DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Application filed on 11/01/2023.
Claims 1-20 are pending. Claims 1, 9, and 17 are independent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regards to claim 1,
Step 2A, Prong 1
Claim 1 recites:
An information handling system comprising:
a memory to store telemetry data for the information handling system; and
a processor to communicate with the memory, wherein the processor to:
determine one or more types of the telemetry data;
determine one or more machine learning models to be executed, wherein each different machine learning model corresponds to a different type of telemetry data;
determine one or more constraints for the machine learning models;
based on the one or more constraints, determine a device to execute the machine learning models, wherein the machine learning models are executed in the determined device, wherein the telemetry data is provided as inputs to the machine learning models; and
based on the execution of the machine learning models, determine a persona for the information handling system.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can determine a data type, select a machine learning model for the data type, determine a constraint, select a device, and determining a persona mentally or with a pen and paper.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
The additional elements in this claim are “a memory” and “a processor”. These elements are recited at a high level of generality and this is a generic computer component performing computer functions. This these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
Even when viewed in combination the additional element does not integrate the recited judicial exception in to a practical application.
Step 2A, Prong 2 (No).
Step 2B
As explained with respect to Step 2A, the only additional element is “one or more processors” which at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step2B (No).
Claim 1 is ineligible.
With respect to claims 9 and 17,
These claims are similar in scope to claim 1 and are rejected under a similar rationale. The processors and memory relied in these claims are also generic computing components.
Claims 9 and 17 are ineligible.
Dependent Claims:
Claims 2-8, 10-16, and 18-20: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
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, 5-9, and 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comer et al. (US2023/0043991) in view of Lee et al. (US2022/0100809).
IN regards to claim 1, Comer et al. discloses an information handling system comprising:
a memory to store telemetry data for the information handling system (Comer et al. fig. 1 105 para[0026]); and
a processor to communicate with the memory (Comer et al. fig. 1 101 para[0026]), wherein the processor to:
determine one or more types of the telemetry data (Comer et al. para[0057], The productivity and security targets may also be based on user’s behavioral analytics HIS telemetry and/or environmental information);
determine one or more machine learning models to be executed, wherein each different machine learning model corresponds to a different type of telemetry data (Comer et al. para[0127], determines which optimization algorithm in an algorithm repository for a given class is most relevant for the current context);
determine one or more constraints for the machine learning models (Comer et al. para[0121], selection of optimization method based on determined constraints (workspace definition)); a
Comer et al. does not explicitly disclose based on the one or more constraints, determine a device to execute the machine learning models, wherein the machine learning models are executed in the determined device, wherein the telemetry data is provided as inputs to the machine learning models; and
based on the execution of the machine learning models, determine a persona for the information handling system.
However Lee et al. discloses based on the one or more constraints, determine a device to execute the machine learning models, wherein the machine learning models are executed in the determined device, wherein the telemetry data is provided as inputs to the machine learning models (Lee et al. fig. 7 para[0098], At step 708, based on the at least one determined persona trait of the user, the client device can assign the user to at least one persona category selected from the plurality of persona categories specified by the persona categorization data.); and
based on the execution of the machine learning models, determine a persona for the information handling system(Lee et al. para[0070], The machine learning models can process the keywords and their scores to determine at least one persona category and, optionally, at least one context category, to which to assign the user of the client device.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the optimization method of Comer et al. with the characterization method of Lee et al. in order to personalize services without collecting private user data (Lee et al. para[0017]).
In regards to claim 5, Comer et al. as modified by Lee et al. discloses the information handling system of claim 1, wherein a different label is provided as an output from a different one of the machine learning models (Comer et al. para[0130], Training ML models may include creating a set of labeled training samples for corresponding optimization algorithm).
In regards to claim 6, Comer et al. as modified by Lee et al. discloses the information handling system of claim 5, wherein the persona for the information handling system is based on a combination of the different labels (Comer et al. para[0012], User context may include: an identification of a persona, an identification of a preferred application, and an identification of a user presence).
In regards to claim 7, Comer et al. as modified by Lee et al. discloses the information handling system of claim 1, wherein one of the constraints is a latency requirement for optimization within the information handling system (Comer et al. para[0059], determine upper limit on latency).
In regards to claim 8, Comer et al. as modified by Lee et al. discloses the information handling system of claim 1, wherein an update to the information handling system is based on the persona of the information handling system (Comer et al. para[0092], calculates productivity score associated with IHS based on user persona).
Claims 9, and 13-16 recite substantially similar limitations to claims 1, and 5-8. Thus claims 9, and 13-16 are rejected along the same rationale as claims 1, and 5-8.
In regards to claim 17, Comer et al. discloses an information handling system comprising:
a memory to store telemetry data for the information handling system (Comer et al. fig. 1 105 para[0026]); and
a processor(Comer et al. fig. 1 101 para[0026]) to:
determine one or more types of the telemetry data (Comer et al. para[0057], The productivity and security targets may also be based on user’s behavioral analytics HIS telemetry and/or environmental information);
determine one or more machine learning models to be executed, wherein each different machine learning model corresponds to a different type of telemetry data(Comer et al. para[0127], determines which optimization algorithm in an algorithm repository for a given class is most relevant for the current context);
determine one or more constraints for the machine learning models (Comer et al. para[0121], selection of optimization method based on determined constraints (workspace definition)).
Comer et al. does not explicitly disclose determine a normalized score for the one or more constraints;
based on the normalized score, determine a device to execute the machine learning models, wherein the machine learning models are executed in the determined device, and the telemetry data is provided as inputs to the machine learning models;
based on the execution of the machine learning models, determine a persona for the information handling system, wherein an update to the information handling system is based on the persona of the information handling system.
However Lee et al. discloses determine a normalized score for the one or more constraints (Lee et al. para[0033], The categorizer 114 can normalize the trait data, for example to a value from 0 to 1.);
based on the normalized score, determine a device to execute the machine learning models, wherein the machine learning models are executed in the determined device, and the telemetry data is provided as inputs to the machine learning models (Lee et al. fig. 7 para[0098], At step 708, based on the at least one determined persona trait of the user, the client device can assign the user to at least one persona category selected from the plurality of persona categories specified by the persona categorization data.);
based on the execution of the machine learning models, determine a persona for the information handling system, wherein an update to the information handling system is based on the persona of the information handling system (Lee et al. para[0070], The machine learning models can process the keywords and their scores to determine at least one persona category and, optionally, at least one context category, to which to assign the user of the client device.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the optimization method of Comer et al. with the characterization method of Lee et al. in order to personalize services without collecting private user data (Lee et al. para[0017]).
Claims 18-19 recite substantially similar limitations to claims 5- 6. Thus claims 18-19 are rejected along the same rationale as claims 5- 6.
.
Claim(s) 2-4, 10-12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comer et al. in view of Lee aet al. and Kocberber et al. (US2020/0118039).
In regards to claim 2, Comer et al. as modified by Lee et al. discloses the information handling system of claim 1. Comer et al. does not explicitly disclose wherein each of the constraints includes a constraint state value indicating whether an associated machine learning model should be executed in the information handling system, a cloud server, or an edge device
However Kocberber et al. discloses wherein each of the constraints includes a constraint state value indicating whether an associated machine learning model should be executed in the information handling system, a cloud server, or an edge device (Kocberber et al. para[0061], The advantage of using provider edge devices is providing advantages of (i) and (ii) at the cost of additional telemetry collection from PE devices. Overall, these techniques achieve the same goal and depending on the systems logistics, a decision may be made to employ a specific one of the three sources.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the optimization method of Comer et al. with the characterization method of Kocberber et al. in order to efficiently allocate server resources (Kocberber et al. para[0006]).
In regards to claim 3, Comer et al. as modified by Lee et al. and Kocberber et al. discloses the information handling system of claim 2, wherein the processor further to: based on the constraint state values, determine a normalized score for the constraints (Kocberber et al. para[0062], data is cleaned for null values and normalized).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the optimization method of Comer et al. with the characterization method of Kocberber et al. in order to efficiently allocate server resources (Kocberber et al. para[0006]).
In regards to claim 4, Comer et al. as modified by Lee et al and Kocberber et al. discloses the information handling system of claim 3, wherein the determination of the device to execute the machine learning models is based on the normalized score (Kocberber et al. para[0081], The preprocessing may include cleaning the readings for null values, normalizing the data, downsampling the features, etc. Further preprocessing may involve generating an enhanced feature set based on features selected for server utilization ML modeling as described above).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the optimization method of Comer et al. with the characterization method of Kocberber et al. in order to efficiently allocate server resources (Kocberber et al. para[0006]).
Claims 10-12 recite substantially similar limitations to claims 2-4. Thus claims 10-12 are rejected along the same rationale as claims 2-4.
Claim 20 recites substantially similar limitations to claim 2. Thus claim 20 are rejected along the same rationale as claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yao et al. (US2025/0086493) teaches computing weights based on constraints for an edge computing.
Nanjundawamy et al. (US2024/0211233) teaches updating information handling systems.
Vichare et al. (US2021/0117856) teaches using machine learning models to optimize resources of information handling systems.
Boyapalle et al. (US10073753) teaches utilizing resources of information handling systems.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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