DETAILED ACTION
This office action is in response to amendment filed on 5/19/2026.
Claims 1, 3, 5, 9, 11, 13, 14, 18 and 20 are amended.
Claims 1 – 20 are pending.
35 USC 112b rejection of claims 2-5, 12, 13 and 19 are withdrawn in view of the amendment.
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 – 4, 7, 8, 11 – 13 and 16 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frato et al (US 20230336557, hereinafter Frato), in view of Zhang et al (US 20180091999, hereinafter Zhang).
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 of a plurality of user profiles, (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 associated with the user. (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 a plurality of other users to create the plurality of user profiled based on attributes of the plurality of users, wherein each of the plurality of user profile is associated with a set of attributes and a set of system resources, and wherein the learning model is configured to determine the user profile of the plurality of user profiles to associate with the user based on the attributes of the user;
However, Zhang teaches:
wherein the user profile is associated using a learning model, wherein the learning model uses machine learning to characterize attributes of a plurality of other users to create the plurality of user profiled based on attributes of the plurality of users, wherein each of the plurality of user profile is associated with a set of attributes and a set of system resources, and wherein the learning model is configured to determine the user profile of the plurality of user profiles to associate with the user based on the attributes of the user; (Zhang [0072]: “proxy device 230 may determine a profile for user device 205. For example, a profile may include a set of settings (e.g., parameter values) that may be applied to the first flow and/or the second flow. In some implementations, proxy device 230 may correlate a profile with a device identifier of user device 205. In some implementations, proxy device 230 may receive, from user device 205, additional requests for resources, and may determine parameter values to be applied to a flow based on the profile (e.g., based on performing a lookup in a data structure). For example, proxy device 230 may determine a device identifier of user device 205, and may identify a profile, to be applied to a flow, based on the device identifier. In some implementations, proxy device 230 may select a particular profile (e.g., that includes a particular set of settings) based on the condition information associated with user device 205 and/or the condition information associated with base station 210. For example, particular conditions, combinations of conditions, etc., may map to a particular profile. In this case, proxy device 230 may apply the profile (e.g., a set of parameter values associated with the profile) to a flow based on the mapping. Additionally, or alternatively, proxy device 230 may select a particular profile based implementing a technique (e.g., a model).”; [0021]: “User device 205 includes one or more devices capable of connecting to a network via base station 210.”)
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 Zhang 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 a plurality of other users to create the plurality of user profiled based on attributes of the plurality of users, wherein each of the plurality of user profile is associated with a set of attributes and a set of system resources, and wherein the learning model is configured to determine the user profile of the plurality of user profiles to associate with the user based on the attributes of the user. 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 Frato and Zhang 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. (Zhang [0068])
As per claim 3, the combination Frato and Zhang 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. (Zhang [0066])
As per claim 4, the combination Frato and Zhang 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. (Zhang [0066])
As per claim 7, the combination Frato and Zhang 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 Frato and Zhang 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 16, the combination of Frato and Zhang 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. (Zhang [0038]: 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, 6, 14, 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frato and Zhang, and further in view of Dattatri et al (US 20220358025, hereinafter Dattatri).
As per claim 5, the combination of Frato and Zhang 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 of the plurality of user profiles.
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 of the plurality of user profiles. (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 Zhang 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 6, the combination of Frato and Zhang did not 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.
However, Dattatri teaches:
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. (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 Zhang in order to gather data during use of the cloud computing system by the user and updating the learning model based on the data. 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 Zhang did not teach:
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 of the plurality of user profiles.
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 of the plurality of user profiles. (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 Zhang 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 15, it is the apparatus variant of claim 6 and is therefore rejected under the same rationale.
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 Zhang, and further in view of Baughman et al (US 20130346614, hereinafter Baughman).
As per claim 9, the combination of Frato and Zhang 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 of the plurality of user profiles 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 of the plurality of user profiles 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 Zhang 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”. 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, Zhang 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].)
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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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/CHARLES M SWIFT/Primary Examiner, Art Unit 2196
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