DETAILED ACTION
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 .
This application claims the benefit of U.S. Provisional Patent Application No. 63/564,062, filed 03/12/2024. The benefit claim is acknowledged by the Examiner.
Examiner’s Note
The Examiner cites particular columns, paragraphs, figures, and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may also apply. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
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, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde et al. (WO 2024177751 A1) hereafter Hyde in view of Ranganath et al. (US 20240259879 A1) hereafter Ranganath.
Regarding claim 1, Hyde teaches:
A non-transitory computer readable medium including program instructions for execution (Paragraph 59; “The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se”);
client device (Paragraph 44; “The software platform may include a client application that executes on an external computing device and that interacts with the platform in a client-server architecture.”);
allocate the hardware resources for execution of scientific visualization of a physical simulation, wherein the scientific visualization is configured to execute on different types of the hardware resources according to an application programming interface (Paragraph 24; “recommendation engine 230 may use static analysis, artificial intelligence, or other related analytical and statistical techniques (which would be apparent to one skilled in the art) to optimize the execution of software in the system and using cloud provider resources. For instance, the engine 230 may identify a particular software executable as being capable of using accelerators like GPUs, and accordingly, the engine would select GPU-equipped cloud compute node 160 instance types. Similarly, the engine 230 may identify that the software may scale linearly up to 16 cores but sub-linearly after 16 cores and might accordingly select compute nodes 160 with 16 cores per node.” discloses the engine identifying whether the software is capable of using various kinds of hardware resources, such as accelerators and GPUs among various compute node instance types. Paragraph 13 further discloses “user 100 may be a scientific researcher who has physics simulation code that they wish to run on the cloud; for instance, a three-dimensional (3D) fluid simulation of a waterfall. This fluid simulation code may be a command-line application that is invoked from a terminal or command prompt, as is typical for scientific software (though we later detail support for software with graphical interfaces as well). The user 100 enters a command to the utility 120 that they wish to be run in the cloud. The utility 120 then transmits information, software, and data from the user's device to an ADVISER backend 150 via ADVISER application programming interfaces (APIs) 130.”, which discloses a 3D fluid simulation, corresponding to a scientific visualization of a physical simulation, which is implemented using an API);
a hardware independent API (Paragraph 34; “data assimilation APIs 530 may form part of the ADVISER APIs 130 and may receive data from the user 100 to be used by the software of the ADVISER backend 150”, and Paragraph 24; “engine 230 may identify a particular software executable as being capable of using accelerators like GPUs, and accordingly, the engine would select GPU-equipped cloud compute node 160 instance types. Similarly, the engine 230 may identify that the software may scale linearly up to 16 cores but sub-linearly after 16 cores and might accordingly select compute nodes 160 with 16 cores per node”, where the API presents a hardware agnostic interface to the caller while the hardware specific resource selection is performed transparently by the backend.);
render images using the scientific visualization tasks based on datasets from the execution of the physical simulation (Paragraph 19; “In some embodiments, the user 100 may also interact with their data without having to download any of the results. For instance, the system may have visualization software 140 pre- installed on cloud compute nodes, and that software may connect to cloud provider storage 170 and be made accessible via ADVISER APIs 130 or directly to the user 100 (even though this is not indicated in FIG. 1, this connection should be clear to one skilled in the art). In the example process of one embodiment, a user may run a scientific visualization or animation software like ParaView or Blender to create rendered animations of a waterfall simulation. Visualization or analysis software 140 may also be supplied by the user 100 as opposed to being built into the system, and such software may also be uploaded via ADVISER APIs 130. Visualization and analysis software 140 may be rendered to users 100 via techniques like server- side rendering and streaming of frames or may use other remote desktop protocols.”, the data that the user interacts with corresponds to datasets from the execution of the physical simulation. The user is able to utilize visualization or animation software to render images/animations.);
one of (i) send the rendered images for display at the client device or (ii) send physical simulation data for rendering and display at the client device (Paragraph 37; “user 100 may have provided the data and software to the ADVISER backend 150, and the ADVISER backend 150 may have executed the simulation based on such data. In one embodiment, the ADVISER backend 150 may include Google Cloud under a load-balancing framework. The visualization tools 140 may have generated the 3D rendering of FIG. 6A for display on the user's 100 computer.”, explicitly disclosing generating the rendering for display at the client device, which requires that the rendered image, or the information necessary to generate the rendered image, is sent to the client device. In the example given, the visualization tools 140 is part of the hosted infrastructure, thereby corresponding to the second element of the list of elements.).
Hyde does not teach hardware resources of a virtual data center (VDC); place tasks of the scientific visualization for execution on the hardware resources in proximity to data of intermediate results from execution of the physical simulation at the VDC.
However, Ranganath teaches:
one or more compute nodes having hardware resources (Paragraph 32; “As examples, the compute node/platform operating as a controller can be one or more edge compute nodes, one or more cloud compute nodes (or a cloud compute cluster), one or more application servers, one or more RAN nodes, a collection of hardware accelerators, and/or some other computing element”);
data centers (Paragraph 23; “The following example implementations generally related to edge computing, cloud computing, network communication, data centers”);
virtualization (Paragraph 55; “The application layer 3c03 operates on top of a system SW layer 3c40 (also referred to as a “virtualization layer 3c40” or the like). The system SW layer 3c40 includes virtualized infrastructure 3c41 (also referred to as “virtual operating platform 3c41”, “virtual infrastructure 3c41”, “virtualized HW resources 3c41”, or the like), which is an emulation of one or more HW platforms on which the VMs 3c31, apps 3c32, and/or containers 3c33 operate.”);
place tasks for execution on the hardware resources in proximity to data of intermediate results from execution (Paragraph 135; “Each of the edge compute nodes are disposed at an edge of a corresponding access network, and are arranged to provide computing resources and/or various services (e.g., computational task and/or workload offloading, cloud-computing capabilities, IT services, and other like resources and/or services as discussed herein) in relatively close proximity to data source devices (e.g., UEs 710)”, which teaches placing computational tasks on resources in relatively close proximity to data. A person of ordinary skill in the art would have understood that executing visualization applications on edge compute nodes located near devices providing the data reduces data transfer requirements and latency associated with data processing. In the context of a visualization workflow, intermediate results are generated from the data being processed by the visualization application. Since Ranganath teaches executing applications on resources located near the source of the data, it suggests placement of visualization tasks on resources located near the data used to generate such intermediate results);
Hyde and Ranganath are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde to incorporate the teachings of Ranganath and have implemented the data center environment with the virtualization infrastructure disclosed because it teaches the use of virtualization to provide virtual hardware resources for operating VMs, applications, and containers. Such an implementation would have yielded virtual datacenters with the predictable result of improving resource utilization and flexibility of the data center environment by allowing workloads to execute on abstracted virtual resources. Further, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have placed tasks of the scientific visualization on the hardware resources in proximity to data of intermediate results from execution of the physical simulation. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that edge computing is a known technique designed to reduce latency and avoid unnecessary transfer of data by moving computation closer to where data is generated. Applying this known technique to visualization processing would yield the predictable result of intermediate results also being located near the edge compute devices and predictably reducing data movement and improving processing efficiency.
Claim 11 recites similar limitations as those of claim 1, directed towards a method. Claim 11 is rejected for similar reasons as those of claim 1.
Claim 20 recites similar limitations as those of claim 1, directed towards a system, additionally reciting one or more compute nodes of a virtual data center (VDC) having hardware resources configured to execute a scientific visualization system configured for interactive visualization of simulation results. Hyde teaches:
scientific visualization (Paragraph 13; “user 100 may be a scientific researcher who has physics simulation code that they wish to run on the cloud; for instance, a three-dimensional (3D) fluid simulation of a waterfall.”, explicitly disclosing 3D fluid simulation. Paragraph 19 further discloses “In the example process of one embodiment, a user may run a scientific visualization or animation software like ParaView or Blender to create rendered animations of a waterfall simulation.”.);
interactive visualization of simulation results (Paragraph 33; “The user-facing tools and workflows may include a client-side interactive data visualizer 525. The client-side interactive data visualizer 525 may form part of the visualization tools 140 and may include an interactive user interface by which the user 100 may view data used or generated by the ADVISER backend 150 and by which the user 100 may interact with such data.”).
Ranganath teaches:
one or more compute nodes having hardware resources (Paragraph 32; “As examples, the compute node/platform operating as a controller can be one or more edge compute nodes, one or more cloud compute nodes (or a cloud compute cluster), one or more application servers, one or more RAN nodes, a collection of hardware accelerators, and/or some other computing element”);
data centers (Paragraph 23; “The following example implementations generally related to edge computing, cloud computing, network communication, data centers”);
virtualization (Paragraph 55; “The application layer 3c03 operates on top of a system SW layer 3c40 (also referred to as a “virtualization layer 3c40” or the like). The system SW layer 3c40 includes virtualized infrastructure 3c41 (also referred to as “virtual operating platform 3c41”, “virtual infrastructure 3c41”, “virtualized HW resources 3c41”, or the like), which is an emulation of one or more HW platforms on which the VMs 3c31, apps 3c32, and/or containers 3c33 operate.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have implemented the data center environment with the virtualization infrastructure disclosed because it teaches the use of virtualization to provide virtual hardware resources for operating VMs, applications, and containers. Such an implementation would have yielded virtual datacenters with the predictable result of improving resource utilization and flexibility of the data center environment by allowing workloads to execute on abstracted virtual resources.
Claim 20 is rejected for similar reasons as those of claim 1.
Claims 2, 4, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski et al. (US 20150310643 A1) hereafter Rzeszotarski.
Regarding claim 2, Hyde in view of Ranganath teach the apparatus of claim 1. Hyde teaches:
program instructions for execution on the hardware resources (Paragraph 59; “The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se”. Paragraph 24; “recommendation engine 230 may use static analysis, artificial intelligence, or other related analytical and statistical techniques (which would be apparent to one skilled in the art) to optimize the execution of software in the system and using cloud provider resources. For instance, the engine 230 may identify a particular software executable as being capable of using accelerators like GPUs, and accordingly, the engine would select GPU-equipped cloud compute node 160 instance types. Similarly, the engine 230 may identify that the software may scale linearly up to 16 cores but sub-linearly after 16 cores and might accordingly select compute nodes 160 with 16 cores per node.” discloses the engine identifying whether the software is capable of using various kinds of hardware resources, such as accelerators and GPUs among various compute node instance types.).
Hyde in view of Ranganath does not teach apply user-controlled filters to manage a size of the datasets.
However, Rzeszotarski teaches:
apply user-controlled filters to manage a size of the datasets (Paragraph 66; “The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest.”).
Hyde, Ranganath, and Rzeszotarski are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath to incorporate the teachings of Rzeszotarski and apply user-controlled filters to manage a size of the datasets. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that applying user-controlled filters is a known technique for reducing the amount of data presented or processed, yielding the predictable result of improving processing efficiency and allowing users to focus on data relevant to a particular analysis or visualization while preserving meaningful analytical results.
Claim 12 recites similar limitations as those of claim 2. Claim 12 is rejected for similar reasons as those of claim 2.
Regarding claim 4, Hyde in view of Ranganath, further in view of Rzeszotarski teach the apparatus of claim 2. Hyde teaches:
a physical simulation (Paragraph 13; “user 100 may be a scientific researcher who has physics simulation code that they wish to run on the cloud; for instance, a three-dimensional (3D) fluid simulation of a waterfall.”);
a client device (Paragraph 44; “The software platform may include a client application that executes on an external computing device and that interacts with the platform in a client-server architecture.”).
Rzeszotarski teaches:
wherein the user-controlled filters reduce the datasets to areas of interest for visual comparison and navigation through the reduced datasets (Paragraph 66; “The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest.”, which supports navigating the organization of the data space by moving among meaningful arrangements, and Paragraph 67; “The user may use queries and overlays to change the appearance or behavior of points on the screen.”, corresponding to visual comparison.).
Claim 14 recites similar limitations as those of claim 4. Claim 14 is rejected for similar reasons as those of claim 4.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Adolf (US 11003814 B1).
Regarding claim 3, Hyde in view of Ranganath, further in view of Rzeszotarski teach the apparatus of claim 2. Rzeszotarski teaches:
user-controlled filters (Paragraph 66; “The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest.”).
Hyde in view of Ranganath, further in view of Rzeszotarski does not teach steer the execution of the physical simulation to manage the size of the datasets.
However, Adolf teaches:
steer the execution of the physical simulation to manage the size of the datasets (Col. 2, lines 51-53; “The forward and reverse simulations of the physical device in the physics simulator can require large state datasets” and Col. 2, lines 62-67; “embodiments described herein apply adaptive filter techniques to significantly reduce the number of time steps between each structural parameter optimization iteration. Additional compact bookkeeping techniques may also be used to reduce the dataset size per simulation time step.”, which discloses recognizing that physical simulations generate large state datasets that increase memory footprint and simulation time, and addresses this by applying adaptive filter techniques to reduce the number of simulation time steps between optimization iterations, thereby modifying the manner in which the physical simulation is executed in order to manage the size of the datasets produced and processed during the simulation.).
Hyde, Ranganath, Rzeszotarski, and Adolf are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath, further in view of Rzeszotarski to incorporate the teachings of Adolf and steer the execution of the physical simulation to manage the size of the datasets. A person of ordinary skill in the art would have recognized steering execution to manage dataset size as a known method in the art because controlling the amount of data processed during a simulation reduces computational resource requirements and improves execution efficiency, yielding the predictable result of reducing memory consumption and improving the performance of the physical simulation.
Claim 13 recites similar limitations as those of claim 3. Claim 13 is rejected for similar reasons as those of claim 3.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen et al. (US 20200217978 A1) hereafter Chen.
Regarding claim 5, Hyde in view of Ranganath, further in view of Rzeszotarski teach the apparatus of claim 2. Hyde teaches:
a client device (Paragraph 44; “The software platform may include a client application that executes on an external computing device and that interacts with the platform in a client-server architecture.”);
a scientific visualization (Paragraph 19; “In some embodiments, the user 100 may also interact with their data without having to download any of the results. For instance, the system may have visualization software 140 pre- installed on cloud compute nodes, and that software may connect to cloud provider storage 170 and be made accessible via ADVISER APIs 130 or directly to the user 100 (even though this is not indicated in FIG. 1, this connection should be clear to one skilled in the art). In the example process of one embodiment, a user may run a scientific visualization or animation software like ParaView or Blender to create rendered animations of a waterfall simulation.”).
Rzeszotarski teaches:
wherein the visualization tasks are configured to synthesize and organize the datasets that enable interactive viewing and querying based on the user-controllable filters (Paragraphs 64-67; “The user can mutate points, for instance combining multiple points into one group so as to observe more points at once or see larger trends. The user may place barriers that block or selectively block points based on criteria. The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest. The user may use queries and overlays to change the appearance or behavior of points on the screen.”, which shows that multidimensional data is converted into physical points having multiple attributes, thereby synthesizing the dataset into a representation that can be manipulated an analyzed. It further discloses that users may mutate points to observe more points and see larger trends, in which combining and arranging data points from a dataset in this manner causes the dataset to be synthesized and organized for interactive viewing and querying.).
Rzeszotarski teaches:
interactive viewing and querying of the datasets (Paragraphs 64-67; “The user can mutate points, for instance combining multiple points into one group so as to observe more points at once or see larger trends. The user may place barriers that block or selectively block points based on criteria. The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest. The user may use queries and overlays to change the appearance or behavior of points on the screen.”, which shows that multidimensional data is converted into physical points having multiple attributes, thereby synthesizing the dataset into a representation that can be manipulated an analyzed. It further discloses that users may mutate points to observe more points and see larger trends, in which combining and arranging data points from a dataset in this manner causes the dataset to be synthesized and organized for interactive viewing and querying. Paragraph 33 suggests the use of multiple datasets. “allowing a user to use physics-based data-manipulation tools can provide the user with not only with easy-to-use and intuitive tools but can also permit the user to comprehend and assimilate the data in datasets relatively quickly and efficiently, thereby allowing the user to make better and/or quicker decisions.”).
Hyde in view of Ranganath, further in view of Rzeszotarski does not teach an ensemble of simulation results.
However, Chen teaches:
an ensemble of simulation results (Paragraph 40; “The ensemble of fine-scale reservoir parameters C may then be used in 2D or 3D flow simulation 14 to generate an ensemble of flow simulation results D which may include pressure, saturation, and flow rate.”).
Hyde, Ranganath, Rzeszotarski, and Chen are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Hyde in view of Ranganath, further in view of Rzeszotarski to incorporate the teachings of Chen and have organized the ensemble of simulation results. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized organizing datasets into an ensemble of simulation results as a known method in the art because aggregating multiple simulation outputs facilitates analysis of trends and variations among different simulation outcomes, the implementation of which would yield the predictable result of enabling more comprehensive evaluation and visualization of simulation data.
Claim 15 recites similar limitations as those of claim 5. Claim 15 is rejected for similar reasons as those of claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen, further in view of Mate et al. (US 9380328 B2) hereafter Mate.
Regarding claim 6, Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen teach the apparatus of claim 5. Hyde teaches:
a client device (Paragraph 44; “The software platform may include a client application that executes on an external computing device and that interacts with the platform in a client-server architecture.”).
Rzeszotarski teaches:
interactive viewing and querying of the datasets (Paragraphs 64-67; “The user can mutate points, for instance combining multiple points into one group so as to observe more points at once or see larger trends. The user may place barriers that block or selectively block points based on criteria. The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest. The user may use queries and overlays to change the appearance or behavior of points on the screen.”, which shows that multidimensional data is converted into physical points having multiple attributes, thereby synthesizing the dataset into a representation that can be manipulated an analyzed. It further discloses that users may mutate points to observe more points and see larger trends, in which combining and arranging data points from a dataset in this manner causes the dataset to be synthesized and organized for interactive viewing and querying. Paragraph 33 suggests the use of multiple datasets. “allowing a user to use physics-based data-manipulation tools can provide the user with not only with easy-to-use and intuitive tools but can also permit the user to comprehend and assimilate the data in datasets relatively quickly and efficiently, thereby allowing the user to make better and/or quicker decisions.”).
Chen teaches:
an ensemble of simulation results (Paragraph 40; “The ensemble of fine-scale reservoir parameters C may then be used in 2D or 3D flow simulation 14 to generate an ensemble of flow simulation results D which may include pressure, saturation, and flow rate.”).
Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen does not teach wherein a size and quality of the results visualized on the device is continuously adjusted based on capabilities of the device.
However, Mate teaches:
wherein a size and quality of the results visualized on the device is continuously adjusted based on capabilities of the device (Col. 7, lines 24-30; “the SMP may adjust the properties of the preview content, such as resolution, size, quality and/or a type of the preview content according to one or more parameters, which may include display capabilities of the user's apparatus, resolutions available in the UGC, network capability through which the user's apparatus is connected, batter status of the user's apparatus, etc.”, explicitly disclosing adjusting the properties of the content based on the capabilities and the status of the user apparatus, which would necessitate continuous adjustment.).
Hyde, Ranganath, Rzeszotarski, Chen, and Mate are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen to incorporate the teachings of Mate and have a size and quality of the results visualized on the device is continuously adjusted based on capabilities of the device. A person of ordinary skill in the art would have recognized continuously adjusting the size and quality of the results based on device capabilities is a known method in the art because device capabilities and operating conditions, such as network capability, may change during operation, the network capability being further considered by Mate (Col. 7, lines 24-30). That is, because Mate recognizes adaptive adjustment based on a changing parameter, such as network capability, updating that adjustment as the parameter changes is a predictable implementation choice, the implementation of which would yield the predictable result of maintaining an appropriate balance between content quality and available device resources.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Zenoff (US 20160048369 A1).
Regarding claim 7, Hyde in view of Ranganath, further in view of Rzeszotarski teach the apparatus of claim 2. Rzeszotarski teaches:
user-controlled filters (Paragraph 66; “The user can employ filters to selectively include or exclude points to help avoid overload or choose only a small subset of interest.”).
Hyde in view of Ranganath, further in view of Rzeszotarski does not teach parametric filter expressions.
However, Zenoff teaches:
parametric filter expressions (Paragraph 434; “The new parsing template is identified as a match and is used to analyze the structure of the selector and to extract parametric information for the selector's filter expression. Since an expression evaluation template has already been created for filter expressions of this type, there is no need to perform any new explicit prepare step or to create a new evaluation template. Instead, the previously-saved expression evaluation template (which already represents this type of expression in a format that can be evaluated efficiently) can be selected by an evaluation template selector 690”, explicitly disclosing the use of parametric information for filter expressions.).
Hyde, Ranganath, Rzeszotaski, and Zenoff are considered to be analogous to the claimed invention because they are in the same field of data visualization. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath, further in view of Rzeszotarski to incorporate the teachings of Zenoff and have the user-controlled filters include parametric filter expressions. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized applying parametric filter expressions to user-controlled filters as a known method in the art because parameter based filtering allows users to more precisely define selection criteria for identifying relevant data, the implementation of which would yield the predictable result of improving control over dataset filtering and enabling more efficient analysis of desired portions of the datasets.
Claim 17 recites similar limitations as those of claim 7. Claim 17 is rejected for similar reasons as those of claim 7.
Claims 8-9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Onodera et al. (US 20100269034 A1) hereafter Onodera.
Regarding claim 8, Hyde in view of Ranganath teach the apparatus of claim 1. Hyde teaches:
program instructions for execution on the hardware resources (Paragraph 59; “The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se”. Paragraph 24; “recommendation engine 230 may use static analysis, artificial intelligence, or other related analytical and statistical techniques (which would be apparent to one skilled in the art) to optimize the execution of software in the system and using cloud provider resources. For instance, the engine 230 may identify a particular software executable as being capable of using accelerators like GPUs, and accordingly, the engine would select GPU-equipped cloud compute node 160 instance types. Similarly, the engine 230 may identify that the software may scale linearly up to 16 cores but sub-linearly after 16 cores and might accordingly select compute nodes 160 with 16 cores per node.” discloses the engine identifying whether the software is capable of using various kinds of hardware resources, such as accelerators and GPUs among various compute node instance types.);
scientific visualization tasks (Paragraph 13; “user 100 may be a scientific researcher who has physics simulation code that they wish to run on the cloud; for instance, a three-dimensional (3D) fluid simulation of a waterfall.”);
tasks are placed at processing resources of the hardware resources (Paragraph 16; “One skilled in the art would readily recognize that other cloud provider resources, such as networking, domain name system (DNS), load balancing, proxy, and other cloud provider resources may be provisioned to support the ultimate execution of the software on compute nodes. Notably, scientific software, like fluid simulation codes, are often parallel, and as such, there may be several compute nodes 160 involved in performing the task specified by the user”, in which the compute nodes are processing resources assigned to perform the specified task. Paragraph 36 further discloses “the cloud provider abstracts 555 may include one or more data structures that store logical data objects that correspond to one or more pieces of hardware”, thereby showing that the resources are abstracted hardware resources.).
Ranganath teaches:
one or more compute nodes having hardware resources (Paragraph 32; “As examples, the compute node/platform operating as a controller can be one or more edge compute nodes, one or more cloud compute nodes (or a cloud compute cluster), one or more application servers, one or more RAN nodes, a collection of hardware accelerators, and/or some other computing element”);
data centers (Paragraph 23; “The following example implementations generally related to edge computing, cloud computing, network communication, data centers”);
virtualization (Paragraph 55; “The application layer 3c03 operates on top of a system SW layer 3c40 (also referred to as a “virtualization layer 3c40” or the like). The system SW layer 3c40 includes virtualized infrastructure 3c41 (also referred to as “virtual operating platform 3c41”, “virtual infrastructure 3c41”, “virtualized HW resources 3c41”, or the like), which is an emulation of one or more HW platforms on which the VMs 3c31, apps 3c32, and/or containers 3c33 operate.”);
A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that edge computing is a known technique designed to reduce latency and avoid unnecessary transfer of data by moving computation closer to where data is generated. Applying this known technique to visualization processing would yield the predictable result of intermediate results also being located near the edge compute devices and predictably reducing data movement and improving processing efficiency.
Hyde in view of Ranganath does not teach trade-off memory consumption for processing speed.
However, Onodera teaches:
trade-off memory consumption for processing speed (Paragraph 60; “The reason of the division by size in the equation is that the larger the argument of the echo command, the more the memory is occupied. In other words, the reason is to consider a tradeoff between a memory footprint and a processing speed.”, explicitly teaches trading off memory footprint for processing speed and considering a tradeoff between the two to balance memory consumption and execution performance.).
Hyde, Ranganath, and Onodera are considered to be analogous to the claimed invention because they are in the same field of program control. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath to incorporate the teachings of Onodera and have performed a tradeoff between memory consumption for processing speed. A person of ordinary skill in the art would have recognized tradeoffs between memory consumption and processing speed to be a known method in the art because computing systems commonly balance available resources against execution performance, the implementation of which would yield the predictable result of optimizing resource utilization and processing efficiency.
Claim 18 recites similar limitations as those of claim 8. Claim 18 is rejected for similar reasons as those of claim 8.
Regarding claim 9, Hyde in view of Ranganath, further in view of Onodera teach the apparatus of claim 8. Hyde teaches:
wherein the processing resources are graphics processing units (Paragraph 24; “For instance, the engine 230 may identify a particular software executable as being capable of using accelerators like GPUs, and accordingly, the engine would select GPU-equipped cloud compute node 160 instance types.”, the compute nodes corresponding to processing resources, explicitly disclosed as being GPU-equipped, corresponding to the claimed limitation.).
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Karpistsenko et al. (US 20180101529 A1) hereafter Karpistsenko.
Regarding claim 10, Hyde in view of Ranganath teach the apparatus of claim 1. Hyde teaches:
a scientific visualization (Paragraph 19; “In some embodiments, the user 100 may also interact with their data without having to download any of the results. For instance, the system may have visualization software 140 pre- installed on cloud compute nodes, and that software may connect to cloud provider storage 170 and be made accessible via ADVISER APIs 130 or directly to the user 100 (even though this is not indicated in FIG. 1, this connection should be clear to one skilled in the art). In the example process of one embodiment, a user may run a scientific visualization or animation software like ParaView or Blender to create rendered animations of a waterfall simulation.” );
a physical simulation (Paragraph 13; “user 100 may be a scientific researcher who has physics simulation code that they wish to run on the cloud; for instance, a three-dimensional (3D) fluid simulation of a waterfall.”).
Hyde in view of Ranganath does not teach execute in parallel.
However, Karpistsenko teaches:
execute in parallel (Paragraph 92; “The visualizations may be used to aid a user in managing and/or guiding simulations and/or parallel execution of models into a particular area of parameters, for example, by a selection of values 606.”, explicitly disclosing guiding simulations and parallel execution of models.).
Hyde, Ranganath, and Karpistsenko are considered to be analogous to the claimed invention because they are in the same field of simulation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath to incorporate the teachings of Karpistsenko and have executed the physical simulation and scientific visualization tasks in parallel. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized application of parallel execution techniques to be a known method in the art because parallel execution is known to improve computational efficiency by allowing multiple operations to be concurrently performed, whose implementation would yield the predictable result of reducing simulation execution time and improving responsiveness of the visualization system.
Claim 19 recites similar limitations as those of claim 10. Claim 19 is rejected for similar reasons as those of claim 10.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen, further in view of Prabhu et al. (US 20190005104 A1) hereafter Prabhu.
Regarding claim 16, Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen teach the method of claim 15. Hyde teaches:
a client device (Paragraph 44; “The software platform may include a client application that executes on an external computing device and that interacts with the platform in a client-server architecture.”).
Chen teaches:
an ensemble of simulation results (Paragraph 40; “The ensemble of fine-scale reservoir parameters C may then be used in 2D or 3D flow simulation 14 to generate an ensemble of flow simulation results D which may include pressure, saturation, and flow rate.”).
Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen does not teach qualitative visualization for analysis is automatically increased in detail as the viewed simulation results are narrowed.
However, Prabhu teaches:
qualitative visualization for analysis is automatically increased in detail as the viewed results are narrowed (Paragraph 49; “In several embodiments, the particular facts within the reporting data that are included within the rendered visualization image data depend on the portion of the reporting data currently being explored using an interest-driven data visualization device. This allows the user to perform a semantic pan and zoom throughout the reporting data. A semantic zoom functionality provides the user with the ability to view a broad overview of the reporting data within the visualization and then dynamically drill down in increasing detail on particular portions of the reporting data.”, explicitly teaching increasing in detail as particular portions of the reporting data are zoomed in upon, corresponding to qualitative visualization for analysis being automatically increased as the viewed results are narrowed.).
Hyde, Ranganath, Rzeszotarski, Chen, and Prabhu are considered to be analogous to the claimed invention because they are in the same field of data visualization. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hyde in view of Ranganath, further in view of Rzeszotarski, further in view of Chen to incorporate the teachings of Prabhu and have qualitative visualization for analysis be automatically increased in detail as the viewed results are narrowed. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized automatically increasing the detail of a qualitative visualization as results are narrowed to be a known method in the art because providing greater detail for a smaller subset of data allows users to more effectively analyze relevant information without imposing unnecessary processing and display burdens on larger datasets, the implementation of which would yield the predictable result of improving visualization clarity and user analysis of the results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dhatchina et al. (WO 2024081073 A1) discloses identifying a simulation of what an application can do by utilizing visualizations displayed to the user for review to determine how best to use the application.
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/KENNETH P TRAN/Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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