DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Request for Continued Examination and Applicant Amendment and Arguments filed on 01 April, 2026.
Claims 1-6 and 9-20 are pending for examination. Claims 7-8 were cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01 April, 2026 has been entered.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 9-13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over HWANG et al. (US Pub. 2020/0196024 A1) in view of Ferreira Moreno et al. (US Pub. 2019/0370634 A1; hereafter Moreno) and further in view of SHI (US Pub. 2020/0412521 A1) and Afzal et al. (US Patent. 11,537,853 B1).
HWANG, Ferreira and Moreno was cited in the previous Office Action.
As per claim 1, HWANG teaches the invention substantially as claimed including A neural network model scheduling method (HWANG, Fig. 6, 140, 606, 230 and 240 rendering engine model; [0007] lines 14-15, execute the neural network to process the media data and generate the particular media data output), comprising:
storing a plurality of pre-trained neural network models in a memory (HWANG, Fig. 4, 230 and 410; Fig. 6, 140, 606, 230 and 240 rendering engine model; [0091] lines 1-4, The neural network 410 can be pre-trained to process the features from the data in the input layer 402 using the different hidden layers 404 in order to provide the output through the output layer 406; [0111] lines 1-11, the remote system 140 can have the infrastructure and capabilities, such as storage and/or compute capabilities, to compute and/or maintain a large number of rendering engine models. For example, the remote system 140 can be a server or cloud service containing a large repository of rendering engine models and having the compute capabilities to generate and store rendering engine models having increased complexity, customization, size, etc. The remote system 140 can also train rendering engine models and make the trained rendering engine models available; [0112] lines 3-7, the address 604 to rendering engine model 606 on the remote system 140. The address 604 can include, for example, a uniform resource identifier (URI), a link, a path, a resource locator, or any other address such as a network, storage; also see [0119] The rendering engine models can be pre-configured by the manufacturer, and can describe rendering engines (e.g., neural networks) which have been trained and tuned as described in flow 700; [0142] The storage device may include any of a variety of distributed or locally accessed data storage media such as…flash memory, volatile or non-volatile memory), and acquiring a base address of the pre-trained neural network models (HWANG, Fig. 6, see arrow from 140 rendering engine model to be collected by 110 compute engine; [0061] lines 1-10, the compute engine 110 can receive media data (e.g., image data, video data, audio data, etc.) captured by any of the data capture devices 102, 104, 152, 154 in the computing environment 100, and receive, generate, and/or retrieve from storage one or more rendering engine models for the media data. The compute engine 110 can embed the one or more rendering engine models in a media item containing the media data, and/or embed an address (e.g., a uniform resource identifier (URI); a link; a path; a network, storage or destination address; a resource locator; etc.) to one or more rendering engine models in a media item containing the media data; also see Fig. 6, 604 address for rendering engine model (as acquiring a base address of the pre-trained neural network model and embedded into media item); [0067] lines 1-4, The compute engine 110 can generate or obtain one or more rendering engine models 230, 240 configured to process and render the media data 210; [0091] lines 1-4, The neural network 410 can be pre-trained to process the features from the data in the input layer 402 using the different hidden layers 404 in order to provide the output through the output layer 406),
acquiring base addresses of the corresponding neural network models selected from the plurality of pre-trained neural network models according to a task type, the corresponding neural network models stored at the base addresses (HWANG, [0116] lines 1-9, address (e.g., 604) can include such information (e.g., description information) even if it is the only address in the media item 602. The media processing system 102 (or associated user) can use this information to determine whether the rendering engine model associated with the address is suitable for the particular instance or comports with the desired processing or rendering outcome; [0112] lines 1-12, the media item 602 includes the media data 210 and the address 604 to rendering engine model 606 on the remote system 140. The address 604 can include, for example, a uniform resource identifier (URI), a link, a path, a resource locator, or any other address such as a network, storage or destination address. The address 604 indicates where the rendering engine model 606 is located and/or how it can be retrieved. When the media processing system 102 processes the media item 602 (e.g., as described in flow 300 illustrated in FIG. 3A), it can use the address 604 to retrieve the from the remote system 140 (as to acquiring base addresses of corresponding rendering engine model 606 neural network models according to a task type); also see [0131] the media processing system 102 can include multiple rendering engine models (e.g., 230, 240) and/or addresses (e.g., 604) in the media item. For example, the media processing system 102 can embed an additional rendering engine model (e.g., 240) in the media item (e.g., 220). The additional rendering engine model can include an additional description of an additional neural network configured to process the media data (e.g., 210) and generate a different media data output. The additional description can define a different neural network architecture for the additional neural network. The different neural network architecture can be customized for a different operational outcome based on different neural network layers, filters, activation functions, parameters, etc; [0136] a address (e.g., 604) to a remote rendering engine model or a remote location of the remote rendering engine model. The remote rendering engine model can include a respective description of a neural network configured to process the media data and generate a respective media data output. The media item with the address can be sent to a recipient, which can use the address to retrieve the remote rendering engine model from the remote location and, based on the respective description in the remote rendering engine model, generate the neural network associated with the remote rendering engine model and process the media data in the media item using the neural network to generate the respective media data output (e.g., the rendering of the media data).); and
invoking, on the basis of the base addresses of the corresponding neural network models, the corresponding neural network models to compute the data to obtain a computation result, and outputting the computation result. (HWANG, Fig. 7, 410 neural network, 704 output, [0007] lines 14-15, execute the neural network to process the media data and generate the particular media data output; [0112] lines 1-12, The address 604 can include, for example, a uniform resource identifier (URI), a link, a path, a resource locator, or any other address such as a network, storage or destination address. The address 604 indicates where the rendering engine model 606 is located and/or how it can be retrieved. When the media processing system 102 processes the media item 602 (e.g., as described in flow 300 illustrated in FIG. 3A), it can use the address 604 to retrieve the rendering engine model 606 from the remote system 140; [0109] lines 3-12, The rendering engine model 606 can be a particular rendering engine model capable of rendering the media data 210. In this example, the rendering engine model 606 is stored in the remote system 140. The remote system 140 can store one or more rendering engine models 606, 230, 240 for the media data 210 (and other media data), which can be access by the media processing system 102 (and any other device) to process and render media data).
HWANG fails to specifically teach when storing, it is loading a plurality of trained neural network models to a model storage area in a memory, wherein the memory further comprises a common data storage area, reading data in the common data storage area and when compute, it is compute the data read in the common data storage area to obtain a computation result.
However, Moreno teaches loading a plurality of trained neural network models to a model storage area in a memory (Moreno, [0048] lines 8-9, The trained models may be loaded on the memory 304; Fig. 3, 304 memory, machine learning models (as model storage area), wherein the memory further comprises a common data storage area, reading data in the common data storage area (Moreno, Fig. 3, 304 memory, data units (as common data storage area in the memory); [0027] lines 1-4, A data unit 202 describes an input data embedded in a smart contract. There may be a plurality of data units 202. A data unit 202 may be received from a data provider 204 and stored in a memory device or storage device; [0029] lines 1-12, Machine learning algorithms 206 are used to generate machine learning models 208. There may be new or existing machine learning models. A machine learning algorithm, for example, trains a machine learning model, for example, based on a data set contained or referenced in a data unit 202. A machine learning model trained by running a machine learning algorithm or technique may include parameters or weights which are generated during the training phase. A machine learning model takes data units as input (e.g., input feature vectors) in order to produce an outcome such as a prediction, classification, and/or another outcome) and
when compute, it is compute the data read in the common data storage area to obtain a computation result (Moreno, [0029] lines 1-12, Machine learning algorithms 206 are used to generate machine learning models 208. There may be new or existing machine learning models. A machine learning algorithm, for example, trains a machine learning model, for example, based on a data set contained or referenced in a data unit 202. A machine learning model trained by running a machine learning algorithm or technique may include parameters or weights which are generated during the training phase. A machine learning model takes data units as input (e.g., input feature vectors) in order to produce an outcome such as a prediction, classification, and/or another outcome; [0038] lines 1-7, data unit's data blocks may be used as a training set to train a model of a given machine learning algorithm. Multiple models may be trained based on different data unit's data blocks. The trained models may be run with input data and the outcomes or output data produced by the trained models may be verified against the ground-truth data (or what the actual outcome associated with the input data should be). In this way, the data platform 210 may infer which data units provide the best value for a given service. For example, a selection of data units relating to coarse-resolution satellite scenes may have been selected to train a model used by a service that computes statistics on deforestation).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of HWANG with Moreno because Moreno’s teaching of utilizing the data that is stored in the memory to train the machine learning model in the memory to produce the output would have provided HWANG’s system with the advantage and capability to allow the system to build an accurate machine learning model in order to improving the system performance and efficiency (see Moreno, [0003] “building of an accurate machine learning model, that data set should be recognized as needing improvement”).
HWANG and Moreno fail to specifically teach loading the corresponding neural network models into a processor.
However, SHI teaches loading the corresponding neural network models into a processor (SHI, [0022] lines 25-28, the processor can be able to efficiently implement custom data processing applications by loading pre-trained neural network models into the AI/neuromorphic core 314).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of HWANG and Moreno with SHI because SHI’s teaching of loading the pre-trained neural network models into core/processer would have provided HWANG and Moreno’s system with the advantage and capability to allow the system to be able to efficiently implement custom data processing applications in order to improving the system performance and efficiency (see SHI, [0022] “efficiently implement custom data processing applications”).
HWANG, Moreno and SHI fail to specifically teach wherein an intermediate computation result is stored into the common data storage area for a sequent neural network to use.
However, Afzal teaches wherein an intermediate computation result is stored into the common data storage area for a sequent neural network to use (Afzal, Col 2, lines 6-11, a neural network processor, also referred to as a neural network accelerator (NNA), in which the neural network processor includes a decompression pipeline for decompressing data operated on by a neural network and/or a compression pipeline for compressing data generated by the neural network; Col 15, lines 33-43, instead of being written to the activation buffer 640, the compressed activation values 617 could be written to system memory, e.g., for storage as the final results of the neural network, or for subsequent loading back to the same or a different NNA. Alternatively, the compressed activation values 617 could be sent to a remote computing system, e.g., to a cloud server through a network interface of the computing system in which NNA 100 is deployed. The remote computing system may execute a neural network using the compressed activation values 617 or perform some other downstream processing of the compressed activation values 617. (as wherein an intermediate computation result (since it will be used later) is stored into the common data storage area (i.e., an area that storing this result in the memory) for a sequent neural network to use (i.e., remote computing system that execute a neural network)).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of HWANG, Moreno and SHI with Afzal because Afzal’s teaching of storing the result into a memory which allowing the next neural network to use would have provided HWANG, Moreno and SHI’s system with the advantage and capability to allow the system to storing, transmitting and computing the result for different neural networks which improving the resource utilization and system performance.
As per claim 2, HWANG, Moreno, SHI and Afzal teach the invention according to claim 1 above. Moreno further teaches wherein the model storage area is configured to store a network structure of the pre-trained neural network models and parameters of the pre-trained neural network models (Moreno, Fig. 2, 208; Fig. 3, 304 memory, machine learning models; [0034] lines 8-9, a model having architecture or structure associated with the machine learning algorithm; [0025] lines 1-3, the generated models may operate with different parameters, and thus may produce different outcomes; [0048] lines 8-9, The trained models may be loaded on the memory 304; (as including the network structure and parameters of the neural network model)).
As per claim 3, HWANG, Moreno, SHI and Afzal teach the invention according to claim 1 above. HWANG further teaches wherein the base address is an initial storage address of a neural network model in the memory (HWANG, [0111] lines 1-11, the remote system 140 can have the infrastructure and capabilities, such as storage and/or compute capabilities, to compute and/or maintain a large number of rendering engine models. For example, the remote system 140 can be a server or cloud service containing a large repository of rendering engine models and having the compute capabilities to generate and store rendering engine models having increased complexity, customization, size, etc. The remote system 140 can also train rendering engine models and make the trained rendering engine models available; [0112] lines 3-7, the address 604 to rendering engine model 606 on the remote system 140. The address 604 can include, for example, a uniform resource identifier (URI), a link, a path, a resource locator, or any other address such as a network, storage (as including initial storage address); [0142] The storage device may include any of a variety of distributed or locally accessed data storage media such as…flash memory, volatile or non-volatile memory).
As per claim 4, HWANG, Moreno, SHI and Afzal teach the invention according to claim 3 above. HWANG teaches invoking, on the basis of the base addresses of the corresponding neural network models, the corresponding neural network models to compute the data (HWANG, Fig. 7, 410 neural network, 704 output, [0007] lines 14-15, execute the neural network to process the media data and generate the particular media data output; [0112] lines 1-12, The address 604 can include, for example, a uniform resource identifier (URI), a link, a path, a resource locator, or any other address such as a network, storage or destination address. The address 604 indicates where the rendering engine model 606 is located and/or how it can be retrieved. When the media processing system 102 processes the media item 602 (e.g., as described in flow 300 illustrated in FIG. 3A), it can use the address 604 to retrieve the rendering engine model 606 from the remote system 140; [0109] lines 3-12, The rendering engine model 606 can be a particular rendering engine model capable of rendering the media data 210. In this example, the rendering engine model 606 is stored in the remote system 140. The remote system 140 can store one or more rendering engine models 606, 230, 240 for the media data 210 (and other media data), which can be access by the media processing system 102 (and any other device) to process and render media data).
In addition, Moreno teaches compute the data read in the common data storage area, (Moreno, Fig. 3, 304 memory, data units (as common data storage area in the memory); [0027] lines 1-4, A data unit 202 describes an input data embedded in a smart contract. There may be a plurality of data units 202. A data unit 202 may be received from a data provider 204 and stored in a memory device or storage device; [0029] lines 1-12, Machine learning algorithms 206 are used to generate machine learning models 208. There may be new or existing machine learning models. A machine learning algorithm, for example, trains a machine learning model, for example, based on a data set contained or referenced in a data unit 202. A machine learning model trained by running a machine learning algorithm or technique may include parameters or weights which are generated during the training phase. A machine learning model takes data units as input (e.g., input feature vectors) in order to produce an outcome such as a prediction, classification, and/or another outcome):
preprocessing the data and inputting the preprocessed data into the invoked neural network models for computation (Moreno, Fig. 1, input to each invoked Model 116-122 (as invoked neural network for computation); [0020] lines 1-15, the data platform determines valuable data units for a type of machine learning algorithm by feeding different combinations of data units into separate machine learning instances. For instance, each combination of data units produces a different training set. (as preprocessing the data); A machine learning instance refers to a machine learning algorithm or architecture set up to build a machine learning model based on a training data set. The data platform in some embodiments evaluates the various training sets against an objective function (such as an expected shape), verifies the correspondence between input data units and outcomes of each machine learning model, and infers which data units provide the best value, for example, to a user of that machine learning model, for example, executed on the data platform).
As per claim 6, HWANG, Moreno, SHI and Afzal teach the invention according to claim 1 above. Moreno further teaches wherein training performed to the pre-trained neural network models comprises: constructing a neural network (Moreno, Fig. 1, input to each invoked Model 116-122; [0002] lines 7-8, builds and/or uses machine learning models; [0048] The dataset held or referenced in the data units may be loaded onto the memory 304 for use in training the machine learning models. The trained models may be loaded on the memory 304), selecting a training data set (Moreno, [0034] lines 7-20, each machine learning algorithm may train a model having architecture or structure associated with the machine learning algorithm based on a set of training data. Responsive to a machine learning algorithm being selected, the data platform 210 may select a set of data providers 204. For instance, the data platform 210 may select data providers that provide the type of data used by the selected machine learning algorithm. For example, the data platform 210 may determine or select a data provider, for example, by retrieving the information in the catalog or registry, which specifies data providers, associated type of data offered by the data providers, and machine learning algorithms or types of machine learning algorithms that can use the data offered by the data providers), and training the constructed neural network using the selected training data set, and verifying the trained neural network (Moreno, [0020] lines 1-15, the data platform determines valuable data units for a type of machine learning algorithm by feeding different combinations of data units into separate machine learning instances. For instance, each combination of data units produces a different training set; A machine learning instance refers to a machine learning algorithm or architecture set up to build a machine learning model based on a training data set. The data platform in some embodiments evaluates the various training sets against an objective function (such as an expected shape), verifies the correspondence between input data units and outcomes of each machine learning model, and infers which data units provide the best value, for example, to a user of that machine learning model, for example, executed on the data platform).
As per claim 9, it is a computer device claim of claim 1 above. Therefore, it is rejected for the same reason as claim 1 above. In addition, HWANG further teaches A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor, when executing the computer program, implements (HWANG, Fig. 9, 915 memory, 910 processor; [0156] lines 1-10, Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions).
As per claim 10, it is a non-transitory computer-readable storage medium claim of claim 1 above. Therefore, it is rejected for the same reason as claim 1 above.
As per claims 11-13 and 15, they are computer device claims of claims 2-4 and 6 respectively above. Therefore, they are rejected for the same reasons as claims 2-4 and 6 respectively above.
As per claim 16-18 and 20, they are storage medium claims of claims 2-4 and 6 respectively above. Therefore, they are rejected for the same reasons as claims 2-4 and 6 respectively above.
Claims 5, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over HWANG, Moreno, SHI and Afzal, as applied to claims 4, 13 and 18 respectively above, and further in view of Ross et al. (US Patent. 10,685,295 B1).
Ross was cited in the previous Office Action.
As per claim 5, HWANG, Moreno, SHI and Afzal teach the invention according to claim 4 above. Moreno teaches the step of inputting the preprocessed data into the invoked neural network models for computation (Moreno, Fig. 1, input to each invoked Model 116-122 (as invoked neural network for computation); [0020] lines 1-15, the data platform determines valuable data units for a type of machine learning algorithm by feeding different combinations of data units into separate machine learning instances. For instance, each combination of data units produces a different training set. (as preprocessing the data); A machine learning instance refers to a machine learning algorithm or architecture set up to build a machine learning model based on a training data set. The data platform in some embodiments evaluates the various training sets against an objective function (such as an expected shape), verifies the correspondence between input data units and outcomes of each machine learning model, and infers which data units provide the best value, for example, to a user of that machine learning model, for example, executed on the data platform).
HWANG, Moreno, SHI and Afzal fail to specifically teach configuring corresponding hardware resources according to network structures of the corresponding neural network models; and computing the preprocessed data based on the corresponding hardware resources.
However, Ross teaches configuring corresponding hardware resources according to network structures of the corresponding neural network models (Ross, Abstract, lines 1-9, allocating resources for a machine learning model is disclosed. A machine learning model to be executed on a special purpose machine learning model processor is received. A computational data graph is generated from the machine learning model. The computational dataflow graph represents the machine learning model which includes nodes, connector directed edges, and parameter directed edges (as network structures); Col 1, lines 55-59, compiling a representation of a machine learning model using deterministic instruction set architecture, a processing system can determine, at compile-time, the amount of resources that a machine learning model will use; Col 2, lines 24-27, By knowing the resource use of each machine learning model, the data center can adjust resources, and efficiently schedule machine learning models to load-balance models automatically); and
computing the preprocessed data based on the corresponding hardware resources (Ross, Col 1, line 66 – Col 2, line 7, determines the amount of resources that a machine learning model will use during execution and then uses this determination to allocate resources for a single machine learning model on a special purpose machine learning model processor or in a datacenter. The processing system can allocate different machine learning models for a particular machine learning task or allocate resources for machine learning models executing different tasks; please note: computing the preprocessed data was taught by Moreno).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of HWANG, Moreno, SHI and Afzal with Ross because Ross’s teaching of adjusting/configuring the resources according to the network structure of the machine learning model and computing the data based on the allocated resources would have provided HWANG, Moreno, SHI and Afzal’s system with the advantage and capability to efficiently allocating the resources to the machine learning model based on its structure in order to improving the resource utilization and system performance.
As per claim 14, it is a computer device claim of claim 5 above. Therefore, it is rejected for the same reason as claim 5 above.
As per claim 19, it is a storage medium claim of claim 5 above. Therefore, it is rejected for the same reason as claim 5 above.
Response to Arguments
In the remark Applicant’s argue in substance:
(a), in the present application, there are a model storage area and a common data storage area in the same memory, which are respectively used to store pre- trained neural network models and data to be processed…Hwang's solution is an embedded rendering engine, which addresses compatibility issues in media rendering…Therefore, the present application and Hwang take different technical measures to solve different technical problems, and thus are totally and essentially different technical solutions.
(b), In contrast, according to Hwang, raw media data (images/videos/audio) and a rendering engine model are embedded in a media item, making the media item a unified container of "data + model"…It can be seen that in Hwang, by generating a media item, the relationship between "raw media data" and "rendering engine model" is bound or fixed, that is, for any type of "raw media data", there is a corresponding "rendering engine model". Therefore, it is unnecessary to divide specific areas in the memory for storing "raw media data" and "rendering engine model"…according to Hwang, there is no need to divide specific areas in the memory for storing "raw media data" and "rendering engine model". Furthermore, for the remote model invocation mode, it is impossible to store "raw media data" and "rendering engine model" in the same memory… even if it is assumed that Moreno discloses dividing areas in memory for storing data and models, a person of ordinary skill in the art would have no motivation to combine such a partitioning scheme with Hwang's unified media item architecture, as doing so would require a fundamental redesign of Hwang's 'plug-and- play' container system.
(c), it is not clear from the record how Moreno's teaching of dividing memory areas specifically for storing data and models could be incorporated into the Hwang device. Under such a modification, the media item container of Hwang would have to be disassembled to separate the "raw media data" from the "rendering engine model" into discrete, managed memory partitions. However, Hwang's basic principle of operation is to provide a unified, "format-independent" container that eliminates the need for such local memory management. Such modification would require a substantial reconstruction or redesign of the elements of the Hwang device, and/or would change the basic principle of operation of the Hwang device. There is no evidence that a person of ordinary skill in the art would be motivated to perform such changes and redesign.1 Furthermore, it is not clear from the record whether such modification would actually improve the utilization rate of hardware resources, as the overhead of disassembling Hwang's unified media items into managed memory partitions could potentially decrease system efficiency.
(d), there is no substantial evidence, nor clear and particular evidence, within the record of motivation for modifying the Hwang device by incorporating Moreno's memory partitioning. Without such motivation and absent improper hindsight reconstruction,
a person of ordinary skill in the art would not be motivated to perform the proposed modification.
Examiner respectfully disagreed with Applicant’s argument for the following reasons:
As to point (a), Examiner would like to remind applicant that the rejection is based on 103 rejection using multiple references. The concept of “a model storage area and a common data storage area in the same memory” was taught by Moreno (see 103 rejection above). To the extent that applicants are arguing against the references individually, the examiner reminds the applicants that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As to point (b), Again, applicant is attacking the reference individually without considering Moreno reference. In addition, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, examiner has clearly established that by combining the feature of Moreno with HWANG, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of HWANG with Moreno because Moreno’s teaching of utilizing the data that is stored in the memory to train the machine learning model in the memory to produce the output would have provided HWANG’s system with the advantage and capability to allow the system to build an accurate machine learning model in order to improving the system performance and efficiency (see Moreno, [0003] “building of an accurate machine learning model, that data set should be recognized as needing improvement”).
As to point (c), Please see point (a) to (b) above. Again, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
As to point (d), In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
For the reasons above, Applicant’s argument has not been found to be persuasive, and therefore the rejections are maintained.
Conclusion
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/ZUJIA XU/Examiner, Art Unit 2195
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