DETAILED ACTION
The present application, filed on 7/7/2023 is being examined under the AIA first inventor to file provisions.
The following is a non-final First Office Action on the Merits. Claims 1-8 are pending and have been considered below.
Priority
This application is a CON of PCT/CN2022/114385 08/24/2022;
CHINA 202210632036.6 06/07/2022.
The priority is acknowledged.
Information Disclosure Statement (IDS)
The information disclosure statement (IDS) submitted on 7/7/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, such IDS is being considered by Examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION – The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Claims 1, 7-8 are rejected under 35 U.S.C. 112(b) because the scope of the claim cannot be ascertained. The claims recite: “under a default cache allocation scheme.” However, it is not clear which default cache allocation scheme the claim limitation refers to, for numerous such schemes are known.
Claims 1, 7-8 are rejected under 35 U.S.C. 112(b) because the scope of the claim cannot be ascertained. The claims recite: “a cache dynamic allocation and management strategy.” However, it is not clear which cache dynamic allocation and management strategy the claim limitation refers to, for numerous such strategies are known.
Claim 6 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. The claim recites: “relatively uniformly distributed.” Whether the distribution is uniform or not is a matter of opinion; reasonable people would reasonably disagree as to the distribution being uniform or not. See MPEP 2173.05(b) and authorities cited therein.
The remainder of the claims are rejected by virtue of dependency.
Examiner Remarks
The instant claim set is eligible under 35 USC 101, because the claims are in conformity with the provisions of MPEP 2106.04-07.
No art rejection has been applied to the instant set of claims. The identified prior art does not disclose at least the following claim elements:
calculating an average sample number of each training batch hit in a cache of each task under a default cache allocation scheme, and an expected sample number of each training batch hit in the cache of each task;
concurrently executing deep learning training by the multiple concurrent tasks by using a cache dynamic allocation and management strategy; and
adding no new sample data to the cache of each task, moreover, with the sample data in the cache being gradually consumed,
gradually releasing occupied cache,
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20210042155 A1 WANG; Yingrui et al. TASK SCHEDULING METHOD AND DEVICE, AND COMPUTER STORAGE MEDIUM Provided are a task scheduling method and device, and a computer storage medium. The task scheduling method includes that: a dependency relationship among multiple operation tasks is determined according to operands corresponding to multiple operation tasks in an operation task queue; and the multiple operation tasks in the operation task queue are scheduled based on the dependency relationship among the multiple operation tasks.
US 20160239344 A1 Wang; Feng et al. Process Scheduling to Improve Victim Cache Mode Aspects include computing devices, systems, and methods for implementing scheduling an execution process to an execution processor cluster to take advantage of reduced latency with a victim cache. The computing device may determine a first processor cluster with a first remote shared cache memory having an available shared cache memory space. To properly schedule the execution process, the computing device may determine a second processor cluster with a lower latency to the first remote shared cache memory than an execution processor cluster scheduled with the execution process. The second processor cluster may be scheduled the execution process, thus becoming the execution processor cluster, based on a size of the available shared cache memory space and the latency of the second processor cluster to the first remote shared cache memory. The available shared cache memory space may be used as the victim cache for the execution process.
US 20220277565 A1 HARO; Rocco REAL-TIME VIDEO DIMENSIONAL TRANSFORMATIONS OF VIDEO FOR PRESENTATION IN MIXED REALITY-BASED VIRTUAL SPACES A non-immersive virtual reality (NIVR) method includes receiving sets of images of a first user and a second user, each image from the sets of images being an image of the associated user taken at a different angle from a set of angles. Video of the first user and the second user is received and processed. A first location and a first field of view are determined for a first virtual representation of the first user, and a second location and a second field of view are determined for a second virtual representation of the second user. Frames are generated for video planes of each of the first virtual representation of the first user and the second virtual representation of the second user based on the processed video, the sets of images, the first and second locations, and the first and second fields of view.
US 20160232091 A1 Wang; Feng et al. Methods of Selecting Available Cache in Multiple Cluster System Aspects include computing devices, systems, and methods for implementing selecting an available shared cache memory as a victim cache. The computing device may identify a remote shared cache memory with available shared cache memory space for use as the victim cache. To select the appropriate available shared cache memory, the computing device may retrieve data for the identified remote shared cache memory or a processor cluster associated with the identified remote shared cache memory relating to a metric, such as performance speed, efficiency, or effective victim cache size. Using the retrieved data, the computing device may determine the identified remote shared cache memory to use as the victim cache and select the determined remote shared cache memory to use as the victim cache.
US 20200167658 A1 Du; Jessica System of Portable Real Time Neurofeedback Training A first device collects human user brainwave data and transfers it to a second device through Bluetooth/USB. The second device uses artificial intelligence to process the data received from the first device then ports trained Deep Learning models to the third device. Human users use the third device which provides neurofeedback services to change the current brainwave state to the desired state.
US 20150040669 A1 Borkholder; David A. et al. DEVICES, SYSTEMS AND METHODS FOR DETECTING AND EVALUATING IMPACT EVENTS An impact detection device for detecting impacts to a body part of a user and various supporting systems are discussed. In an example, an impact detect device can include a circuit board, a component having a first section and a second section, a battery, and a molding for housing the circuit boat, the battery and the component. The circuit board can include impact detection circuitry including at least two sensors and a communication circuit. A zone of reduced rigidity can connect the first and second sections of the component, with the circuit board secured to the first section. The battery can be secured to the second section of the component allowing for flex relative to the circuit board. The molding can be shaped and dimensioned for mounting to a body part of the user.
US 20210319312 A1 MALAYA; Nicholas Penha et al. DEEP LEARNING ACCELERATION OF PHYSICS-BASED MODELING Values of physical variables that represent a first state of a first physical system are estimated using a deep learning (DL) algorithm that is trained based on values of physical variables that represent states of other physical systems that are determined by one or more physical equations and subject to one or more conservation laws. A physics-based model modifies the estimated values based on the one or more physical equations so that the resulting modified values satisfy the one or more conservation laws.
US 20210232498 A1 ZHANG; Hang et al. METHOD FOR TESTING EDGE COMPUTING, DEVICE, AND READABLE STORAGE MEDIUM The disclosure discloses a method for testing edge computing, a device, and a readable storage medium, and relates to a field of edge computing technologies. The detailed implementation includes: obtaining a model to be tested from a mobile edge platform; generating a test task based on the model to be tested, the test task including the model to be tested and an automated test program for operating the model to be tested; delivering the test task to an edge device, to enable the edge device to operate the model to be tested by executing the automated test program; and generating a test result based on execution information of the test task.
US 20200348965 A1 Chenxi; Hu et al. METHODS, DEVICES AND COMPUTER PROGRAM PRODUCTS FOR PROCESSING TASK Embodiments of the present disclosure provide methods, devices, and computer program products for processing a task. A method of processing a task comprises: receiving, at a network device and from a set of computing devices, a set of processing results derived from processing the task by the set of computing devices; in response to receiving the set of processing results, executing a reduction operation on the set of processing results; and transmitting a result of the reduction operation to the set of computing devices. In this way, embodiments of the present disclosure can significantly reduce an amount of data exchanged among a plurality of devices processing a task in parallel, and thus reduce network latency caused by data exchange.
US 12522798 B2 Tran; Thanh Quoc et al. Microorganic detection system using a deep learning model Aspects of the present disclosure relate to a method of colony enumeration. The method includes identifying colony forming units of microorganisms in a combined image using a pretrained deep learning model on a colony enumeration device. The method can include providing a plurality of identification characteristics of the colony forming units to an interaction component such that the interaction component can project at least some of the plurality of identification characteristics onto the combined image.
US 11373062 B1 Wang; Zijia et al. Model training method, data processing method, electronic device, and program product Embodiments of the present disclosure relate to a model training method, a data processing method, an electronic device, and a computer program product. The method includes: acquiring storage information associated with a simulated network environment; and training a reinforcement learning model using simulated data and based on a simulated-data read request for a node among multiple nodes included in the simulated network environment and each having a cache. With the technical solutions of the present disclosure, the cache allocation and cache replacement problems can be simultaneously solved by using a reinforcement learning model to determine in a dynamic environment a data caching scheme that meets predetermined criteria, so that it is possible to not only improve the accuracy and efficiency of determining the data caching scheme with less cost overhead, but also improve the user experience of users using the caching system.
US 20230111370 A1 Tran; Thanh Quoc et al. MICROORGANIC DETECTION SYSTEM USING A DEEP LEARNING MODEL Aspects of the present disclosure relate to a method of colony enumeration. The method includes identifying colony forming units of microorganisms in a combined image using a pretrained deep learning model on a colony enumeration device. The method can include providing a plurality of identification characteristics of the colony forming units to an interaction component such that the interaction component can project at least some of the plurality of identification characteristics onto the combined image.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Radu Andrei whose telephone number is 313.446.4948. The examiner can normally be reached on Monday – Friday 8:30am – 5pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached at 571.272.7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Radu Andrei/
Primary Examiner, AU 3698