DETAILED ACTION
This office action is in response to application filed on 10/20/2023.
Claims 1 – 20 are pending.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 – 5, 12, 13 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 2, 4, 12 and 19, the claimed limitation “and/or” is considered ambiguous because it can create uncertainty about the scope of the claim. When The scope of the claim limitation presented in claims 2, 4, 12 and 19 is unclear, a rejection under 35 USC 112(b) is proper. Examiner note that the office action has treated the claimed term as “or” in the art rejection.
any claim not specifically mentioned above, is rejected due to its dependency on the rejected claim.
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, 11 – 13 and 15 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frato et al (US 20230336557, hereinafter Frato), in view of Covell et al (US 20230297416, hereinafter Covell).
As per claim 1, Frato discloses: A method, comprising:
receiving, by a processor, a request from a user to access a cloud computing system; (Frato figure 2 and [0020]: “At operation 202, the resource management device 104 receives a resource request 120 for a user. In one embodiment, the resource request 120 identifies a user.”)
in response to the request, associating, the user with a user profile, (Frato figure 2 and [0021]: “the resource management device 104 first identifies a user profile 118 that is associated with the user. For example, the resource management device 104 may use the user identifier from the resource request 120 as a search token to identify a corresponding user profile 118 from memory 112 that is associated with the user.”)
and allocating system resources to the user based on the user profile. (Frato figure 2 and [0028]: “At operation 214, the resource management device 104 associates the second resource with the user. After obtaining the first resource, the resource management device 104 associates the second resource with the user for a predetermined period of time. The predetermined period of time may be an hour, a day, a month, six months, a year, fours years, or any other suitable amount of time. For example, the resource management device 104 may associate the second resource with the user by creating an entry for the association in user profile 118 and/or the resource allocation information 114. The resource management device 104 may also specify any other terms or conditions that are associated with returning the second resource and/or retrieving the first resource in the user profile 118 and/or the resource allocation information 114.”)
Frato did not explicitly disclose:
Wherein the user profile is associated using a learning model, wherein the learning model uses machine learning to characterize attributes of the user and uses the attributes of the user to determine the user profile of a plurality of user profiles to associate with the user, each of the plurality of user profiles associated with a set of attributes and a set of system resources;
However, Covell teaches:
Wherein the user profile is associated using a learning model, wherein the learning model uses machine learning to characterize attributes of the user and uses the attributes of the user to determine the user profile of a plurality of user profiles to associate with the user, each of the plurality of user profiles associated with a set of attributes and a set of system resources; (Covell [0033]: “the UXaaS model may be trained to automatically detect the various examples of user profile data provided in exemplary table 400 by for example, receiving different samples of user profile data as training data which may be associated with a knowledge corpus… the UXaaS model may use the machine-learning algorithms to identify user profile data such as a user's name 402, the type of user or user role 404, user location data 406, preferred datacenter 408 (further including a user's preferred datacenter for performing specific computer tasks/activities), typical computer activities performed 410, language preferences 412, typical time of peak computing activity 414, minimum computing requirements per computing activity based on a service level agreement (SLA) 416 latency requirements for certain computing tasks 418, response percentage required for computing tasks/activities 420, and scheduled computing tasks/activities 422.”; [0034]: “In turn, the UXaaS program 108A, 108B may use the identified and correlated user profile data to establish user profiles for each of the different types of users and may further correlate the user profile data with the datacenters and/or geographical regions that are used or may be used by the user.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Covell into that of Frato in order to have the user profile is associated using a learning model, wherein the learning model uses machine learning to characterize attributes of the user and uses the attributes of the user to determine the user profile of a plurality of user profiles to associate with the user, each of the plurality of user profiles associated with a set of attributes and a set of system resources. Frato [0018] teaches a “resource management device 104 may first collect information about a user and the resources that they currently own or are assigned. For example, the resource management device 104 may populate a user profile 118 with contact information for a user, demographic information for a user, account information for a user, one or more resource identifiers, information associated with each resource, and/or any other suitable type of information that is associated with a user and their resources. After the user profile 118 is populated, the resource management device 104 stores and maintains the information in the user profile 118 so that it can be accessed at a later time when a user requests access to a new resource.” One of ordinary skill in the art can easily see that the resource management device 104 of Frato may easily be implementing a ML system to create and match user profile in order to achieve the predictable results of having a trained model accurately predict and allocate resources in response to user demands and is therefore rejected under 35 USC 103.
As per claim 2, the combination of Frato and Covell further teach:
The method of claim 1, wherein the set of attributes comprises usage data associated with a resource, a type of resources used, a length of time the resources are used, and/or a time of day the resources are used. (Covell [0033]: “the UXaaS program 108A, 108B may use the UXaaS model to determine when the user performs specific computing tasks/activities such as identifying whether a specific type of user typically performs certain computing tasks/activities, and/or load-intensive activities, during the daytime or a at night as well as may determine the specific time the specific type of user performs such activities (such as 3:00 pm).”)
As per claim 3, the combination of Frato and Covell further teach:
The method of claim 2, wherein the learning model is configured to determine a usage data range associated with each of the set of attributes for each user profile. (Covell [0033])
As per claim 4, the combination of Frato and Covell further teach:
The method of claim 3, wherein the usage data range for each of the plurality of user profiles comprises a threshold minimum and/or a threshold maximum. (Covell [0033]: “minimum computing requirements per computing activity based on a service level agreement (SLA)”)
As per claim 6, the combination of Frato and Covell further teach:
The method of claim 1, further comprising gathering, by the processor, data during use of the cloud computing system by the user and updating the learning model based on the data. (Covell [0032])
As per claim 7, the combination of Frato and Covell further teach:
The method of claim 1, further comprising, during a training phase: gathering data during use of the cloud computing system from a plurality of users; and using the data to create and update the plurality of user profiles, each user profile comprising a plurality of attributes, wherein one or more of the plurality of attributes each comprise a usage data range for the attribute. (Frato [0018] – [0019])
As per claim 8, the combination of Frato and Covell further teach:
The method of claim 1, wherein the system resources comprise utilization of at least one of a CPU, a GPU, an accelerator, an FPGA, ROM storage, RAM storage, and an internet connection speed. (Frato [0034])
As per claim 11, it is the apparatus variant of claim 1 and is therefore rejected under the same rationale. (Frato figure 3: processor and memory.)
As per claim 12, it is the apparatus variant of claim 2 and is therefore rejected under the same rationale.
As per claim 13, it is the apparatus variant of claim 3 and is therefore rejected under the same rationale.
As per claim 15, it is the apparatus variant of claim 6 and is therefore rejected under the same rationale.
As per claim 16, the combination of Frato and Covell further teach:
The apparatus of claim 11, the operations further comprising: during a training phase, gathering data during use of the cloud computing system from a plurality of users; and using the data to create and update the plurality of user profiles, each user profile comprising a plurality of attributes, wherein one or more of the plurality of attributes each comprise a usage data range for the attribute. (Frato [0018] – [0019])
As per claim 17, it is the apparatus variant of claim 8 and is therefore rejected under the same rationale.
As per claim 18, it is the program product comprising a non-transitory computer readable storage medium variant of claim 1 and is therefore rejected under the same rationale. (Covell [0053]: CRM.)
As per claim 19, it is the program product comprising a non-transitory computer readable storage medium variant of claim 2 and is therefore rejected under the same rationale.
Claim(s) 5, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frato and Covell, and further in view of Dattatri et al (US 20220358025, hereinafter Dattatri).
As per claim 5, the combination of Frato and Covell did not teach:
The method of claim 4, further comprising: gathering, by the processor, data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile.
However, Dattatri teaches:
The method of claim 4, further comprising: gathering, by the processor, data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile. (Dattatri [0033])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Dattatri into that of Frato and Covell in order to gather data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile. Frato [0018] teaches a “the resource management device 104 stores and maintains the information in the user profile 118 so that it can be accessed at a later time when a user requests access to a new resource.” One of ordinary skill in the art can easily see that the resource management device 104 of Frato maintaining the user profile may be accomplished the known methods of comparing actual data usage and update the profile accordingly as demonstrated by Dattatri [0033], in order to achieve the predictable results of updating user profile according to actual runtime data and improve the resource allocation method and is therefore rejected under 35 USC 103.
As per claim 14, the combination of Frato and Covell did not teach:
The method of claim 4, further comprising: gathering, by the processor, data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile.
However, Dattatri teaches:
The apparatus of claim 11, the operations further comprising: gathering, by the processor, data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile. (Dattatri [0033])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Dattatri into that of Frato and Covell in order to gather data during use of the cloud computing system by the user; comparing the data to each of the usage data ranges associated with each of the set of attributes; and updating the user profile for the user in response to determining that the data fits within a different user profile. Frato [0018] teaches a “the resource management device 104 stores and maintains the information in the user profile 118 so that it can be accessed at a later time when a user requests access to a new resource.” One of ordinary skill in the art can easily see that the resource management device 104 of Frato maintaining the user profile may be accomplished the known methods of comparing actual data usage and update the profile accordingly as demonstrated by Dattatri [0033], in order to achieve the predictable results of updating user profile according to actual runtime data and improve the resource allocation method and is therefore rejected under 35 USC 103.
As per claim 20, it is the program product comprising a non-transitory computer readable storage medium variant of claim 14 and is therefore rejected under the same rationale.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frato and Covell, and further in view of Baughman et al (US 20130346614, hereinafter Baughman).
As per claim 9, the combination of Frato and Covell did not teach:
The method of claim 1, wherein an attribute of the user comprises a workload type previously used by the user and wherein the user profile associated with the user comprises a user profile correlated with the workload type.
However, Baughman teaches:
The method of claim 1, wherein an attribute of the user comprises a workload type previously used by the user and wherein the user profile associated with the user comprises a user profile correlated with the workload type. (Baughman [0038].)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Baughman into that of Frato and Covell in order to have the attribute of the user comprises a workload type previously used by the user and wherein the user profile associated with the user comprises a user profile correlated with the workload type. Frato [0018] teaches a “the resource management device 104 stores and maintains the information in the user profile 118 so that it can be accessed at a later time when a user requests access to a new resource”. Covell [0033] teaches the user profile includes “typical computer activities performed 410”. One of ordinary skill in the art can easily see that the user profile data collected and maintained by the resource management device 104 of Frato can be expended to include the type of computing task in order to achieve the predictable results of having a more comprehensive user profile to aid in the scheduling and execution of user request, and is therefore rejected under 35 USC 103.
As per claim 10, the combination of Frato, Covell and Baughman further teach:
The method of claim 9, wherein the workload type is input/output ("I/0") bound, memory bound, and/or central processing unit ("CPU") bound. (Baughman [0038].)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lliev et al (US 20220322986) teaches “determining whether a user employs System 1 type thinking or System 2 type thinking when engaged in a task are disclosed. The systems and methods include determining one or more properties of the task based on information regarding the task received from a database storing information regarding the task, determining one or more properties of the user with respect to the task, determining a state of the user based on one or more physiological sensors configured to sense one or more characteristics of the user, and determining that the user employs System 1 type thinking or System 2 type thinking when engaged in the task based on the determined one or more properties of the task, the determined one or more properties of the user, and the determined state of the user.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES M SWIFT whose telephone number is (571)270-7756. The examiner can normally be reached Monday - Friday: 9:30 AM - 7PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Blair can be reached at 5712701014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES M SWIFT/Primary Examiner, Art Unit 2196