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 .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
This Action is in response to communications filed 10/29/2025.
Claims 15-16 and 18 have been cancelled.
Claims 1, 3-7, 9, 13-14, and 20 have been amended.
Claim 21 is newly added.
Claims 1-14, 17, and 19-21 are pending.
Claims 1-14, 17, and 19-21 are rejected.
Response to Amendment
In the Remarks filed 10/29/2025, Applicant has amended:
On Page 9, the language of claim 4 to address the antecedent basis issue. The Examiner therefore withdraws the corresponding 112(b) rejection made in the Office action dated 07/29/2025.
On Page 9, Claim 18 is now cancelled which previously recited a relative term of degree thereby rendering the limitation as indefinite and the rejection is moot. The Examiner therefore withdraws the corresponding 112(b) rejection made in the Office action dated 07/29/2025. However, as noted in the Remarks, currently amended claim 1 now recites a limitation containing the relative term of degree and Applicant argues the term is now made clear and definite due to the current recitation. The Examiner disagrees and represents the issue in the corresponding 112(b) rejection made herein.
On Page 10, the language of independent claim 1 in order to direct the claim to an improvement to computer functionality in order to render the claim as eligible under 35 U.S.C. § 101. The Examiner determines the amendments as being sufficient to render the claim as eligible and therefore withdraws the corresponding 101 rejection made in the Office action dated 07/29/2025.
Response to Arguments
In Remarks filed on 10/29/2025, Applicant substantially argues:
On Pages 10-13, the applied references including Vaysman and Dunn do not teach the currently amended limitations of claim 1, and similarly amended claim 9 and claim 14, regarding the selecting one of the plurality of predefined thermal models having the respective output which best fits the first thermal data. Applicant’s arguments filed have been fully considered but they are moot in view of the current rejection made in response to Applicant’s amendments.
The applied references fail to disclose the limitations of claims 2-8, 10-13, and 17-21 by virtue of dependency on respective independent claims 1, 8, and 14 for the reasons identified above. Applicant’s arguments filed have been fully considered but they are moot in view of the current rejection made in response to Applicant’s amendments.
Newly added claim 21 is addressed for the first time in the current action.
All arguments by the applicant are believed to be covered in the body of the office action; thus, this action constitutes a complete response to the issues raised in the remarks dated October 29, 2025.
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 1-14, 17, and 19-21 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 pre-AIA the applicant regards as the invention.
Claim 1 recites “select one of the plurality of predefined thermal models having the respective output that best fits the first thermal data”. Herein the term “best fits” is a relative term of degree and renders the limitation indefinite as the claim lacks significant detail to ascertain the scope of the model as being determined to “best fit” the thermal data. Specifically, the claim requires that the respective output of the model “best fits the first thermal data” but this recitation is unclear as to determination being made if it is irrespective of the model or specific to each model output. The standard or degree to which the output is can be considered “best fit” to the first thermal data is not sufficiently established to render the limitation as definite. The Examiner reminds Applicant that details from the Specification are not imported into the claims. See MPEP 2111.01(II).
Respective dependent claims 2-8 do not resolve this issue.
Furthermore, claim 1 recites “wherein each of the plurality of thermals models is created…” Herein the limitation lacks proper antecedent basis with respect to the prior limitation in the claim 1 which recites “wherein the apparatus stores a plurality of predefined thermal models”.
Claim 9 and Claim 14 recite the same issues as claim 1. Respective dependent claims 10-13, 17, and 19-21 do not resolve the issue.
Appropriate correction is required.
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 of this title, 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(a) 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, 3, 6, 14, 17, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Muralimanohar et al. (US 2017/0357463) in view of McKiernan (US 2018/0348301) and further in view of Bill (2022/0134815) and still in further view of Amin-Shahidi et al. (US 2018/0004260).
Regarding claim 1, Muralimanohar discloses, in the italicized portions, a memory apparatus, comprising: a memory array; and a controller coupled to the memory array, wherein the memory controller is configured to (Figure 3, controller 300 and memory devices 308 and 309 comprising memory arrays): capture first thermal data of the apparatus during a first time period; wherein the first thermal data includes a first command sequence, a thermal solution of the apparatus, and a deployment setting of the apparatus ([0010] The characterization engine 110 is to receive information 112 regarding a memory device, and characterize expected temperature exposure 114 of the memory device based on the information 112. The characterization engine 110 also is to store the characterization profile 116 for a plurality of memory devices of a computing system. [0011] In some example implementations, the information 112 for a memory device can be provided as a location of the memory device, e.g., in a region of a computing system. The characterization engine 110 can then identify the location relative to heat sources and cooling/airflow in the computing system, to infer the expected temperature exposure of the given memory device, e.g., relative to other memory devices and their locations… Thus, the characterization profile 116 can represent general temperature characteristics of memory devices in a given computing system, which can identify the airflow, cooling sources, and heat sources in the computing system.); wherein the apparatus stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the apparatus; run each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; select one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capture second thermal data of the apparatus during a second time period after the first time period; wherein the second thermal data includes a second command sequence; predict a thermal event of the apparatus based on the first and second thermal data and the selected thermal model; provide an operational adjustment option to generate proactive resilience to the thermal event; and operate according to the operational adjustment option ([0012] The allocation engine 120 can reduce energy by prioritizing page allocation to cooler memory devices, as characterized by the characterization engine 110. The allocation engine 120 can also schedule more requests to cooler memory devices to benefit from faster writes (in some example implementations, a scheduling engine/instructions can provide scheduling functionality).). Herein Muralimanohar discloses a memory controller containing circuitry for characterizing memory devices of the system including the thermal cooling and configuration of the memory, herein determined as analogous to the thermal solution and deployment setting. Subsequent to obtaining this characterization information, the system is able to operate the memory devices in response to the information in order to improve operational throughput. Muralimanohar does not explicitly disclose the characterization information including a first command sequence and the subsequent limitations involving “wherein the apparatus stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the apparatus; run each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; select one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capture second thermal data of the apparatus during a second time period after the first time period; wherein the second thermal data includes a second command sequence; predict a thermal event of the apparatus based on the first and second thermal data and the selected thermal model; provide an operational adjustment option to generate proactive resilience to the thermal event”. Regarding the first command sequence and the plurality of predefined models which are created in a test environment respective of different thermal solutions or deployment settings, McKiernan discloses in Paragraphs [0050] and [0071] “[0050] The test support models can be configured to mimic the behaviors of the corresponding subsystem during a test event (operational simulation). For example, the test support models can be generated to include the attributes and properties of the corresponding subsystems such that each of the test support models responds to a test event in the same or substantially similar manner that the corresponding subsystem would in a real operational scenario. In one embodiment, a test support model generated for a temperature sensor can be configured to generate simulated temperature data that matches the temperature data the temperature sensor would measure and collect under the same or substantially similar conditions. In an embodiment, the test events can correspond to real-time operational scenarios. [0071] One or more data files can be generated by the test module 206 or provided to the test module 206 to initialize the test support models with attributes of the respective subsystems they are to represent. For example, attributes of the different subsystems may be preloaded into a test support model based at least in part on the testing scenario. During each test event, each subsystem of the embedded controller 202 may be expected to respond in a predetermined way and/or generate measurements and data in an expected way to simulate a realistic operation of the embedded controller 202.” Herein McKiernan discloses generating and storing a plurality of test models which mimic real operational factors in order to project execution parameters. This enables the system to establish expected performance in view of modeled realistic operation which includes workloads comprising command sequences. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate and store these models for testing configurations in order to provide a plurality of potential expected outcomes based on characteristics as monitored in Muralimanohar (McKiernan [0072]). McKiernan does not explicitly address running the plurality of models using the first thermal data to select a model whose output best fits the first thermal data and capturing second thermal data to then predict a thermal event and provide an operational adjustment. Regarding running the plurality of models using data to select a model with output that best fits the thermal data, Bill discloses in Paragraph [0140] “FIG. 12 is a flow chart giving more detail of how a temperature profile can be selected in block 1104 of FIG. 6. At block 1202 a “trajectory” of the temperature over time is determined by interpolating between the measurement points or by fitting a curve to the measurement points... At block 1206, the received variable data is used to filter the temperature profiles to only those with metadata compatible with the variable data. For example, if the variable data indicates that brake cooling fans are on, then only temperature profiles with metadata indicating that the brake cooling fans were on are considered for selection. Then, at block 1208, a response profile is determined from the filtered temperature profiles is determined or selected that best fits the measured data. For example, pattern matching techniques can be used to determine which profile has the best fit.” Herein Bill discloses performing the process of checking input variables against a plurality of temperature profiles which are compatible with the input variables to the determine which profile is best fit for the input variables. Specifically, Bill identifies that the determine profile best fits the data based on pattern matching and as best understood of the claim language, this is determined to meet the requirements of the limitation. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the model that best fits the data as performed in Bill in the context of McKiernan and Muralimanohar in order to determine the most accurate model which reflects the current condition of the memory system. Bill does not explicitly address predicting a thermal event based on captured second thermal data and the first thermal data to then provide an operational adjustment. Regarding this aspect of the limitation, Amin-Shahidi discloses in Paragraphs [0035] and [0060] “[0035] At operation 205, the data storage system 110 models thermal characteristics of a data storage system based on the inlet air characteristics of the enclosure, the performance characteristics and thermal constraints of the data storage devices, and the constraints of a workload. The model of the thermal characteristics can include a thermal profile of the enclosure including air temperatures across the enclosure as well as predicted temperatures or thermal profiles for each component of the data storage system 110 (e.g., data storage devices 120a-120n). The thermal profiles associated with data storage devices 120a-120n can vary over time, such as due to ambient temperature changes outside of enclosure 113, changes in operational workloads of individual data storage devices, or changes in ventilation or climate controls associated with data storage system 110, among other fluctuations. [0060] FIG. 4B illustrates an example predicted air temperature distribution 410B that occurs as a result of a nonuniform power/workload distribution 412B. The predicted air temperature distribution is modeled based on various thermal and air characteristics that are fed back to the system. As discussed, the distribution of the storage operations can be optimized based on the model to increase collective IOPS of the multiple data storage devices while maintaining surface temperature below a safe threshold for each of the multiple data storage devices. In the example of FIG. 4B, power/workload distribution 412B is determined to provide maximum IOPs while maintaining surface temperature below a safe threshold.” Herein Amin-Shahidi discloses determining and modifying operation of the memory device based on predictive thermal modeling in view of workload execution. This prediction includes the temperature meeting a threshold, determined as analogous to a thermal event, wherein the behavior of the memory is adjusted according to the workload execution, interpreted as analogous to the second command sequence, to optimize storage performance in view of the threshold. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the predictive thermal event steps as performed in Amin-Shahidi in view of Muralimanohar, McKiernan, and Bill in order to modify the performance of the memory to maximize collective IOPS (Amin-Shahidi [0036]). Muralimanohar, McKiernan, Bill, and Amin-Shahidi are analogous art because they are from the same field of endeavor of managing storage system configurations.
Regarding claim 3, Muralimanohar further discloses the memory apparatus of claim 1, wherein the plurality of operational adjustment options include one or more of a group of operational adjustment options including: throttling operations of the memory array; deferring writes to the memory array; controlled shutdown of the apparatus; and changing a mode of operation of the host device ([0030] Power gating policies 368 affect how frequently, regarding memory cycles, a read request is performed versus putting the memory in sleep/power-down mode (and the associated penalty of waking up the memory from sleep). Aggressive power gating policies 368 can reduce the delta of putting memory into sleep mode, to consume less power (less leakage current) and reduce temperatures.). Herein Muralimanohar discloses implementing power gating policies controlling the function of the storage device which includes throttling and shutdown.
Regarding claim 6, Muralimanohar further discloses the memory apparatus of claim 1, further comprising a plurality of thermal sensors coupled to the controller and configured to provide thermal data related to operation of the apparatus to the controller ([0011] Temperature sensors in communication with controller). Herein Muralimanohar discloses temperature sensors present within the storage system to measure the current operating temperature to use as part of the storage device reconfiguration in combination with the workload and model.
Regarding claim 14, Muralimanohar discloses, in the italicized portions, a method, comprising: capturing first thermal data of a deployed memory device during a first time period; wherein the first thermal data includes a first command sequence, a thermal solution of the deployed memory device, and a deployment setting of the deployed memory device ([0010-11]); wherein the memory device stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the memory device; running each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; selecting one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capturing second thermal data of the deployed memory device during a second time period after the first time period; wherein the second thermal data includes a second command sequence; predicting future thermal events for the deployed memory device based on the first and second thermal data and the selected thermal model; and proactively adjusting operation of the deployed memory device based on the predicted future thermal events. Herein Muralimanohar discloses a memory controller containing circuitry for characterizing memory devices of the system including the thermal cooling and configuration of the memory, herein determined as analogous to the thermal solution and deployment setting. Muralimanohar does not explicitly disclose the characterization information including a first command sequence and the subsequent limitations involving “wherein the memory device stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the memory device; running each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; selecting one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capturing second thermal data of the deployed memory device during a second time period after the first time period; wherein the second thermal data includes a second command sequence; predicting future thermal events for the deployed memory device based on the first and second thermal data and the selected thermal model”. Regarding the first command sequence and the plurality of predefined models which are created in a test environment respective of different thermal solutions or deployment settings, McKiernan discloses in Paragraphs [0050] and [0071] generating and storing a plurality of test models which mimic real operational factors in order to project execution parameters. This enables the system to establish expected performance in view of modeled realistic operation which includes workloads comprising command sequences. McKiernan does not explicitly address running the plurality of models using the first thermal data to select a model whose output best fits the first thermal data and capturing second thermal data to then predict a thermal event and provide an operational adjustment. Regarding running the plurality of models using data to select a model with output that best fits the thermal data, Bill discloses in Paragraph [0140] performing the process of checking input variables against a plurality of temperature profiles which are compatible with the input variables to the determine which profile is best fit for the input variables. Specifically, Bill identifies that the determine profile best fits the data based on pattern matching. Bill does not explicitly address predicting a thermal event based on captured second thermal data and the first thermal data to then provide an operational adjustment. Regarding this aspect of the limitation, Amin-Shahidi discloses in Paragraphs [0035] and [0060] determining and modifying operation of the memory device based on predictive thermal modeling in view of workload execution. This prediction includes the temperature meeting a threshold, determined as analogous to a thermal event, wherein the behavior of the memory is adjusted according to the workload execution, interpreted as analogous to the second command sequence, to optimize storage performance in view of the threshold. Claim 14 is rejected on a similar basis as claim 1.
Regarding claim 17, Bill further discloses the method of claim 14, wherein predicting future thermal events based on the selected thermal model comprises executing a linear or non-linear regression thermal model ([0088] Linear regression with least squares). Herein one of ordinary skill in the art may recognize the linear regression model as including linear and nonlinear forms based on the inputs and how the sub-models are adapted to each operation.
Regarding claim 19, Muralimanohar further discloses the method of claim 14, wherein proactively adjusting operation of the deployed memory device comprises reducing a quality of operation of the deployed memory device to prevent exceeding a thermal constraint of the deployed memory device ([0030] Reduced power consumption to reduce temperatures). Herein Muralimanohar discloses employing dynamic configuration to reduce temperature through reductions in performance in order to avoiding exceeding temperature thresholds.
Regarding claim 21, Bill further discloses the method of claim 14, wherein the first thermal data further includes first environmental data and/or first thermal sensor data; wherein the second thermal data further includes second environmental data and/or second thermal sensor data ([0140]). Herein Bill identifies that the temperature profile used to characterize the measured data is based on two different sets of data, one being the data used to generate the temperature profile and the other being the measured data being compared to in order to determine the profile of best fit.
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Muralimanohar in view of McKiernan and further in view of Bill and still in further view of Amin-Shahidi and Dunn (US 2017/0060442).
Regarding claim 2, Muralimanohar, McKiernan, Bill and Amin-Shahidi do not explicitly disclose the memory apparatus of claim 1, wherein the controller is configured to: provide a plurality of operational adjustment options to a host device external to the apparatus; receive an indication of a selected one of the operational adjustment options; and operate according to the selected operational adjustment option. Regarding this limitation, Dunn discloses in Paragraphs [0026] and [0032] “[0026] In some examples, service level interface 115 presents one or more service level options 133 to host system 140. Service level options 133 can be presented to host system 140 via a driver interface, software interface, user interfaces, console or text interface, API, or other interface. Host system 140 can select among the presented service level options 133, and select desired service level factors and desired values or levels for those factors. [0032] When storage controller 111 or service level interface 115 receive service level selections for more than one of data storage devices 120, such as for the entirety of data storage system 110, then the service level selections can be allocated or distributed over various ones of data storage devices 120 to achieve the service level selections.” Herein Dunn discloses the storage system providing to a host system a plurality of configuration options for the system to adjust operation in order to achieve the selected configuration. Furthermore, in Paragraph [0065], these changes are identified to address temperature, load, and other operational and environmental conditions. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the reconfiguration being performed in Muralimanohar, McKiernan, Bill and Amin-Shahidi to include host system input in order to configure the storage system to a desired performance (Dunn [0061]). Muralimanohar, McKiernan, Bill, Amin-Shahidi, and Dunn are analogous art because they are from the same field of endeavor of managing storage system configurations.
Regarding claim 5, Muralimanohar, McKiernan, Bill and Amin-Shahidi do not explicitly disclose the memory apparatus of claim 1, wherein the plurality of predefined thermal models are trained based on a library of reference workloads for memory arrays and based on different physical characteristics of different memory apparatuses. Regarding this limitation, Dunn discloses in Paragraph [0065] that each workload characterization value is based on established respective values and the process of adjusting the values is in response to different workload conditions. Specifically, respective device performance due to individual variation between devices and the predictive modeling may be adapted for each device based on the corresponding metrics of the storage device.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Muralimanohar in view of McKiernan and further in view of Bill and still in further view of Amin-Shahidi and Lee et al. (US 2023/0153014).
Regarding claim 4, Muralimanohar, McKiernan, Bill, and Amin-Shahidi do not explicitly disclose the memory apparatus of claim 3, wherein the second command sequence corresponds to a current mode of operation of the host device external to the apparatus; and wherein the host device is a vehicle. Regarding this aspect of the limitation, Lee discloses in Paragraphs [0198] and [0207] that the storage system operates within the vehicle 1001 and the storage device 200 is in communication with the ECUs of the vehicle. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to that the storage device configuration may be performed in the context of a vehicle as a host device. Muralimanohar, McKiernan, Bill, Amin-Shahidi, and Lee are analogous art because they are from the same field of endeavor of managing storage system configurations.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Muralimanohar in view of McKiernan and further in view of Bill and still in further view of Amin-Shahidi and Vaysman et al. (US 2023/0367378).
Regarding claim 20, McKiernan further discloses, in the italicized portions, the method of claim 14, wherein each of the plurality of predefined thermals models is created in the test environment and is further associated with a different command sequence for the memory device ([0050] and [0071]); and wherein predicting network future thermal events based on the selected thermal model comprises executing a recurrent neural network, a long short-term memory network, or a transformer model. Herein McKiernan discloses the creation of models in a test environment. Muralimanohar, McKiernan, Bill and Amin-Shahidi does not explicitly address the execution of a particular network or transformer model. Regarding this aspect of the limitation, Vaysman discloses in Paragraphs [0114] and [0207] the sub-models are tuned to corresponding operations and the models are trained via a neural network. One ordinary skill in the art may recognize the neural network may be of the corresponding form as claimed as the limitation does not include any distinguishing detail regarding the individual forms of the model as being a distinct product of each network or model. Muralimanohar, McKiernan, Bill, Amin-Shahidi, and Vaysman are analogous art because they are from the same field of endeavor of managing storage system configurations.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Muralimanohar in view of McKiernan and further in view of Bill and still in further view of Amin-Shahidi and Lee and Jakobsson (US 2020/0159960).
Regarding claim 7, Muralimanohar, McKiernan, Bill, and Amin-Shahidi do not explicitly disclose the memory apparatus of claim 6, wherein the command sequence is received from a vehicle electronic control unit (ECU) that is coupled to the controller; and wherein the second thermal data further includes environmental data associated with a geolocation of the vehicle ECU. Regarding the command sequence received from a vehicle ECU limitation, Lee discloses in Paragraphs [0198] and [0207] that the storage system operates within the vehicle 1001 and the storage device 200 is in communication with the ECUs of the vehicle. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to that the storage device configuration may be performed in the context of a vehicle as a host device. Lee does not address the controller receiving environmental data. Regarding this aspect of the limitation Jakobsson discloses in Paragraphs [0286] and [0378] that temperature sensor information associated with a vehicle GPS can be used to enable predictive temperature control as requested. In the context of Muralimanohar, McKiernan, Bill, Amin-Shahidi, and Lee wherein storage system operation is managed through predictive temperature modeling, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the techniques employed in Jakobsson for addressing temperature control may be incorporated into the context of the previous references to include taking into account the environmental information associated with the geolocation of the vehicle ECU to achieve the desired temperature settings (Jakobsson [0286]). Muralimanohar, McKiernan, Bill, Amin-Shahidi, Lee, and Jakobsson are analogous art because they are from the same field of endeavor of managing storage system configurations.
Regarding claim 8, Lee and Jakobsson in combination further disclose the memory apparatus of claim 7, wherein the operational adjustment option includes an activation of a vehicle cooling mechanism (Jakobsson [0286]). Herein Jakobsson discloses controlling temperature by means of increasing or decreasing heating and cooling. In the context of Lee wherein the storage device is in communication with the vehicle ECU, one of ordinary skill in the art would recognize the vehicle cooling mechanism may be controlled to address the temperature.
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2023/0153014) in view of Muralimanohar and in further view of McKiernan and still further in view of Bill and Amin-Shahidi.
Regarding claim 9, Lee discloses, in the italicized portions, an apparatus, comprising: an electronic control unit (ECU) of a vehicle (Figure 17, ECUs 1100); a System-on-a-Chip (SoC) coupled to the ECU (Figure 1, Host controller 110 implemented as SoC), wherein the SoC further comprises: a plurality of thermal sensors; a memory array; and a controller coupled to the memory array and the plurality of thermal sensors (Temperature sensors 230 and 240, storage device 200 including NAND memory array, memory controller 210), wherein the controller is configured to: capture first thermal data of the apparatus during a first time period; wherein the first thermal data includes a first command sequence from the ECU, a thermal solution of the apparatus, and a deployment setting of the apparatus, and thermal data from the plurality of thermal sensors during operation of the SoC ([0126] temperature sensor 230 transmitting and receiving data to and from memory controller 210); wherein the SoC stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the apparatus; run each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; select one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capture second thermal data of the apparatus during a second time period after the first time period; wherein the second thermal data includes a second command sequence from the ECU; predict a thermal event of the SoC based on the first and second thermal data and the selected thermal model; provide an operational adjustment option to the ECU to generate proactive resilience to the thermal event; and wherein the ECU is configured to operate according to the operational adjustment option. Specifically, Lee identifies modeling the predicted temperature based on measured temperatures but Lee does not explicitly disclose the steps of capturing first thermal data, storing a plurality of thermal models created in a test environment, selecting a predefined thermal model of best fit based on the first thermal data, receiving a second command sequence, predicting a thermal event based on the second command sequence, thermal data and model, and providing an option to the ECU to generate proactive resilience to the thermal event and operating accordingly. Regarding the capturing of first thermal data and operating the ECU according to an operational adjustment, Muralimanohar discloses in Paragraphs [0010-12] a memory controller containing circuitry for characterizing memory devices of the system including the thermal cooling and configuration of the memory, herein determined as analogous to the thermal solution and deployment setting. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the data capturing as performed in Muralimanohar into the controller in Lee in order to perform the known technique in the art of gathering system data analytics. Muralimanohar does not explicitly disclose the characterization information including a first command sequence and the subsequent limitations involving “wherein the SoC stores a plurality of predefined thermal models; wherein each of the plurality of thermals models is created in a test environment and each is associated with a respective different thermal solution or respective different deployment setting for the apparatus; run each of the plurality of predefined thermal models using the first thermal data to generate respective outputs; select one of the plurality of predefined thermal models having the respective output that best fits the first thermal data; capture second thermal data of the apparatus during a second time period after the first time period; wherein the second thermal data includes a second command sequence from the ECU; predict a thermal event of the SoC based on the first and second thermal data and the selected thermal model; provide an operational adjustment option to the ECU to generate proactive resilience to the thermal event; and wherein the ECU is configured to operate according to the operational adjustment option”. Regarding the first command sequence and the plurality of predefined models which are created in a test environment respective of different thermal solutions or deployment settings, McKiernan discloses in Paragraphs [0050] and [0071] generating and storing a plurality of test models which mimic real operational factors in order to project execution parameters. This enables the system to establish expected performance in view of modeled realistic operation which includes workloads comprising command sequences. McKiernan does not explicitly address running the plurality of models using the first thermal data to select a model whose output best fits the first thermal data and capturing second thermal data to then predict a thermal event and provide an operational adjustment. Regarding running the plurality of models using data to select a model with output that best fits the thermal data, Bill discloses in Paragraph [0140] performing the process of checking input variables against a plurality of temperature profiles which are compatible with the input variables to the determine which profile is best fit for the input variables. Specifically, Bill identifies that the determine profile best fits the data based on pattern matching. Bill does not explicitly address predicting a thermal event based on captured second thermal data and the first thermal data to then provide an operational adjustment. Regarding this aspect of the limitation, Amin-Shahidi discloses in Paragraphs [0035] and [0060] determining and modifying operation of the memory device based on predictive thermal modeling in view of workload execution. This prediction includes the temperature meeting a threshold, determined as analogous to a thermal event, wherein the behavior of the memory is adjusted according to the workload execution, interpreted as analogous to the second command sequence, to optimize storage performance in view of the threshold. Lee, Muralimanohar, McKiernan, Bill, and Amin-Shahidi are analogous art because they are from the same field of endeavor of managing storage system configurations.
Regarding claim 11, Bill further discloses the apparatus of claim 9, wherein the plurality of thermal models include a linear regression model and a non-linear regression model ([0088] Linear regression with least squares). Herein one of ordinary skill in the art may recognize the linear regression model as including linear and nonlinear forms based on the inputs and how the sub-models are adapted to each operation.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Muralimanohar and in further view of McKiernan and still further in view of Bill and Amin-Shahidi and Dunn.
Regarding claim 10, Lee, Muralimanohar, McKiernan, Bill, and Amin-Shahidi do not explicitly disclose the apparatus of claim 9, wherein the plurality of predefined thermal models are trained based on a library of reference workloads for the SoC and based on different physical characteristics of different SoC implementations in different vehicles; and wherein the controller is configured to select the one of the plurality of predefined thermal models further based on physical characteristics of the SoC implementation in the vehicle. Regarding this limitation, Dunn discloses in Paragraph [0065] that each workload characterization value is based on established respective values and the process of adjusting the values is in response to different workload conditions. Specifically, respective device performance due to individual variation between devices and the predictive modeling may be adapted for each device based on the corresponding metrics of the storage device. In the context of Lee, this would include the behavior of the SoC in the vehicle as well.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Muralimanohar and in further view of McKiernan and still further in view of Bill and Amin-Shahidi and Vaysman.
Regarding claim 12, Lee, Muralimanohar, McKiernan, Bill, and Amin-Shahidi do not explicitly disclose the apparatus of claim 11, wherein the plurality of thermal models further include a recurrent neural network, a long short-term memory network, or a transformer model. Regarding this aspect of the limitation, Vaysman discloses in Paragraphs [0114] and [0207] the sub-models are tuned to corresponding operations and the models are trained via a neural network. One ordinary skill in the art may recognize the neural network may be of the corresponding form as claimed as the limitation does not include any distinguishing detail regarding the individual forms of the model as being a distinct product of each network or model.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Muralimanohar and in further view of McKiernan and still further in view of Bill and Amin-Shahidi and Ramic et al. (US 2020/0089487).
Regarding claim 13, Lee, Muralimanohar, McKiernan, Bill, and Amin-Shahidi do not explicitly disclose the apparatus of claim 9, wherein the controller is further configured to: retrieve, from a third-party server, environmental data associated with a geolocation of the vehicle ECU; and predict the thermal event of the SoC based on the first and second thermal data, the selected thermal model, and the environmental data. Lee does disclose in Paragraph [0204] that the ECU of the vehicle communicates with an external server. Regarding the limitation, Ramic discloses in Paragraphs [0020] and [0045] that externally provided and location-based temperature information may be transmitted to the processing system. In the context of Lee, Vaysman and Dunn wherein temperature information is used to predictively model the temperature of the storage device, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize third-party provided environmental data as part of the modeling to more accurately predict the temperature of the storage device. Lee, Muralimanohar, McKiernan, Bill, Amin-Shahidi, and Ramic are analogous art because they are from the same field of endeavor of managing storage system configurations.
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.
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/ALEXANDER YOON/
Examiner, Art Unit 2135
/JARED I RUTZ/Supervisory Patent Examiner, Art Unit 2135
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