DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 3/31/2026.
Claims 1, 9, and 17 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the amendments. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner points to the rejection below. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
The Examiner notes how broad the claim actually is worded. Specifically pointing to “storing data associated with the composite score in a table of the storage”. The Examiner notes under BRI that associated with the composite score is any data used or taken into consideration when determining the composite score (e.g. the performance score), which is clearly taught by Cox et al. in at least Figure 1, Figure 4, and Paragraphs 0024-0025. Applicant’s arguments are not persuasive.
The Examiner further notes that feature code as claimed is clearly taught with the ranking in Paragraphs 0037-0040 and Figure 4 of Cox et al. Applicant’s arguments are not persuasive.
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 9-20 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.
Regarding Claims 9-20: Claims 9 and 17 recite “the storage”. There is insufficient antecedent basis for this claim limitation. Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 9-20 are directed toward a process and claims 1-8 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward method comprising: receiving, by a processor of a first information handling system, first telemetry data associated with a second information handling system; based on the first telemetry data and user survey data associated with the second information handling system, training a machine learning (ML) model; storing the trained ML model in the first information handling system; receiving second telemetry data for the second information handling system; executing, by the processor, the trained ML model to determine a composite score for the second information handling system; storing data associated with the composite score in a table of the storage, wherein the data includes a feature code identifying a top feature contributing to the composite score; and based on the composite score, providing a remediation event for the second information handling system (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing telemetry and survey data of information handling systems to make recommendations about how to operate the information handling systems, which is a commercial interaction.
Dependent claims 2-8, 10-16, and 18-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “an information handling system comprising: a storage configured to store a machine learning (ML) model; and a processor to communicate with the storage, the processor to; train the ML model (claim 1)”; “by a processor of a first information handling system; a second information handling system; training a machine learning (ML) model; by the processor (claims 9 and 17)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-8, 10-16, and 18-20 further narrow the abstract idea and recite no additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)) and the further analysis narrows the abstract idea.
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, Method; System Independent claims 1, 9, and 17 recite “an information handling system comprising: a storage configured to store a machine learning (ML) model; and a processor to communicate with the storage, the processor to; train the ML model (claim 1)”; “by a processor of a first information handling system; a second information handling system; training a machine learning (ML) model; by the processor (claims 9 and 17)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0012-0013 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-8, 10-16, and 18-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed/scored. The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 9-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cox et al. (US 2021/0232425 A1) in view of Landry (US 2020/0118152 A1).
Regarding Claims 1, 9, and 17: Cox et al. teach a method comprising (See Abstract and claim 1):
receiving, by a processor of a first information handling system, first telemetry data associated with a second information handling system (See Figure 1, Paragraph 0024 – “An operating system telemetry module 104 may collect telemetry data from the operating system 102. A telemetry collector 106 may collect telemetry data from the operating system telemetry module 104, the one or more applications 108, and the hardware sensors 110”, Paragraph 0025 – “An optimization importance model 114 may receive persona experience importance information from the persona experience importance model 116 and telemetry data from the telemetry collector 106”, and Paragraph 0036 – “monitor performance parameters of the information handling system. Monitoring performance parameters may include both monitoring internal performance statistics, such as resource and input/output utilization by the top-ranked process, overall system utilization, and system events, and monitoring user actions taken after the adjustments are made”);
based on the first telemetry data and user data associated with the second information handling system, training a machine learning (ML) model (See Figure 1 and Paragraphs 0024-0025 – “An optimization importance model 114 may receive persona experience importance information from the persona experience importance model 116 and telemetry data from the telemetry collector 106”, Paragraph 0026 – “The optimization importance model 114, persona experience importance model 116, dynamic user experience score module 112, telemetry collector 106, state aggregation module 118, policy discovery module 120, policy observations database 122, expected future events module 124, decision module 126, mock application module 128, and application module 130 may all be components of a reinforcement learning system of the information handling system for improving information handling system performance in an automated manner using intelligence from previous decisions”, and Paragraph 0030 – “The reinforcement learning algorithm may learn from user interactions with the information handling system and performance of the information handling system and may adapt continuously to a changing environment”);
storing the trained ML model in the first information handling system (See Figure 1 and Paragraph 0027 – “The reinforcement learning loop 200 may include an environment 204 and an agent 202. The agent 202 may, for example, include one or more applications configured to monitor, configure, and communicate with applications, an operating system, firmware, and hardware of an information handling system. The environment 204 may, for example, be an operating environment of the agent 202, such as a software stack of the information handling system, which may include an embedded controller, a basic input/output system (BIOS), an operating system (OS), and applications executed by the information handling system”);
receiving second telemetry data for the second information handling system (See Figure 1, Paragraph 0024 – “The information handling system 100 may also execute one or more applications 108. The operating system 102 and the applications 108 may each execute one or more system processes. A user may interact with the applications 108 and/or the operating system 102 via a user interface 132. Hardware sensors and controls 110 of the information handling system 100 may monitor and control hardware operation of the information handling system 100. An operating system telemetry module 104 may collect telemetry data from the operating system 102. A telemetry collector 106 may collect telemetry data from the operating system telemetry module 104, the one or more applications 108, and the hardware sensors 110”, Paragraph 0025, and Paragraph 0036 – “monitor performance parameters of the information handling system. Monitoring performance parameters may include both monitoring internal performance statistics, such as resource and input/output utilization by the top-ranked process, overall system utilization, and system events, and monitoring user actions taken after the adjustments are made”);
executing, by the processor, the trained ML model to determine a composite score for the second information handling system (See Figure 1, Figure 6, Paragraphs 0024-0025 – “The telemetry data collected by the telemetry collector 106 may be provided to a dynamic user experience score module 112 to determine a dynamic user experience score”, Paragraph 0037 – “the information handling system may determine whether a performance score is less than a threshold performance score”, and the Examiner interprets the performance score to be the composite score claimed);
storing data associated with the composite score in a table of the storage, wherein the data includes a feature code identifying a top feature contributing to the composite score (See Figure 1, Figure 4, Paragraph 0002 – “An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information”, Paragraph 0024, Paragraph 0025, Paragraph 0037 – “determine whether a performance score is less than a threshold performance score”, Paragraph 0039 – “reduce the ranking of the top-ranked process”);
and based on the composite score, providing a remediation event for the second information handling system (See Figure 1, Figure 6, Paragraph 0026, Paragraph 0028 – “The agent 202 may monitor the environment 204 and may adjust the environment based on the monitoring to enhance performance”, Paragraph 0030, and Paragraphs 0039-0040 – “If the performance score is determined, at step 608, to be less than the threshold performance score, the information handling system may, at step 610, reduce the ranking of the top-ranked process”).
Cox et al. do not specifically disclose user “survey” data. However, Landry further teaches user “survey” data (See Figure 1B, Abstract, Paragraph 0011 – “A user of an electronic device such as a printer, laptop, etc. may be asked to provide feedback of the product to better identify potential technical issues and to gauge user experience. The feedback is provided in the form of surveys”, and claim 1 – “collecting, in a computer system, telemetry data from at least one electronic device; collecting, in the computer system, survey data related to user feedback associated with the at least one electronic device; correlating, in the computer system, data patterns in the telemetry data with data patterns in the survey data; and linking, in the computer system, the survey data with the telemetry data based on the correlated data patterns to contextualize the user feedback to the telemetry data”).
The teachings of Cox et al. and Landry are related because both are analyzing systems using telemetry data to make determinations about what a user should do. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the information handling scoring system of Cox et al. to incorporate the survey of Landry in order to ensure users can provide input into the system for the determinations.
Regarding Claims 2, 10, and 18: Cox et al. in view of Landry teach the limitations of claim 1. Cox et al. further teach wherein during the training of the ML model, the processor further to: correlate the first telemetry data to the first user data (See Figure 1 and Paragraphs 0024-0025 – “An optimization importance model 114 may receive persona experience importance information from the persona experience importance model 116 and telemetry data from the telemetry collector 106”, Paragraph 0026 – “The optimization importance model 114, persona experience importance model 116, dynamic user experience score module 112, telemetry collector 106, state aggregation module 118, policy discovery module 120, policy observations database 122, expected future events module 124, decision module 126, mock application module 128, and application module 130 may all be components of a reinforcement learning system of the information handling system for improving information handling system performance in an automated manner using intelligence from previous decisions”, and Paragraph 0030 – “The reinforcement learning algorithm may learn from user interactions with the information handling system and performance of the information handling system and may adapt continuously to a changing environment”).
Cox et al. do not specifically disclose user “survey” data. However, Landry further teaches user “survey” data (See Figure 1B, Abstract, Paragraph 0011 – “A user of an electronic device such as a printer, laptop, etc. may be asked to provide feedback of the product to better identify potential technical issues and to gauge user experience. The feedback is provided in the form of surveys”, and claim 1 – “collecting, in a computer system, telemetry data from at least one electronic device; collecting, in the computer system, survey data related to user feedback associated with the at least one electronic device; correlating, in the computer system, data patterns in the telemetry data with data patterns in the survey data; and linking, in the computer system, the survey data with the telemetry data based on the correlated data patterns to contextualize the user feedback to the telemetry data”).
The teachings of Cox et al. and Landry are related because both are analyzing systems using telemetry data to make determinations about what a user should do. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the information handling scoring system of Cox et al. to incorporate the survey of Landry in order to ensure users can provide input into the system for the determinations.
Regarding Claims 3, 11, and 19: Cox et al. in view of Landry teach the limitations of claim 1. Cox et al. further teach wherein the processor further to: group the second information handling system into one of a plurality of groups based on the composite score (See Figure 6 and Paragraph 0037 – “determine whether a performance score is less than a threshold performance score”).
Regarding Claims 4, 12, and 20: Cox et al. in view of Landry teach the limitations of claim 1. Cox et al. further teach wherein each different one of the plurality of groups is associated with a different user experience for the second information handling system (See Figure 6 and Paragraph 0037 – “determine whether a performance score is less than a threshold performance score … acts taken by a user or internal performance statistics that indicate that the adjustments had a negative impact on system performance may decrease the performance score, while acts taken by the user or internal performance statistics that indicate that the adjustments had a positive impact on system performance may increase the performance score”).
Regarding Claims 5 and 13: Cox et al. in view of Landry teach the limitations of claim 1. Cox et al. further teach wherein during the execution of the ML model, the processor further to: determine a hardware component score based on the second telemetry data (See Paragraphs 0029-0030 – “After adjusting settings, the agent 202 may monitor the environment 204 to determine if the adjustments improved performance (e.g., responsiveness, frame rate, lower processor utilization, more processor time available for a top-ranked process, lower processor temperature, longer battery life, etc.) of the information handling system. If the adjustments did improve performance, the agent 202 may calculate a reward”).
Regarding Claims 6 and 14: Cox et al. in view of Landry teach the limitations of claim 5. Cox et al. further teach wherein during the execution of the ML model, the processor further to: determine an operating system and application score based on the second telemetry data (See Paragraph 0024 – “The telemetry data collected by the telemetry collector 106 may be provided to a dynamic user experience score module 112 to determine a dynamic user experience score … rank processes executed by the information handling system, such as processes of the applications 108 and operating system 102 based on importance of the processes to a user experience of users”).
Regarding Claims 7 and 15: Cox et al. in view of Landry teach the limitations of claim 6. Cox et al. further teach wherein during the execution of the ML model, the processor further to: determine a startup and boot score based on the second telemetry data (See Paragraph 0024 – “The telemetry data collected by the telemetry collector 106 may be provided to a dynamic user experience score module 112 to determine a dynamic user experience score … different personas may be assigned to different users based on behavior observed in the telemetry data, such as frequent gaming, offline-to-online or online-to-offline status changes, frequent use of video editing applications, and other user behaviors”).
Claims 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cox et al. (US 2021/0232425 A1) in view of Landry (US 2020/0118152 A1) and further in view of Brown et al. (US 2021/0141900 A1).
Regarding Claims 8 and 16: Cox et al. in view of Landry teach the limitations of claim 7. Cox et al. further teach:
the hardware component score (See Paragraphs 0029-0030 – “After adjusting settings, the agent 202 may monitor the environment 204 to determine if the adjustments improved performance (e.g., responsiveness, frame rate, lower processor utilization, more processor time available for a top-ranked process, lower processor temperature, longer battery life, etc.) of the information handling system. If the adjustments did improve performance, the agent 202 may calculate a reward”);
the operating system and application score (See Paragraph 0024 – “The telemetry data collected by the telemetry collector 106 may be provided to a dynamic user experience score module 112 to determine a dynamic user experience score … rank processes executed by the information handling system, such as processes of the applications 108 and operating system 102 based on importance of the processes to a user experience of users”);
and the startup and boot score (See Paragraph 0024 – “The telemetry data collected by the telemetry collector 106 may be provided to a dynamic user experience score module 112 to determine a dynamic user experience score … different personas may be assigned to different users based on behavior observed in the telemetry data, such as frequent gaming, offline-to-online or online-to-offline status changes, frequent use of video editing applications, and other user behaviors”).
Cox et al. in view of Landry do not specifically disclose wherein the composite score is a weighted average of the “scores”. However, Brown et al. further teach wherein the composite score is a weighted average of the “scores” (See Paragraph 0210 – “The stream of metric data may represent network traffic, memory usage, or CPU usage for a server computer that runs a periodically executed VM. The low amplitude time intervals 3411-3414 represent time intervals in which the VM is idle. Pulses 3406-3410 represent time intervals when the VM is running. This stream of metric data is an example of metric data modeled using a pulse wave model 2910”, Paragraph 0212 – “An exponentially weighted moving average (“EWMA”) of absolute differences between two consecutive non-trendy metric values denoted by Δ.sub.i=|z.sub.i−z.sub.i−1| is maintained for i=1, . . . , n metric values in the historical window”, and Paragraph 0233).
The teachings of Cox et al., Landry, and Brown et al. are related because all are analyzing systems to make determinations about what a user should do. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the information handling scoring system of Cox et al. in view of Landry to incorporate the weighted average score of Brown in order to ensure the user has input on what the important aspects/priorities of the system should be.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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