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 Arguments
Applicant’s arguments with respect to the instant amendment 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. The 35 USC 101 rejection is withdrawn.
Allowable Subject Matter
Claims 2, 5-7, 9, 12-14, 16, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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.
Claim(s) 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over LO et al. (PG/PUB 20180328615) in view over Brown et al. (PG/PUB 20210381861).
Claim 1
LO et al. teaches a computer-based method of managing refrigeration for interconnected systems, the method comprising:
receiving historical data from one or more sources in a refrigeration area and one or more user-defined rules (ABSTRACT, 0007-0008, 0044, 00568-69, 0073 e.g. see receiving environmental datums from multiple air conditioners as reading on receiving historical data and see determining whether support is needed as reading on one or more rules, 0007, 0054 e.g. see “supportive strategies.” )
identifying one or more units in the refrigeration area corresponding to a coverage of one or more cooling devices (0008, 0039, 0058-60, Figure 1 e.g. see identifying an air conditioner of a plurality of air conditioners having an influence of a region, Figure 2, Figure 4)
identifying one or more refrigeration area parameters associated with the one or more units based on the historical data (ABSTRACT, 0007-0008, 0044, 00568-69, 0073, Figure 4, figure 5a e.g. see environmental datum comprising at least a temperature area parameter of multiple areas)
determining whether the refrigeration area requires management based on the one or more refrigeration area parameters and the one or more user-defined rules (ABSTRACT, 0007-0008, 0044, 00568-69, 0073, Figure 1 e.g. determine compensation is needed due to at least one air conditioner failing to provide sufficient, target cooling of a zone, Figure 3)
in response to determining the refrigeration area requires the management, executing a predicted action for at least one unit of the one or more units (Figure 3-S10-S32 e.g. see predetermined actions set in advance as a reading on a “prediction action.”)
LO et al. does not expressly teach the controlling limitations described below. Brown et al. teaches the controlling limitations described below
controlling at least one unit to regulate an environmental condition by adjusting one or more settings of at least one cooling device covering the at least one unit, wherein the predicted action is determined using a predictive model based on the one or more refrigeration area parameters (Brown, ABSTRACT, 0045, 0227, 0252, 0262 e.g. “At step 916, automated response processes are initiated. As described above, the automated response processes may include corrective actions that may be implemented to correct non-compliance or to prevent predicted compliance issues. In some embodiments, the automated response processes include controlling building equipment (e.g., HVAC equipment) in order to affect at least one of the TPH for a room or building. For example, the automated response process may include adjusting a setpoint, increasing the output of a building device, activating additional equipment to supplement a primary device, etc. In some embodiments, controlling the building equipment may include activating (e.g., controlling) one or more supplementary or complementary systems in order to provide additional capacity or additional functionality to a primary system. For example, if a component of a primary system fails, which causes a compliance issue, a supplementary system that is substantially similar to the primary system may be active to correct the issue. In any case, the equipment may be controlled to bring the TPH for the room back into compliance,” see alco predictive model for generating prediction actions for remedying compliance issues, 0276-0277 e.g. “At step 1018, the predictive model is executed using the new sensor data, in a manner similar to the step 1006. In this regard, the predictive model may generate new predicted trend data based on the new sensor data. Subsequently, at the step 1020, the new predicted trend data can be analyzed (e.g., compared to a compliance standard) to determine an impact of the automated response process. In some embodiments, the analysis determines whether the new predicted trend data is improved over the trend data predicted at the step 1006. In this regard, the analysis of the new trend data may determine whether predicted future compliance issues have been resolved. For example, if the previously predicted trend data indicates predicted future non-compliance and the new predicted trend data does not, the automated response process may be deemed as having a positive impact. More generally, the new trend data may be analyzed to determine whether the automated response process has a positive or a negative impact with respect to prevent future compliance issues. At step 1022, the predictive model is updated. Updating the model may increase the speed and accuracy of the model over time, by comparing the accuracy of a prediction to an actual result or by identifying positive or negative impacts of a response process. In some embodiments, the predictive model is dynamically updated or improved through reinforcement learning. Reinforcement learning can include generating a reward value based on either the prediction made by the predictive model (e.g., at the step 1008) and the accuracy of the prediction, or the impact of the automated response processes.
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Brown, namely applying predictive models for determining prediction actions based on HVAC fault analysis for remedying non-compliant temperatures, to the teachings of LO, namely identifying and addressing HVAC faults, would achieve an expected and predictable result of resolving temperature non-compliance based on predicting the effect of actions and selecting an optimal action to address the non-compliance. Brown is in the same field of endeavor and reasonably pertinent to a problem of predicting actions via a prediction model to resolve temperature non-compliance.
Claim 8.
LO et al. teaches computer system, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
receiving historical data from one or more sources in a refrigeration area and one or more user-defined rules, supra claim 1
identifying one or more units in the refrigeration area corresponding to a coverage of one or more cooling devices; supra claim 1
identifying one or more refrigeration area parameters associated with the one or more units based on the historical data; supra claim 1
determining whether the refrigeration area requires management based on the one or more refrigeration area parameters and the one or more user-defined rules; supra claim 1
in response to determining the refrigeration area requires the management, executing a predicted action for at least one unit of the one or more units, supra claim 1
controlling at least one unit to regulate an environmental condition by adjusting one or more settings of at least one cooling device covering the at least one unit, wherein the predicted action is determined using a predictive model based on the one or more refrigeration area parameters, supra claim 1
Claim 15.
LO et al. teaches a computer program product, the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
receiving historical data from one or more sources in a refrigeration area and one or more user-defined rules; supra claim 1
identifying one or more units in the refrigeration area corresponding to a coverage of one or more cooling devices; supra claim 1
identifying one or more refrigeration area parameters associated with the one or more units based on the historical data; supra claim 1
determining whether the refrigeration area requires management based on the one or more refrigeration area parameters and the one or more user-defined rules; supra claim 1
in response to determining the refrigeration area requires the management, executing a predicted action for at least one unit of the one or more units, supra claim 1
controlling at least one unit to regulate an environmental condition by adjusting one or more settings of at least one cooling device covering the at least one unit, wherein the predicted action is determined using a predictive model based on the one or more refrigeration area parameters, supra claim 1
Claim(s) 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over LO et al. in view over Brown et al. (PG/PUB 20210381861) in view over Demetriou et al. (USPN 9996092).
Claim 3.
The computer-based method of claim 1 but does not teach the probability value limitations described below. Demetriou et al. teaches the probability value limitations described below.
wherein the one or more user-defined rules include a probability value within a threshold of room temperature, a threshold for a lower proportion of available power, and a threshold for a higher proportion of available power (Demetriou et al., ABSTRACT , Figure 3, Figure 1, Figure 4-100, 108, Col 6 lines 26-49, Col 7 lines 1-67 e.g. see predicting time period until temperature reaches a threshold temperature as reading on “probability value within a threshold,” In an embodiment, when the query block 110 returns a negative, this means that there may be insufficient time to correct the issue with the cooling system before the evaluated computer server 22 reaches the threshold temperature T*. In this embodiment, the process 88 may take proactive steps to reduce the dT/dt of the evaluated computer server 22 to avoid reaching the threshold temperature T* or increase the time period before the threshold temperature T* is reached. It should be appreciated that these proactive steps may be performed automatically, such as by the predictive analytics system 38 for example, or recommended actions may be transmitted to the data center operator.
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Demetriou et a, namely adjusting a cooling system based on a probability of exceeding a temperature threshold, to the teachings of LO, namely adjusting a cooling unit responsive to a temperature exceeding a threshold through compensating, would achieve an expected and predictable result of implementing pre-emptive control before exceeding a temperature threshold. Demetriou et al. is reasonably pertinent to a problem of predictive actions based on probabilities in light of temperature thresholds.
Claim 4.
LO, as modified, teaches the computer-based method of claim 3, wherein determining whether the refrigeration area requires the management further comprises:
identifying a room temperature value of the probability value for each unit according to the one or more user-defined rules, Col 6 lines 26-49, Col 7 lines 1-67 e.g. “ In an embodiment, when the query block 110 returns a negative, this means that there may be insufficient time to correct the issue with the cooling system before the evaluated computer server 22 reaches the threshold temperature T*. In this embodiment, the process 88 may take proactive steps to reduce the dT/dt of the evaluated computer server 22 to avoid reaching the threshold temperature T* or increase the time period before the threshold temperature T* is reached. It should be appreciated that these proactive steps may be performed automatically, such as by the predictive analytics system 38 for example, or recommended actions may be transmitted to the data center operator.
in response to determining at least one identified room temperature value is greater than the threshold of room temperature, determining the at least one unit having the at least one identified room temperature value greater than the threshold of room temperature lacks cooling resources, supra claim 1 for determining insufficient cooling due to target temperature being exceeded)
Claim 10. The computer system of claim 8, wherein the one or more user-defined rules include a probability value within a threshold of room temperature, a threshold for a lower proportion of available power, and a threshold for a higher proportion of available power, supra claim 3
Claim 11. The computer system of claim 10, wherein determining whether the refrigeration area requires the management further comprises:
identifying a room temperature value of the probability value for each unit according to the one or more user-defined rules; and
in response to determining at least one identified room temperature value is greater than the threshold of room temperature, determining the at least one unit having the at least one identified room temperature value greater than the threshold of room temperature lacks cooling resources, supra claim 4
Claim 17. The computer program product of claim 15, wherein the one or more user-defined rules include a probability value within a threshold of room temperature, a threshold for a lower proportion of available power, and a threshold for a higher proportion of available power, supra claim 3
Claim 18. The computer program product of claim 17, wherein determining whether the refrigeration area requires the management further comprises:
identifying a room temperature value of the probability value for each unit according to the one or more user-defined rules; supra claim 4
in response to determining at least one identified room temperature value is greater than the threshold of room temperature, determining the at least one unit having the at least one identified room temperature value greater than the threshold of room temperature lacks cooling resources, supra claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
11860729 – claim 1 relevancy for prediction actions e.g. “Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion. The system applies the remedial action to the particular problematic system behavior, despite already determining that the predicted effectiveness does not satisfy the one or more performance criteria.”
11449379 – claim 1 relevancy for prediction actions e.g. “A process collects information about multiple systems of a computing environment from environmental tools web sources. The process builds and trains a classification model that extracts relevant information that classifies technical issues of the computing environment. The process performs root cause and predictive analyses, and identifies root causes of experienced technical issues and predicted technical issues. Based on identifying the root causes of the experienced technical issues, or identifying predicted issues, the process provides suggested corrective actions or optimization actions. Effectiveness of the suggested corrective actions and suggested optimization actions is tracked, and results of the tracking are fed into a training process that further trains the classification model.”
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.
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
