseline of activity, a machine learning model for each user of the application; [0004][0017][0020] [0038][0039] (using machine learning for users and PKI)
Gaber teaches generating, using the trained machine learning model, a probability of usage for a set of users of the application over a particular period of time; [0041] (teaches optimizing license allocation based on machine learned model)
Gaber fails to explicitly teach license revocation.
Borthakur teaches generating, using the trained machine learning model, a probability of usage and triggering a license revocation process based at least in part on the probability of usage, the license revocation process revoking a set of licenses for the application; [0027][0069][0070][0071][0081] (teaches usage pattern learning and future prediction and dynamically allocating or revoking licenses based on said patterns and predictions)
Borthakur teaches and allocating the set of licenses to a new set of users for using the application. [0027][0070] (teaches usage pattern learning and future prediction and dynamically allocating licenses to users)
It would have been obvious to one of ordinary skill in the art at the effective priority date of the current application to use the teaching of Borthakur with Gaber because it improves licensing optimization.
As per claim 2. The system of claim 1, Borthakur teaches wherein generating, using the trained machine learning model, the probability of usage for the application over the particular period of time comprises: analyzing, by the machine learning model, a recency and a frequency of activity of the application; converting the recency and the frequency of activity to a set of activity patterns; and providing, using the set of activity patterns, a prediction indicating the probability of usage over the particular period of time. [0071]-[0073][0081][0114] (machine learning pattern of activity to predict future usage)
As per claim 3. The system of claim 1, Borthakur teaches wherein triggering the license revocation process based at least in part on the probability of usage comprises: determining a set of probabilities of usage for a particular set of users of the application; and determining that the probabilities of usage of a second set of users is below a threshold value. [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics)
As per claim 4. The system of claim 3, Borthakur teaches wherein the operations further comprise: revoking a particular set of licenses of the application associated with the second set of users. [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics)
As per claim 5. The system of claim 4, Borthakur teaches wherein the particular set of licenses increases a particular number of available licenses of the application for provisioning to the new set of users.[0027] [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics, procuring new license based on expected demand)
As per claim 6. The system of claim 1, Gaber teaches wherein the operations further comprise: determining a particular number of valid revocations; and modifying an application popularity matrix based at least in part on the particular number of valid revocations. [0034][0035] (teaches license optimization and allocation queue including reinforcement learning/machine learning feedback of a user for valid/invalid decisions.)
As per claim 7. The system of claim 1, Gaber teaches wherein the operations further comprise: determining a particular number of invalid revocations; and modifying an application popularity matrix based at least in part on the particular number of invalid revocations. [0034][0035] (teaches license optimization and allocation queue including reinforcement learning/machine learning feedback of a user for valid/invalid decisions.)
As per claim 8. The system of claim 7, Gaber teaches wherein the application popularity matrix comprises information related to a number of access requests for the application in which a license is provided. [0020]-[0022] (teaches metrics of use of the application)
As per claim 10. The system of claim 1, Borthakur teaches wherein the operations further comprise: sending a notification that the set of licenses for the application have been revoked. [0124] (notification screen of reclamation)
As per claim 11. Gaber teaches A method comprising: analyzing a set of authentication logs of users of an application; generating a baseline of activity for the application based at least in part on the analyzing; [0004][0017][0020] (monitoring KPI including login and logout times)
Gaber teaches training, using the baseline of activity, a machine learning model for each user of the application; generating, using the trained machine learning model, a probability of usage for a set of users of the application over a particular period of time; [0004][0017][0020] [0038][0039] (using machine learning for users and clustering according to PKI)
Gaber fails to explicitly teach license revocation.
Borthakur teaches generating, using the trained machine learning model, a probability of usage and triggering a license revocation process based at least in part on the probability of usage, the license revocation process revoking a set of licenses for the application; [0027][0069][0070][0071][0081] (teaches usage pattern learning and future prediction and dynamically allocating or revoking licenses based on said patterns and predictions)
Borthakur teaches and allocating the set of licenses to a new set of users for using the application. [0027][0070] (teaches usage pattern learning and future prediction and dynamically allocating licenses to users)
It would have been obvious to one of ordinary skill in the art at the effective priority date of the current application to use the teaching of Borthakur with Gaber because it improves licensing optimization.
As per claim 12. The method of claim 11, Borthakur teaches wherein generating, using the trained machine learning model, the probability of usage for the application over the particular period of time comprises: analyzing, by the machine learning model, a recency and a frequency of activity of the application; converting the recency and the frequency of activity to a set of activity patterns; and providing, using the set of activity patterns, a prediction indicating the probability of usage over the particular period of time. [0071]-[0073][0081][0114] (machine learning pattern of activity to predict future usage)
As per claim 13. The method of claim 11, Borthakur teaches wherein triggering the license revocation process based at least in part on the probability of usage comprises: determining a set of probabilities of usage for a particular set of users of the application; and determining that the probabilities of usage of a second set of users is below a threshold value. [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics)
As per claim 14. The method of claim 13, Borthakur teaches further comprising: revoking a particular set of licenses of the application associated with the second set of users. [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics)
As per claim 15. The method of claim 14, Borthakur teaches wherein the particular set of licenses increases a particular number of available licenses of the application for provisioning to the new set of users. [0027] [0069][0092] (reclaiming licenses for certain customers for other customers based on predictions and metrics, procuring new license based on expected demand)
As per claim 16. The method of claim 11, Gaber teaches further comprising: determining a particular number of valid revocations; and modifying an application popularity matrix based at least in part on the particular number of valid revocations. [0034][0035] (teaches license optimization and allocation queue including reinforcement learning/machine learning feedback of a user for valid/invalid decisions.)
As per claim 17. The method of claim 11, Gaber teaches further comprising: determining a particular number of invalid revocations; and modifying an application popularity matrix based at least in part on the particular number of invalid revocations. [0034][0035] (teaches license optimization and allocation queue including reinforcement learning/machine learning feedback of a user for valid/invalid decisions.)
As per claim 18. The method of claim 17, Gaber teaches wherein the application popularity matrix comprises information related to a number of access requests for the application in which a license is provided. [0020]-[0022] (teaches metrics of use of the application)
As per claim 20. Gaber teaches A non-transitory computer-storage medium comprising instructions that, when executed by one or more processors of a machine, configure the machine to perform operations comprising: analyzing a set of authentication logs of users of an application; [0004][0017][0020] (monitoring KPI including login and logout times)
Gaber teaches generating a baseline of activity for the application based at least in part on the analyzing; training, using the baseline of activity, a machine learning model for each user of the application; generating, using the trained machine learning model, a probability of usage for a set of users of the application over a particular period of time; 0004][0017][0020] [0038][0039] (using machine learning for users and clustering according to PKI)
Gaber fails to explicitly teach license revocation.
Borthakur teaches generating, using the trained machine learning model, a probability of usage for a set of users of the application over a particular period of time; triggering a license revocation process based at least in part on the probability of usage, the license revocation process revoking a set of licenses for the application; [0027][0069][0070][0071][0081] (teaches usage pattern learning and future prediction and dynamically allocating or revoking licenses based on said patterns and predictions)
Borthakur teaches and allocating the set of licenses to a new set of users for using the application. . [0027][0070] (teaches usage pattern learning and future prediction and dynamically allocating licenses to users)
It would have been obvious to one of ordinary skill in the art at the effective priority date of the current application to use the teaching of Borthakur with Gaber because it improves licensing optimization.
Claim(s) 9, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaber US 2020/0387584 in view of Borthakur US 2018/0321928 in view of Dodgson US 2014/0020107
As per claim 9. The system of claim 1, Dodgson teaches wherein allocating the set of licenses to the new set of users occurs during a pre-hire stage or a first day of the new set of users. [0004][0007] (teaches dynamic license allocation including to new hires)
It would have been obvious to one of ordinary skill in the art at the effective filing date of the current application to use the teaching of Dodgson with the prior art because it expedites enterprise processes.
As per claim 19. The method of claim 11, wherein allocating the set of licenses to the new set of users occurs during a pre-hire stage or a first day of the new set of users. [0004][0007] (teaches dynamic license allocation including to new hires)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BROWN whose telephone number is (571)272-3833. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Luu Pham can be reached at (571) 270-5002. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER J BROWN/Primary Examiner, Art Unit 2439
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