DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 17/843,811, was filed on June 17, 2022, and does not claim foreign priority or domestic benefit to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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 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.
Status of the Application
This Final Office Action is in response to Applicant’s communication of Oct. 29, 2025.
Claims 1-11 and 13-20 are pending, of which claims 1, 15, and 18 are independent.
In the most recent amendment, claims 1, 13-15, and 18 have been amended, and claim 12 has been cancelled.
All pending claims have been examined on the merits.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-11, 13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over US-2014/0033186-A1 to Bonanno et al. (“Bonanno”. Eff. Filed on Aug. 20, 2008. Published on Jan. 30, 2014) in view of Google Patents English Language Translation of DE 102021100404 A1 to Unger et al. (“Unger”. Filed on Jan. 12, 2021. Published on July 14, 2022).
In regards to claim 1:
1. A computing apparatus, comprising:
a processor circuit and a memory; and
instructions encoded within the memory to instruct the processor circuit to:
receive a stored dependency graph for a first … configuration for a plurality of compute nodes;
(See Bonanno, para. [0016]: “FIG. 2 is a flow diagram illustrating a process for optimizing a build order of component source modules 26. In the exemplary embodiment, the process is performed by the build service 20, with or without the aid of additional applications. The process may begin by creating a dependency graph (block 200). In one embodiment, a global reverse dependency graph is created by merging dependency files associated with the component source modules 26 and reversing the directions of the edges in the dependency graph.”)
receive a second … configuration comprising a modification to the first … configuration;
iteratively model the second … configuration comprising adjusting one or more of a plurality of operational parameters for a model of the second … configuration, and
selecting an optimum configuration of the plurality of operational parameters using the stored dependency graph; and
(See Bonanno, para. [0005]: “Methods and systems for optimizing a build order of component source modules comprises creating a dependency graph based on dependency information. Historical build information associated with previous build failures is then used to calculate relative failure factors for paths of the dependency graph; and the relative failure factors are used to determine an order of traversal of the dependency graph during a build process in which component binary modules are built from the component source modules.”)
(See Bonanno, para. [0017]: “In one embodiment the process may begin by polling the source code management repository 22 for any changes in dependency information in the component source modules 26. In one embodiment, both the source code management repository 22 and/or the binary repository 24 may be polled for changes. In one embodiment, polling may be initiated manually whereby the polling is initiated by a developer via a client computer 18. In another embodiment, polling may be initiated automatically. Automatic polling may be triggered by an expiration of a configured time interval. If no changes are detected, then the polling may be rescheduled for a later time. A continuous integration build server, such as Cruise Control™ or Anthill™ can be used to poll the source code management repository 22 and/or the binary repository 24 for changes. Alternatively, a time-based scheduling service, such as “cron” of UNIX-like operating systems, could be used. In response to detecting changes in the dependency information, the dependency graph may be created based on the dependency information.”)
However, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the “hardware configuration” recited in the following features:
receive a stored dependency graph for a first hardware configuration for a plurality of compute nodes;
receive a second hardware configuration comprising a modification to the first hardware configuration;
iteratively model the second hardware configuration comprising adjusting one or more of a plurality of operational parameters for a model of the second hardware configuration, and
selecting an optimum configuration of the plurality of operational parameters using the stored dependency graph; and
Also, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the following features:
cause the optimum configuration to be implemented in a real-world embodiment of the second hardware configuration to optimize resource allocation,
wherein the first hardware configuration and second hardware configuration are for an autonomous vehicle (AV) controller.
All of the above are taught by Unger:
(See Unger, para. [0039]: “A further aspect of the present invention is an assistance device, in particular for a motor vehicle, which includes an artificial neural network in a network configuration determined by means of the method according to the invention and hardware for executing the neural network. The network configuration is matched or adapted to the hardware or its hardware configuration with regard to the specified optimization criterion. In other words, the assistance device according to the invention is at least partially designed using the method according to the invention. Depending on the design and training of the neural network, the assistance device according to the invention can be set up or used for different tasks or purposes. This can include, for example, autonomous or semi-autonomous driving tasks or the autonomous or semi-autonomous control of vehicle functions. However, other possible uses in other technical fields are also possible. The assistance device according to the invention can also include a data interface, for example an input interface and an output interface, a data memory, a processor device and/or the like. In particular, the data memory and the processor device can form the hardware for executing the neural network or be part of it. The neural network can be in the form of software or a computer program that is stored in the data memory and can be executed by the processor device. The processor device can, for example, be or comprise a microchip, a microcontroller, a microprocessor and/or the like. In particular for mobile applications, for example in the vehicle sector, the assistance device according to the invention or its hardware can be designed as an embedded control device, ie as a so-called embedded controller. The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the method for “Using build history information to optimize a software build process”, as taught by Bonanno, with the method for “determining an optimized compressed network configuration, assistance device and motor vehicle”, as further taught by Unger, because Unger’s para. [0039] states:
“The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”
In regards to claim 2:
2. (Original) The computing apparatus of claim 1, wherein the plurality of operational parameters include access to shared resources.
(See Unger, para. [0033]: “A calculation type of the hardware can be dimensioned on a fine level of abstraction. Here, specific values or designs with regard to quantization levels, a number of calculation units, a size of register memories, data shifts that are supported, and/or the like can be determined, ie specified and/or selected. Within the scope of the present optimization method, an optimized assignment between individual data processing tasks and the hardware resources used for them, for example based on a previously determined or selected scheduling or a scheduling technique, can be optimized at this fine level of abstraction in order to enable or provide an estimate that is as accurate as possible, which advantages can be achieved by means of a specific hardware configuration for a specific network configuration or a specific quantization strategy.”)
The Examiner interprets that this disclosure implies that the hardware resources are shared for the “individual data processing tasks”, based on the scheduling technique.
In regards to claim 3:
3. (Original) The computing apparatus of claim 1, wherein a majority of the plurality of compute nodes are to operate in parallel with one another.
(See Unger, para. [0036]: “In a further possible embodiment of the present invention, the hardware configuration provides at least one vectorized hardware accelerator or includes such a hardware accelerator. For this purpose, as part of the hardware model, an aligned SIMD-MAC (Single Instruction Multiple Data Multiply Accumulate Operations Unit) unit can be modeled, which has a predetermined common maximum bit length, which is used for both the weights and the activations is applicable. The use of a vectorized hardware accelerator can represent a particularly reliable option for modeling suitable virtual hardware that is suitable in many applications. In addition, this can result in optimization or acceleration options through data level parallelization at the level of individual calculation units.”)
In regards to claim 4:
4. (Original) The computing apparatus of claim 1, wherein the plurality of compute nodes comprise a first compute node with a data dependency on a second compute node, wherein the first compute node and second compute node are to operate in parallel with one another.
(See Unger, para. [0036]: “In a further possible embodiment of the present invention, the hardware configuration provides at least one vectorized hardware accelerator or includes such a hardware accelerator. For this purpose, as part of the hardware model, an aligned SIMD-MAC (Single Instruction Multiple Data Multiply Accumulate Operations Unit) unit can be modeled, which has a predetermined common maximum bit length, which is used for both the weights and the activations is applicable. The use of a vectorized hardware accelerator can represent a particularly reliable option for modeling suitable virtual hardware that is suitable in many applications. In addition, this can result in optimization or acceleration options through data level parallelization at the level of individual calculation units.”)
In regards to claim 5:
5. (Original) The computing apparatus of claim 4, wherein the first compute node is to use latest available data from the second compute node.
(See Bonanno, para. [0017]: “In one embodiment the process may begin by polling the source code management repository 22 for any changes in dependency information in the component source modules 26. In one embodiment, both the source code management repository 22 and/or the binary repository 24 may be polled for changes. In one embodiment, polling may be initiated manually whereby the polling is initiated by a developer via a client computer 18. In another embodiment, polling may be initiated automatically. Automatic polling may be triggered by an expiration of a configured time interval. If no changes are detected, then the polling may be rescheduled for a later time. A continuous integration build server, such as Cruise Control™ or Anthill™ can be used to poll the source code management repository 22 and/or the binary repository 24 for changes. Alternatively, a time-based scheduling service, such as “cron” of UNIX-like operating systems, could be used. In response to detecting changes in the dependency information, the dependency graph may be created based on the dependency information.”)
In regards to claim 6:
6. (Original) The computing apparatus of claim 1, wherein the model is a machine learning model.
(See Unger, para. [0039]: “A further aspect of the present invention is an assistance device, in particular for a motor vehicle, which includes an artificial neural network in a network configuration determined by means of the method according to the invention and hardware for executing the neural network. The network configuration is matched or adapted to the hardware or its hardware configuration with regard to the specified optimization criterion. In other words, the assistance device according to the invention is at least partially designed using the method according to the invention. Depending on the design and training of the neural network, the assistance device according to the invention can be set up or used for different tasks or purposes. This can include, for example, autonomous or semi-autonomous driving tasks or the autonomous or semi-autonomous control of vehicle functions. However, other possible uses in other technical fields are also possible. The assistance device according to the invention can also include a data interface, for example an input interface and an output interface, a data memory, a processor device and/or the like. In particular, the data memory and the processor device can form the hardware for executing the neural network or be part of it. The neural network can be in the form of software or a computer program that is stored in the data memory and can be executed by the processor device. The processor device can, for example, be or comprise a microchip, a microcontroller, a microprocessor and/or the like. In particular for mobile applications, for example in the vehicle sector, the assistance device according to the invention or its hardware can be designed as an embedded control device, ie as a so-called embedded controller. The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”)
The Examiner interprets that a neural network is a type of machine learning.
In regards to claim 7:
7. (Original) The computing apparatus of claim 1, wherein the model is a statistical model.
(See Unger, para. [0044]: “Artificial neural networks, in particular convolutional neural networks (CNNs), represent the state of the art in many areas today, for example for what is known as computer vision, i.e. image processing or scene recognition based on images by a computer, for which Object recognition and object localization, for semantic segmentation and the like. In the course of the corresponding development, the complexity and thus the calculation and data processing effort, i.e. the resource requirements for the application or execution of corresponding neural networks, have also increased significantly. This means that it is no longer possible to use correspondingly complex neural networks, in particular for real-time applications, in all areas of use. Accordingly, there is an interest in and need for possibilities and methods for compressing complex neural networks in order to reduce their resource requirements, in particular with the least possible loss of accuracy.”)
The Examiner interprets that because neural networks use probabilities as the values for certain parameters, they can be called “a statistical model”.
In regards to claim 8:
8. (Original) The computing apparatus of claim 1, wherein iteratively modeling the second hardware configuration comprises iteratively modeling until a predicted performance for the second hardware configuration reaches a convergence.
(See Unger, para. [0052]: “The respective hardware model 30 or its respective hardware configuration 28 is defined iteratively in a top-down approach over a number of hardware abstraction levels, ie determined or selected. Specifically, these are a rough abstraction level 32, a medium abstraction level 34 and a fine abstraction level 36. A selection or specification of the properties of the hardware model 30 or the hardware modeled by it on the rough abstraction level 32 provides a corresponding rough specification 38. Accordingly, on the medium level of abstraction 34 a medium specification 40 is determined or selected. Finally, an even more detailed fine specification 42 is determined or selected on or in the fine abstraction level 36.”)
In regards to claim 9:
9. (Original) The computing apparatus of claim 1, wherein the optimum configuration is optimum to within a Pareto criterion.
(See Unger, para. [0061]: “In one possible development of the present invention, a Pareto optimization is used on each abstraction level in order to determine the at least one network configuration variation to be retained or at least one combination of a network configuration variation and a hardware configuration to be retained. In particular, a non-dominant sorting genetic algorithm, such as NSGA-II (English: non-dominated sorting genetic algorithm) are used. In other words, in each case at least one configuration is selected for use or as a basis for the possibly next finer level of abstraction or a final result, which lies on the Pareto front of all configurations tested. In this way, especially above the finest level of abstraction, the design space for the optimized network configuration to be ultimately determined or the optimized combination of network and hardware configuration to be ultimately determined can be kept open for a certain range of non-dominated solutions, i.e. configurations, before the design or the respective configuration is further detailed or specified at the next finer level of abstraction. Ultimately, this can lead to a better end result, ie better optimization and thus ultimately, for example, to lower resource requirements, lower latency and/or the like with at least substantially the same accuracy.”)
In regards to claim 10:
10. (Original) The computing apparatus of claim 1, wherein at least one of the plurality of compute nodes comprises a deep learning (DL) model, and adjusting the plurality of operational parameters comprises adjusting a number of intermediate layers in the DL model.
(See Unger, para. [0091]: “The assistance system 104 here includes an input interface 108 for acquiring sensor data recorded or provided by the sensor system 102 , for example surroundings or camera images. The assistance system 104 also includes a data memory 110 in which an operating program 112 is stored here. This operating program 112 includes a deep artificial neural network, the configuration of which is optimized for the hardware of the assistance system 104 using the HW-FlowQ approach, as described. To execute this operating program 112, the assistance system 104 also includes a processor 114. At least the data memory 110 and the processor 114 and their connection can correspond to the hardware optimized together with the neural network using the HW-FlowQ approach as described. This hardware can be used to process the data received or recorded via the input interface 108, which ultimately results in a corresponding control signal, for example, that is to say it can be generated. This control signal can then be output via an output interface 116 of assistance system 104, for example to vehicle component 106 to be controlled accordingly.”)
(See Unger, para. [0010]: “The individual network configuration variations—or their genotypes from the point of view of the genetic algorithm—are characterized according to the invention by their combination of respective bit lengths of weights and activations of their layers and their number of layers. The weights are adjustable parameters of the neural network, while the activations here within the neural network can be or describe intermediate results or intermediate states that arise during data processing. For example, an activation can be an output of a specific layer, which can then serve as an input for the next layer of the neural network either directly or after a processing step. The bit lengths, also referred to as bit widths, describe how many bits are used or available to represent the respective values, i.e. with what precision the corresponding values are specified. This is relevant because storing and processing, say, a 16-bit value can require significantly more resources than storing and processing, say, a 2-bit value. The individual network configuration variations can therefore differ from one another in that the weights and/or activations of one, several or all layers are different in each of two network configuration variations. This is also referred to as quantization or quantization level, so that the neural network can be quantized differently in the different network configuration variations. In this case, the bit lengths can be layer-specific or layer-specific, so that a specific network configuration variation can provide different bit lengths for different of its layers. This is particularly advantageous because different bit lengths at different points within the new ronal network can have varying degrees of influence on the accuracy and performance, i.e. a runtime and/or the energy requirement for a specific data processing.”)
In regards to claim 11:
11. (Original) The computing apparatus of claim 1, wherein at least one of the plurality of compute nodes comprises a deep learning (DL) model, and adjusting the plurality of operational parameters comprises adjusting a precision of the DL model.
(See Unger, para. [0091]: “The assistance system 104 here includes an input interface 108 for acquiring sensor data recorded or provided by the sensor system 102 , for example surroundings or camera images. The assistance system 104 also includes a data memory 110 in which an operating program 112 is stored here. This operating program 112 includes a deep artificial neural network, the configuration of which is optimized for the hardware of the assistance system 104 using the HW-FlowQ approach, as described. To execute this operating program 112, the assistance system 104 also includes a processor 114. At least the data memory 110 and the processor 114 and their connection can correspond to the hardware optimized together with the neural network using the HW-FlowQ approach as described. This hardware can be used to process the data received or recorded via the input interface 108, which ultimately results in a corresponding control signal, for example, that is to say it can be generated. This control signal can then be output via an output interface 116 of assistance system 104, for example to vehicle component 106 to be controlled accordingly.”)
(See Unger, para. [0010]: “The individual network configuration variations—or their genotypes from the point of view of the genetic algorithm—are characterized according to the invention by their combination of respective bit lengths of weights and activations of their layers and their number of layers. The weights are adjustable parameters of the neural network, while the activations here within the neural network can be or describe intermediate results or intermediate states that arise during data processing. For example, an activation can be an output of a specific layer, which can then serve as an input for the next layer of the neural network either directly or after a processing step. The bit lengths, also referred to as bit widths, describe how many bits are used or available to represent the respective values, i.e. with what precision the corresponding values are specified. This is relevant because storing and processing, say, a 16-bit value can require significantly more resources than storing and processing, say, a 2-bit value. The individual network configuration variations can therefore differ from one another in that the weights and/or activations of one, several or all layers are different in each of two network configuration variations. This is also referred to as quantization or quantization level, so that the neural network can be quantized differently in the different network configuration variations. In this case, the bit lengths can be layer-specific or layer-specific, so that a specific network configuration variation can provide different bit lengths for different of its layers. This is particularly advantageous because different bit lengths at different points within the new ronal network can have varying degrees of influence on the accuracy and performance, i.e. a runtime and/or the energy requirement for a specific data processing.”)
In regards to claim 12, it has been cancelled.
In regards to claim 13:
13. (Currently Amended) The computing apparatus of claim 1, wherein the instructions are to run onboard the AV controller.
(See Unger, para. [0039]: “A further aspect of the present invention is an assistance device, in particular for a motor vehicle, which includes an artificial neural network in a network configuration determined by means of the method according to the invention and hardware for executing the neural network. The network configuration is matched or adapted to the hardware or its hardware configuration with regard to the specified optimization criterion. In other words, the assistance device according to the invention is at least partially designed using the method according to the invention. Depending on the design and training of the neural network, the assistance device according to the invention can be set up or used for different tasks or purposes. This can include, for example, autonomous or semi-autonomous driving tasks or the autonomous or semi-autonomous control of vehicle functions. However, other possible uses in other technical fields are also possible. The assistance device according to the invention can also include a data interface, for example an input interface and an output interface, a data memory, a processor device and/or the like. In particular, the data memory and the processor device can form the hardware for executing the neural network or be part of it. The neural network can be in the form of software or a computer program that is stored in the data memory and can be executed by the processor device. The processor device can, for example, be or comprise a microchip, a microcontroller, a microprocessor and/or the like. In particular for mobile applications, for example in the vehicle sector, the assistance device according to the invention or its hardware can be designed as an embedded control device, ie as a so-called embedded controller. The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”)
In regards to claim 15:
15. One or more non-transitory computer-readable storage media having stored thereon executable instructions to:
find an optimum configuration for a set of operational parameters for a second … configuration being a modification of a first … configuration, based at least in part on a dependency graph for the first … configuration
(See Bonanno, para. [0016]: “FIG. 2 is a flow diagram illustrating a process for optimizing a build order of component source modules 26. In the exemplary embodiment, the process is performed by the build service 20, with or without the aid of additional applications. The process may begin by creating a dependency graph (block 200). In one embodiment, a global reverse dependency graph is created by merging dependency files associated with the component source modules 26 and reversing the directions of the edges in the dependency graph.”)
and a model of the second … configuration,
comprising iteratively adjusting a set of operational parameters for the model until a convergence is found;
(See Bonanno, para. [0017]: “In one embodiment the process may begin by polling the source code management repository 22 for any changes in dependency information in the component source modules 26. In one embodiment, both the source code management repository 22 and/or the binary repository 24 may be polled for changes. In one embodiment, polling may be initiated manually whereby the polling is initiated by a developer via a client computer 18. In another embodiment, polling may be initiated automatically. Automatic polling may be triggered by an expiration of a configured time interval. If no changes are detected, then the polling may be rescheduled for a later time. A continuous integration build server, such as Cruise Control™ or Anthill™ can be used to poll the source code management repository 22 and/or the binary repository 24 for changes. Alternatively, a time-based scheduling service, such as “cron” of UNIX-like operating systems, could be used. In response to detecting changes in the dependency information, the dependency graph may be created based on the dependency information.”)
wherein the first and second … configurations comprise a plurality of compute nodes with data dependencies on and resource contention with other compute nodes.
(See Bonanno, para. [0018]: “The historical build information 30 associated with previous build failures is used to calculate relative failure factors for paths of the dependency graph (block 202). The relative failure factors of the paths are then used to determine an order of traversal of the dependency graph during a build process in which component binary and application modules 28 are built from the component source modules 26 (block 204).”)
However, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the “hardware configuration” recited in the following features:
find an optimum configuration for a set of operational parameters for a second hardware configuration being a modification of a first hardware configuration, based at least in part on a dependency graph for the first hardware configuration
and a model of the second hardware configuration,
comprising iteratively adjusting a set of operational parameters for the model until a convergence is found; and
wherein the first and second hardware configurations comprise a plurality of compute nodes with data dependencies on and resource contention with other compute nodes, and
Also, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the following features:
causing the optimum configuration to be implemented in a real-world embodiment of the second hardware configuration to optimize resource allocation,
wherein the first hardware configuration and second hardware configuration are for an autonomous vehicle (AV) controller.
All of the above are taught by Unger:
(See Unger, para. [0039]: “A further aspect of the present invention is an assistance device, in particular for a motor vehicle, which includes an artificial neural network in a network configuration determined by means of the method according to the invention and hardware for executing the neural network. The network configuration is matched or adapted to the hardware or its hardware configuration with regard to the specified optimization criterion. In other words, the assistance device according to the invention is at least partially designed using the method according to the invention. Depending on the design and training of the neural network, the assistance device according to the invention can be set up or used for different tasks or purposes. This can include, for example, autonomous or semi-autonomous driving tasks or the autonomous or semi-autonomous control of vehicle functions. However, other possible uses in other technical fields are also possible. The assistance device according to the invention can also include a data interface, for example an input interface and an output interface, a data memory, a processor device and/or the like. In particular, the data memory and the processor device can form the hardware for executing the neural network or be part of it. The neural network can be in the form of software or a computer program that is stored in the data memory and can be executed by the processor device. The processor device can, for example, be or comprise a microchip, a microcontroller, a microprocessor and/or the like. In particular for mobile applications, for example in the vehicle sector, the assistance device according to the invention or its hardware can be designed as an embedded control device, ie as a so-called embedded controller. The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the method for “Using build history information to optimize a software build process”, as taught by Bonanno, with the method for “determining an optimized compressed network configuration, assistance device and motor vehicle”, as further taught by Unger, because Unger’s para. [0039] states:
“The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”
In regards to claim 16:
16. The one or more non-transitory computer-readable storage media of claim 15, wherein the executable instructions are further to send the optimum configuration to a real-world embodiment of the second … configuration.
(See Bonanno, para. [0035]: “In one embodiment, the build service 20 may publish the build results to a temporary working repository that is used by the hosting system 12 until all changes have been fully verified by completing all of the above defined phases/steps which ends in publication. Builds may resolve against a chain of binary repositories, where the temporary binary repository has greater precedence than the publicly available binary repository 24. This may allow recently built libraries to be resolved against before the libraries are published.”)
In regards to claim 17:
17. The one or more non-transitory computer-readable storage media of claim 15, wherein the set of operational parameters includes access to shared resources.
(See Bonanno, para. [0023]: “In one embodiment, when a component source module 26 is used in a build, historical build data is recorded for the source module and the developer of the source module in the historical build information 30. The list of historical build information may be readily changed and expanded. For example, information such as time of day may be stored to determine whether network access or other outside variables are affecting the build process. These pieces of information can be used to show a relative likelihood of failure.”)
In regards to claim 18:
18. A method of optimizing a vehicle controller after a … change, comprising:
receiving a first … configuration for the vehicle controller, the first … configuration for before the … change;
receiving a dependency graph for the first … configuration;
(See Bonanno, para. [0016]: “FIG. 2 is a flow diagram illustrating a process for optimizing a build order of component source modules 26. In the exemplary embodiment, the process is performed by the build service 20, with or without the aid of additional applications. The process may begin by creating a dependency graph (block 200). In one embodiment, a global reverse dependency graph is created by merging dependency files associated with the component source modules 26 and reversing the directions of the edges in the dependency graph.”)
receiving a second … configuration for the vehicle controller, the second … configuration for after the … change;
receiving a model of the second … configuration, including a plurality of operational parameters for a plurality of compute nodes having data and resource dependencies according to the dependency graph; and
(See Bonanno, para. [0017]: “In one embodiment the process may begin by polling the source code management repository 22 for any changes in dependency information in the component source modules 26. In one embodiment, both the source code management repository 22 and/or the binary repository 24 may be polled for changes. In one embodiment, polling may be initiated manually whereby the polling is initiated by a developer via a client computer 18. In another embodiment, polling may be initiated automatically. Automatic polling may be triggered by an expiration of a configured time interval. If no changes are detected, then the polling may be rescheduled for a later time. A continuous integration build server, such as Cruise Control™ or Anthill™ can be used to poll the source code management repository 22 and/or the binary repository 24 for changes. Alternatively, a time-based scheduling service, such as “cron” of UNIX-like operating systems, could be used. In response to detecting changes in the dependency information, the dependency graph may be created based on the dependency information.”)
iteratively simulating versions of the model with changes to the operational parameters until a convergence is reached.
(See Bonanno, para. [0018]: “The historical build information 30 associated with previous build failures is used to calculate relative failure factors for paths of the dependency graph (block 202). The relative failure factors of the paths are then used to determine an order of traversal of the dependency graph during a build process in which component binary and application modules 28 are built from the component source modules 26 (block 204).”)
However, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the “hardware configuration” and “hardware change” recited in the following features:
receiving a first hardware configuration for the vehicle controller, the first hardware configuration for before the hardware change;
receiving a dependency graph for the first hardware configuration;
receiving a second hardware configuration for the vehicle controller, the second hardware configuration for after the hardware change;
receiving a model of the second hardware configuration, including a plurality of operational parameters for a plurality of compute nodes having data and resource dependencies according to the dependency graph; and
iteratively simulating versions of the model with changes to the operational parameters until a convergence is reached,
Also, under a conservative interpretation of Bonanno, it could be argued that Bonanno does not explicitly teach the following features:
causing the optimum configuration to be implemented in a real-world embodiment of the second hardware configuration to optimize resource allocation,
wherein the vehicle controller is an autonomous vehicle (AV) controller.
All of the above are taught by Unger:
(See Unger, para. [0039]: “A further aspect of the present invention is an assistance device, in particular for a motor vehicle, which includes an artificial neural network in a network configuration determined by means of the method according to the invention and hardware for executing the neural network. The network configuration is matched or adapted to the hardware or its hardware configuration with regard to the specified optimization criterion. In other words, the assistance device according to the invention is at least partially designed using the method according to the invention. Depending on the design and training of the neural network, the assistance device according to the invention can be set up or used for different tasks or purposes. This can include, for example, autonomous or semi-autonomous driving tasks or the autonomous or semi-autonomous control of vehicle functions. However, other possible uses in other technical fields are also possible. The assistance device according to the invention can also include a data interface, for example an input interface and an output interface, a data memory, a processor device and/or the like. In particular, the data memory and the processor device can form the hardware for executing the neural network or be part of it. The neural network can be in the form of software or a computer program that is stored in the data memory and can be executed by the processor device. The processor device can, for example, be or comprise a microchip, a microcontroller, a microprocessor and/or the like. In particular for mobile applications, for example in the vehicle sector, the assistance device according to the invention or its hardware can be designed as an embedded control device, ie as a so-called embedded controller. The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the method for “Using build history information to optimize a software build process”, as taught by Bonanno, with the method for “determining an optimized compressed network configuration, assistance device and motor vehicle”, as further taught by Unger, because Unger’s para. [0039] states:
“The optimization of the network configuration of the neural network described in connection with the method according to the invention, in particular in combination with the hardware configuration of the hardware, can be particularly advantageous for such an application, since there are typically particularly high requirements for accuracy and the available resources are high are limited.”
In regards to claim 19:
19. (Original) The method of claim 18, wherein the plurality of compute nodes comprise a first compute node with a data dependency on a second compute node, wherein the first compute node and second compute node are to operate in parallel with one another.
(See Unger, para. [0036]: “In a further possible embodiment of the present invention, the hardware configuration provides at least one vectorized hardware accelerator or includes such a hardware accelerator. For this purpose, as part of the hardware model, an aligned SIMD-MAC (Single Instruction Multiple Data Multiply Accumulate Operations Unit) unit can be modeled, which has a predetermined common maximum bit length, which is used for both the weights and the activations is applicable. The use of a vectorized hardware accelerator can represent a particularly reliable option for modeling suitable virtual hardware that is suitable in many applications. In addition, this can result in optimization or acceleration options through data level parallelization at the level of individual calculation units.”)
In regards to claim 20:
20. (Original) The method of claim 19, wherein the first compute node is to use latest available data from the second compute node.
(See Bonanno, para. [0017]: “In one embodiment the process may begin by polling the source code management repository 22 for any changes in dependency information in the component source modules 26. In one embodiment, both the source code management repository 22 and/or the binary repository 24 may be polled for changes. In one embodiment, polling may be initiated manually whereby the polling is initiated by a developer via a client computer 18. In another embodiment, polling may be initiated automatically. Automatic polling may be triggered by an expiration of a configured time interval. If no changes are detected, then the polling may be rescheduled for a later time. A continuous integration build server, such as Cruise Control™ or Anthill™ can be used to poll the source code management repository 22 and/or the binary repository 24 for changes. Alternatively, a time-based scheduling service, such as “cron” of UNIX-like operating systems, could be used. In response to detecting changes in the dependency information, the dependency graph may be created based on the dependency information.”)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over are rejected under 35 U.S.C. 103 as being unpatentable over “Bonanno” in view of “Unger” as applied to claim 1 above, and further in view of Official Notice
In regards to claim 14, under a conservative interpretation of “Bonanno” in view of “Unger”, it could be argued that neither “Bonanno” nor “Unger” explicitly teaches the following features:
14. (Currently Amended) The computing apparatus of claim 1, wherein the instructions are to run on a data center or cloud platform offboard the AV controller.
Official Notice is given that it would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include the feature of “wherein the instructions are to run on a data center or cloud platform offboard the AV controller” in the combined teaching of “Bonanno” in view of “Unger”, because distributed computing is old and well known in the art, as is the use of cloud computing for neural network (“artificial intelligence”) processing.
Response to Amendments
Re: Claim Rejections - 35 USC § 101
The 35 U.S.C. §101 rejection of claims 1-11 and 13-20 has been withdrawn. The Examiner interprets that the amended independent claims 1, 15, and 18 recite a practical application of the abstract idea, because these independent claims have been amended to recite the following practical application of the abstract idea of “iteratively modeling the second hardware application”:
cause the optimum configuration to be implemented in a real-world embodiment of the second hardware configuration to optimize resource allocation,
wherein the first hardware configuration and second hardware configuration are for an autonomous vehicle (AV) controller.
Re: Claim Rejections - 35 USC § 103
The 35 U.S.C. §103 rejection of claims 1-11 and 13-20 has been amended, as necessitated by Applicant’s amendments to the independent claims.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
December 24, 2025
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