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
Regarding the rejection of claims in the previous office action over prior art (35 U.S.C. 102 or 35 U.S.C. 103), Applicant’s arguments are directed towards amended claims of a new scope which have not been previously examined, and for which new grounds of rejection are provided below.
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.
Claims 1–18 rejected under 35 U.S.C. 103 over Cummings et al., US Pre-Grant Publication No. 2022/0036123 in view of Park et al., US Pre-Grant Publication No. 2022/0309314 (hereafter Park) and Bush, US Pre-Grant Publication No. 2023/0243543 (hereafter Bush).
Regarding claim 1 and analogous claim 10:
Cummings teaches:
“A system for dynamically adjusting neural network efficiency of a dynamic neural network running on a device, comprising”: Cummings, paragraph 0003, “The training and validation can be especially time consuming and resource intensive for larger ML architectures such as deep neural networks (DNNs) [neural network] and the like”; Cummings, paragraph 0008, “The present disclosure is related to techniques for optimizing artificial intelligence (AI) and/or machine learning (ML) models to reduce resource consumption while improving AI/ML model performance [dynamically adjusting neural network efficiency of a dynamic neural network running on a device]. In particular, the present disclosure provides a ML architecture search (MLAS) framework that involves generalized and/or hardware (HW)-aware ML architectures”; Cummings, paragraph 0115, “Additional examples of the presently described method, system, and device embodiments include the following, non-limiting implementations. Each of the following non-limiting examples may stand on its own or may be combined in any permutation or combination with any one or more of the other examples provided below or throughout the present disclosure.”
“a detector, arranged to detect a change of a status of the device, to generate a trigger signal”: Cummings, paragraph 0024, “The system constraints are values and/or thresholds used to determine one or more system state changes the end-user deems important to track and/or acceptable ranges/bounds of such system status (operational) changes. For example, the system constraints may indicate specific voltage or current load ranges/values of one or more HW components, memory utilization ranges/values, processor utilization ranges/values, power/energy levels, operating system (OS) statuses/indicators, application requirements to be met (or not met), and/or combination(s) thereof that should be used to trigger a new MLMS [a detector, arranged to detect a change of a status of the device, to generate a trigger signal]. Examples of the different types of parameters, metrics, and/or measures that may be used for the system constraints are discussed infra in section 1.2.”
“a signal generator, arranged to generate a control signal according to the trigger signal, to dynamically adjust the neural network efficiency of the dynamic neural network”: Cummings, paragraph 0024, “The system constraints are values and/or thresholds used to determine one or more system state changes the end-user deems important to track and/or acceptable ranges/bounds of such system status (operational) changes. For example, the system constraints may indicate specific voltage or current load ranges/values of one or more HW components, memory utilization ranges/values, processor utilization ranges/values, power/energy levels, operating system (OS) statuses/indicators, application requirements to be met (or not met), and/or combination(s) thereof that should be used to trigger a new MLMS [arranged to generate a control signal according to the trigger signal]”; Cummings, paragraph 0014, “The present disclosure provides an MLMS system that selects and interchanges ML models ( e.g., subnets) in an energy and communication efficient way while adapting the ML models ( e.g., subnets) to real time ( or near-real time) changes in system (HW platform) constraints [a signal generator, arranged to generate a control signal according to the trigger signal, to dynamically adjust the neural network efficiency of the dynamic neural network].”
(bold only) “wherein the detector is further arranged to detect a first temperature of the device at a first time and a second temperature of the device at a second time later than the first time, and determine a signed temperature difference by subtracting the first temperature from the second temperature”: Cummings, paragraph 0044, “The SSI 107 may include or indicate data about the operational conditions and/or hardware performance metrics of the HW platform (or individual HW components of the HW platform) such as, for example, temperature of individual components [wherein the detector is further arranged to detect a first temperature of the device], component load, processor performance, computational capacity, memory and/or storage utilization, amount of memory/storage free space, power source state (e.g., power consumption, voltage levels, current levels, etc.) and/or battery state (e.g., available power/energy, thermal data of the battery, etc.), OS and/or application parameters and requirements such as computational needs, input/output characteristics, and volume of exchanged data (upload or download); overload conditions experienced by an application or the HW platform itself; and/or the like.”
Cummings, paragraph 0017, “The MLMS system 100 achieves energy and communication efficient by using a similarity-based subnet selection process where a subnet is selected from a pool of subnets [wherein the signal generator is further arranged to switch the model architecture of the dynamic neural network between the multiple sub-networks according to the control signal] that has the most overlap in pre-trained parameters from the existing subnet to minimize memory write operation overhead.”
Cummings does not explicitly teach:
(bold only) “wherein the detector is further arranged to detect a first temperature of the device at a first time and a second temperature of the device at a second time later than the first time, and determine a signed temperature difference by subtracting the first temperature from the second temperature”
“wherein a predetermined range of the signed temperature difference has an upper bound and a lower bound”
“wherein the signal generator is further arranged to switch a model architecture of the dynamic neural network for decreasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being greater than the upper bound, and switch the model architecture of the dynamic neural network for increasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being less than the lower bound”
Park teaches:
(bold only) “wherein the detector is further arranged to detect a first temperature of the device at a first time and a second temperature of the device at a second time later than the first time”: Park, paragraph 0004, "In some aspects, the operating condition information may be at least one of the group of a temperature [temperature], a power consumption, an operating frequency, or a utilization of processing units" ; Park, paragraph 0048, "In some embodiments, the AI QoS manager 210 may be configured with any number and combination of algorithms, thresholds, look up tables, etc. for determining from the operating conditions whether to implement dynamic neural network quantization reconfiguration. For, example, the AI QoS manager 210 may compare a received operating condition to a threshold value for the operating condition. In response to the operating condition comparing unfavorably to the threshold value for the operating condition, such as by exceeding the threshold value, the AI QoS manager 210 may determine to implement dynamic neural network quantization reconfiguration. Such an unfavorable comparison may indicate to the AI QoS manager 210 that the operating condition increased constraint of the processing ability of the AI processor 124. In response to the operating condition comparing favorably to the threshold value for the operating condition, such as by falling short of the threshold value, the AI QoS manager 210 may determine to implement dynamic neural network quantization reconfiguration. Such a favorable comparison may indicate to the AI QoS manager 210 that the operating condition decreased constraint of the processing ability of the AI processor 124. In some embodiments, the AI QoS manager 210 may be configured to compare multiple received operating conditions to multiple thresholds for the operating conditions and determine to implement dynamic neural network quantization reconfiguration based on a combination of unfavorable and/or favorable comparison results. In some embodiments, the AI processor 124 may be configured with an algorithm to combine multiple received operating conditions and compare the result of the algorithm to a threshold. In some embodiments, the multiple received operating conditions may be of the same and/or different types. In some embodiments, the multiple received operating conditions may be for a specific time and/or over a time period [detect a first temperature of the device at a first time and a second temperature of the device at a second time later than the first time]."
“wherein a predetermined range of the signed temperature difference has an upper bound and a lower bound” and “wherein the signal generator is further arranged to switch a model architecture of the dynamic neural network for decreasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being greater than the upper bound, and switch the model architecture of the dynamic neural network for increasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being less than the lower bound”: Park, paragraph 0048, "In some embodiments, the AI QoS manager 210 may be configured with any number and combination of algorithms, thresholds, look up tables, etc. for determining from the operating conditions whether to implement dynamic neural network quantization reconfiguration [switch a model architecture of the dynamic neural network]. For, example, the AI QoS manager 210 may compare a received operating condition to a threshold value for the operating condition. In response to the operating condition comparing unfavorably to the threshold value for the operating condition, such as by exceeding the threshold value [an upper bound], the AI QoS manager 210 may determine to implement dynamic neural network quantization reconfiguration. Such an unfavorable comparison may indicate to the AI QoS manager 210 that the operating condition increased constraint of the processing ability of the AI processor 124 [for decreasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being greater than the upper bound]. In response to the operating condition comparing favorably to the threshold value for the operating condition, such as by falling short of the threshold value [a lower bound], the AI QoS manager 210 may determine to implement dynamic neural network quantization reconfiguration. Such a favorable comparison may indicate to the AI QoS manager 210 that the operating condition decreased constraint of the processing ability of the AI processor 124 [for increasing the neural network efficiency of the dynamic neural network in response to the signed temperature difference being less than the lower bound]. In some embodiments, the AI QoS manager 210 may be configured to compare multiple received operating conditions to multiple thresholds for the operating conditions and determine to implement dynamic neural network quantization reconfiguration based on a combination of unfavorable and/or favorable comparison results. In some embodiments, the AI processor 124 may be configured with an algorithm to combine multiple received operating conditions and compare the result of the algorithm to a threshold. In some embodiments, the multiple received operating conditions may be of the same and/or different types. In some embodiments, the multiple received operating conditions may be for a specific time and/or over a time period."
Park and Cummings are analogous arts as they are both related to scaling model systems according to changing conditions. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the temperature-changes scaling of Park with the teachings of Cummings to arrive at the present invention, in order to improve performance, as stated in Park, paragraph 0096, “Reducing the AI processor bit width 610 in conjunction with reducing the AI processor operating frequency 606b may allow for the reduction in the AI processor operating frequency 606b to be less than the reduction in the AI processor operating frequency 606a when reducing the AI processor operating frequency 606a alone. Reducing the AI processor bit width 610 the AI processor operating frequency 606b may yield similar benefits in terms of the AI processor temperature 602a, 602b as reducing the AI processor operating frequency 606a alone, but may also provide the benefit of greater AI processor operating frequency 606b, which may affect AI processor throughput.”
Bush teaches “and determine a signed temperature difference by subtracting the first temperature from the second temperature”: Bush, paragraph 0010, “The method may further comprise: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval as the current indoor temperature; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature [determine a signed temperature difference by subtracting the first temperature from the second temperature]; determining that the current temperature difference is less than the low temperature difference threshold; reducing the fan speed of the indoor fan by a second adjustment interval in response to the determining that the current temperature difference is less than the low temperature difference threshold; storing a third low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold; and storing a third high temperature difference as the high temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold. The third high temperature difference may be between the second low temperature difference and the first low temperature difference, and the third low temperature difference may be smaller than the second low temperature difference.”
Bush and Cummings as modified by Park are analogous arts as they are both related to adjusting device operations based on temperature changes. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the time-based temperature difference of Bush with the teachings of Cummings as modified by Park to arrive at the present invention, in order to adjust a device based on a rate of temperature change, as stated in Bush, paragraph 0010, “[…] reducing the fan speed of the indoor fan by a second adjustment interval in response to the determining that the current temperature difference is less than the low temperature difference threshold.”
Regarding claim 2 and analogous claim 11:
Cummings as modified by Park and Bush teaches “[t]he system of claim 1.”
Cummings further teaches “wherein the dynamic neural network comprises multiple sub-networks”: Cummings, paragraph 0022, “This supernet 130 may include parameters and/or weights that do not significantly contribute to the prediction and/or inference determination, and these parameters and/or weights contribute to the supernet's overall computational complexity and density. Therefore, the supernet 130 contains one or more smaller subnets (e.g., the subnets 135 in the subnet pool 147 in FIG. 1) that, when trained in isolation, can match and/or offer trade-offs in various objectives and/or performance metrics such as accuracy and/or latency of the original supernet 130 when trained for the same number of iterations or epochs [wherein the dynamic neural network comprises multiple sub-networks].”
Regarding claim 3 and analogous claim 12:
Cummings as modified by Park and Bush teaches “[t]he system of claim 2.”
Cummings further teaches “wherein the signal generator is further arranged to switch the model architecture of the dynamic neural network between the multiple sub-networks according to the control signal”: Cummings, paragraph 0017, “The MLMS system 100 achieves energy and communication efficient by using a similarity-based subnet selection process where a subnet is selected from a pool of subnets [wherein the signal generator is further arranged to switch the model architecture of the dynamic neural network between the multiple sub-networks according to the control signal] that has the most overlap in pre-trained parameters from the existing subnet to minimize memory write operation overhead.”
Regarding claim 4 and analogous claim 13:
Cummings as modified by Park and Bush teaches “[t]he system of claim 1.”
Cummings further teaches (bold only) “wherein the detector is further arranged to determine whether the signed temperature difference is outside the predetermined range, and in response to the signed temperature difference being outside the predetermined range, the detector generates the trigger signal”: Cummings, paragraph 0024, “The system constraints are values and/or thresholds used to determine one or more system state changes the end-user deems important to track and/or acceptable ranges/bounds of such system status (operational) changes. For example, the system constraints may indicate specific voltage or current load ranges/values of one or more HW components [the detector is further arranged to determine whether … is outside the predetermined range], memory utilization ranges/values, processor utilization ranges/values, power/energy levels, operating system (OS) statuses/indicators, application requirements to be met (or not met), and/or combination(s) thereof that should be used to trigger a new MLMS [in response to … being outside the predetermined range, the detector generates the trigger signal]. Examples of the different types of parameters, metrics, and/or measures that may be used for the system constraints are discussed infra in section 1.2.”
Bush further teaches (bold only) “wherein the detector is further arranged to determine whether the signed temperature difference is outside the predetermined range, and in response to the signed temperature difference being outside the predetermined range, the detector generates the trigger signal”: Bush, paragraph 0010, “The method may further comprise: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval as the current indoor temperature; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature [signed temperature difference]; determining that the current temperature difference is less than the low temperature difference threshold; reducing the fan speed of the indoor fan by a second adjustment interval in response to the determining that the current temperature difference is less than the low temperature difference threshold; storing a third low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold; and storing a third high temperature difference as the high temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold. The third high temperature difference may be between the second low temperature difference and the first low temperature difference, and the third low temperature difference may be smaller than the second low temperature difference.”
Bush and Cummings as modified by Park are combinable for the rationale given under claim 1.
Regarding claim 6 and analogous claim 15:
Cummings as modified by Park and Bush teaches “[t]he system of claim 1.”
Bush further teaches “wherein the second temperature is higher than the first temperature, and the signed temperature difference is greater than the upper bound”: Bush, paragraph 0027, “The instructions may further implement: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature; determining that the current temperature difference is greater than the high temperature difference threshold; increasing the fan speed of the indoor fan by the first adjustment interval in response to the determining that the current temperature difference is greater than the high temperature difference threshold; and storing the first low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is greater than the high temperature difference threshold [wherein the second temperature is higher than the first temperature, and the signed temperature difference is greater than the upper bound].”
Bush and Cummings as modified by Park are combinable for the rationale given under claim 1.
Regarding claim 7 and analogous claim 16:
Cummings as modified by Park and Bush teaches “[t]he system of claim 6.”
Cummings further teaches:
“wherein the dynamic neural network comprises multiple sub-networks, the multiple sub-networks comprise a first sub-network and a second sub-network the first sub-network is current model architecture of the dynamic neural network, and neural network efficiency of the first sub-network is higher than neural network efficiency of the second sub-network”: Cummings, paragraph 0022, “This supernet 130 may include parameters and/or weights that do not significantly contribute to the prediction and/or inference determination, and these parameters and/or weights contribute to the supernet's overall computational complexity and density. Therefore, the supernet 130 contains one or more smaller subnets [wherein the dynamic neural network comprises multiple sub-networks] (e.g., the subnets 135 in the subnet pool 147 in FIG. 1) that, when trained in isolation, can match and/or offer trade-offs in various objectives and/or performance metrics such as accuracy and/or latency of the original supernet 130 when trained for the same number of iterations or epochs”; Cummings, paragraph 0063, “When the constraint C is met, then at line 6 the subnet selector 141 determines the performance of individual candidate subnets 340 (‘replacement subnet fj’) and the performance of the deployed subnet fd. At line 6, if a difference between the performance of the deployed subnet fd and the performance of the potential replacement subnet fj is within a certain alpha boundary (performance difference margin a), then that replacement subnet fj is deemed to be a suitable replacement subnet fj for the deployed subnet fd. This is done by the first argument in line 6 (e.g., ‘|nj-nd|<= a’) [showing that the efficiency of the replacement sub-network could be lower than the sub-network it is replacing, hence neural network efficiency of the first sub-network is higher than neural network efficiency of the second sub-network].”
“the signal generator switches model architecture of the dynamic neural network from the first sub-network to the second sub-network according to the control signal”: Cummings, paragraph 0024, “The system constraints are values and/or thresholds used to determine one or more system state changes the end-user deems important to track and/or acceptable ranges/bounds of such system status (operational) changes. For example, the system constraints may indicate specific voltage or current load ranges/values of one or more HW components, memory utilization ranges/values, processor utilization ranges/values, power/energy levels, operating system (OS) statuses/indicators, application requirements to be met (or not met), and/or combination(s) thereof that should be used to trigger a new MLMS”; Cummings, paragraph 0014, “The present disclosure provides an MLMS system that selects and interchanges ML models ( e.g., subnets) in an energy and communication efficient way while adapting the ML models ( e.g., subnets) to real time ( or near-real time) changes in system (HW platform) constraints [the signal generator switches model architecture of the dynamic neural network from the first sub-network to the second sub-network according to the control signal].”
Bush further teaches “in response to the signed temperature difference being greater than the upper bound”: Bush, paragraph 0027, “The instructions may further implement: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature; determining that the current temperature difference is greater than the high temperature difference threshold; increasing the fan speed of the indoor fan by the first adjustment interval in response to the determining that the current temperature difference is greater than the high temperature difference threshold; and storing the first low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is greater than the high temperature difference threshold [in response to the signed temperature difference being greater than the upper bound].”
Bush and Cummings as modified by Park are combinable for the rationale given under claim 1.
Regarding claim 8 and analogous claim 17:
Cummings as modified by Park and Bush teaches “[t]he system of claim 1.”
Bush further teaches “wherein the second temperature is lower than the first temperature, and the signed temperature difference is less than the lower bound”: Bush, paragraph 0010, “The method may further comprise: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval as the current indoor temperature; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature; determining that the current temperature difference is less than the low temperature difference threshold; reducing the fan speed of the indoor fan by a second adjustment interval in response to the determining that the current temperature difference is less than the low temperature difference threshold [wherein the second temperature is lower than the first temperature, and the signed temperature difference is less than the lower bound]; storing a third low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold; and storing a third high temperature difference as the high temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold. The third high temperature difference may be between the second low temperature difference and the first low temperature difference, and the third low temperature difference may be smaller than the second low temperature difference.”
Bush and Cummings as modified by Park are combinable for the rationale given under claim 1.
Regarding claim 9 and analogous claim 18:
Cummings as modified by Park and Bush teaches “[t]he system of claim 8.”
Cummings further teaches:
“wherein the multiple sub-networks comprise a first sub-network and a second sub-network, the first sub-network is current model architecture of the dynamic neural network, and neural network efficiency of the first sub-network is lower than neural network efficiency of the second sub-network”: Cummings, paragraph 0022, “This supernet 130 may include parameters and/or weights that do not significantly contribute to the prediction and/or inference determination, and these parameters and/or weights contribute to the supernet's overall computational complexity and density. Therefore, the supernet 130 contains one or more smaller subnets [wherein the multiple sub-networks comprise a first sub-network and a second sub-network] (e.g., the subnets 135 in the subnet pool 147 in FIG. 1) that, when trained in isolation, can match and/or offer trade-offs in various objectives and/or performance metrics such as accuracy and/or latency of the original supernet 130 when trained for the same number of iterations or epochs”; Cummings, paragraph 0063, “When the constraint C is met, then at line 6 the subnet selector 141 determines the performance of individual candidate subnets 340 (‘replacement subnet fj’) and the performance of the deployed subnet fd. At line 6, if a difference between the performance of the deployed subnet fd and the performance of the potential replacement subnet fj is within a certain alpha boundary (performance difference margin a), then that replacement subnet fj is deemed to be a suitable replacement subnet fj for the deployed subnet fd. This is done by the first argument in line 6 (e.g., ‘|nj-nd|<= a’) [showing that the efficiency of the replacement sub-network could be higher than the sub-network it is replacing, hence the first sub-network is current model architecture of the dynamic neural network, and neural network efficiency of the first sub-network is lower than neural network efficiency of the second sub-network].”
“the signal generator switches the model architecture of the dynamic neural network from the first sub-network to the second sub-network according to the control signal”: Cummings, paragraph 0024, “The system constraints are values and/or thresholds used to determine one or more system state changes the end-user deems important to track and/or acceptable ranges/bounds of such system status (operational) changes. For example, the system constraints may indicate specific voltage or current load ranges/values of one or more HW components, memory utilization ranges/values, processor utilization ranges/values, power/energy levels, operating system (OS) statuses/indicators, application requirements to be met (or not met), and/or combination(s) thereof that should be used to trigger a new MLMS”; Cummings, paragraph 0014, “The present disclosure provides an MLMS system that selects and interchanges ML models ( e.g., subnets) in an energy and communication efficient way while adapting the ML models ( e.g., subnets) to real time ( or near-real time) changes in system (HW platform) constraints [the signal generator switches the model architecture of the dynamic neural network from the first sub-network to the second sub-network according to the control signal].”
Bush further teaches “in response to the signed temperature difference being less than the lower bound”: Bush, paragraph 0010, “The method may further comprise: measuring the indoor temperature a delay time after the reducing of the fan speed of the indoor fan by the first adjustment interval as the current indoor temperature; subtracting the set point temperature from the current indoor temperature to determine the current temperature difference between the current indoor temperature and the set point temperature; determining that the current temperature difference is less than the low temperature difference threshold; reducing the fan speed of the indoor fan by a second adjustment interval in response to the determining that the current temperature difference is less than the low temperature difference threshold [in response to the signed temperature difference being less than the lower bound]; storing a third low temperature difference as the low temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold; and storing a third high temperature difference as the high temperature difference threshold in response to the determining that the current temperature difference is less than the low temperature difference threshold. The third high temperature difference may be between the second low temperature difference and the first low temperature difference, and the third low temperature difference may be smaller than the second low temperature difference.”
Bush and Cummings as modified by Park are combinable for the rationale given under claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hagihara et al., US Pre-Grant Publication No. 2019/0354892, discloses a device that selects an appropriate machine-learning model based on a change in ambient temperature.
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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/VAS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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