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 . Claims 6 and 18 are canceled. Claims 1-5, 7-17 and 19-22 remain pending in the application.
Information Disclosure Statement
The information disclosure statement submitted on 01/08/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 1-3 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over COULL et al. (US 20210073377 A1) hereinafter Coull in view of Gur et al. (US 20210019665 A1) hereinafter Gur.
As to independent claim 1, Coull teaches a transfer learning method for a system, the system comprising: [transfer learning system ¶18]
a plurality of existing agents each including a trained machine learning model for modelling a respective existing machine learning scenario; [compute devices (agents) fig. 2 210-214) hosting a model for its domain (scenario) ¶22 " ML models are generated at some or all of compute devices 210-214"]
a new agent different from the existing agents; and, [Compute device Fig. 2 216 ¶22 " a need is identified at compute device 216 for a trained ML model for the new domain “D,”"]
a database comprising: [model repository (DB) Fig. 2 215 ¶22]
a plurality of available machine learning models for modelling machine learning scenarios, including the plurality of trained machine learning models; [Models are in the model repository ¶24]
existing scenario metadata [domain values (metadata) such as syntax, semantics, file types, OS (features) ¶15]
transfer learning data indicative of parts of the trained machine learning models associated with respective features of the existing machine learning scenarios, [trainable layers (parts) and second dataset ¶3 overlap between features ¶17 "the selection of the at least one trainable layer can be based on one or more properties of the trainable layers from the plurality of trainable layers, e.g., the number of low-level features included in or represented by those layers, the number of high-level features included in or represented by those layers, an overlap between features of those layers and features associated with the domain of interest, a degree of similarity between features of those layers and features associated with the domain of interest, etc"]
the method being performed by the new agent, implemented on one or more computer processors, and comprising steps of: , [Compute device Fig. 2 216 ¶22 " a need is identified at compute device 216 for a trained ML model for the new domain “D,”"]
receiving new scenario metadata indicative of one or more features of a new machine learning scenario to be modelled by the new agent; [receives from model repository ¶22 "retrieve, or receive in response to a query of the trained ML model repository 215, and as part of a transfer learning process, one or more of the trained ML models stored in the trained ML model repository 215"]
receiving new scenario training data for training a machine learning model to model the new machine learning scenario; [second dataset ¶3, compares properties for new model to gets datasets ¶22-23]
querying the database to select one of the available machine learning models to be used by the new agent to model the new machine learning scenario, the selection being based on the received new scenario metadata and the existing scenario metadata; [query ¶22-23 "the compute device 216 can retrieve, or receive in response to a query of the trained ML model repository 215, and as part of a transfer learning process, one or more of the trained ML models stored in the trained ML model repository 215"]
querying the database to select at least some of the transfer learning data to be used by the new agent to train the selected machine learning model, the selection being based on the received new scenario metadata and the existing scenario metadata; and, [selects based on similarity and overlap between datasets (metadata) ¶22-23 "Upon receipt at compute device 216 of the one or more trained ML models from the trained ML model repository 215, the compute device 216 can compare each of the one or more trained ML models with properties of the new domain “D” to identify one or a subset of the one or more “candidate” trained ML models that is most similar to, that best matches with, that at least partially overlaps with, or that most overlaps with, the one or more datasets associated with the new domain “D"}
training the selected machine learning model using the received new scenario training data and using the selected transfer learning data. [trains accordingly for new scenario ¶22-23 "produce a trained ML model for the new domain “D.”"]
Coull does not specifically teach existing scenario metadata indicative of one or more features of the existing machine learning scenarios;
However, Gur teaches existing scenario metadata indicative of one or more features of the existing machine learning scenarios; and, [¶40 " ML repository 130 as a ML metadata model comprising a description of the aggregate collections upon which the ML model trained by the ML algorithm operates"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Coull by incorporating the existing scenario metadata indicative of one or more features of the existing machine learning scenarios; disclosed by Gur because both techniques address the same field of machine learning and by incorporating Gur into Coull helps manage learning models, datasets and versioning [Gur ¶19]
As to dependent claim 2, the rejection of claim 1 is incorporated, Coull and Gur further teach wherein querying the database to select one of the available machine learning models comprises:
transmitting the new scenario metadata to the database and, in response, receiving the selected machine learning model from the database; or, [Gur registers model in repository ¶36 "The trained ML model may then be registered, by the trained ML model registration logic 120, in a ML model repository 140"]
receiving the plurality of available machine learning models and existing scenario metadata from the database, and comparing the new scenario metadata against the existing scenario metadata to select one of the available machine learning models. [Coull compares for selection including overlap/similarity ¶22-23]
As to dependent claim 3, the rejection of claim 1 is incorporated, Coull and Gur further teach wherein querying the database to select at least some of the transfer learning data comprises:
transmitting the new scenario metadata to the database and, in response, receiving the selected transfer learning data from the database; or, [Gur metadata in repository ¶40 " The ML index 132 represents an ML algorithm registered in the ML repository 130 as a ML metadata model comprising a description of the aggregate collections upon which the ML model trained by the ML algorithm operates"]
receiving the transfer learning data and existing scenario metadata from the database, and comparing the new scenario metadata against the existing scenario metadata to select at least some of the transfer learning data. [Coull compares for selection including overlap/similarity ¶22-23].
As to dependent claim 21, the rejection of claim 1 is incorporated, Coull and Gur further teach a non-transitory, computer-readable storage medium storing instructions thereon that when executed by one or more computer processors cause the computer processors to execute the method of claim 1. [Coull medium, processing and instructions ¶36-37]
As to dependent claim 22, the rejection of claim 1 is incorporated, Coull and Gur further teach a computing device comprising one or more processors configured to perform the method of claim 1. [Coull devices and processor ¶25]
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Gur, as applied in the rejection of claim 1 above, and further in view of Wang et al. (US 20220366269 A1) hereafter Wang.
As to dependent claim 4, Coull and Gur teach the method of claim 1 above that is incorporated,
Coull and Gur do not specifically teach wherein the database comprises a graph structure linking the existing scenario metadata to the available machine learning models, and wherein the new scenario metadata is compared against the graph structure to select one of the available machine learning models.
However, Wang teaches wherein the database comprises a graph structure linking the existing scenario metadata to the available machine learning models, and wherein the new scenario metadata is compared against the graph structure to select one of the available machine learning models. [knowledge graph representing features and uses/traverses graph to select features ¶47 " knowledge graph can be traversed to find a concept or node (not mapped to a feature of the dataset), which is connected to a node that is mapped to a feature in the dataset"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Gur by incorporating the wherein the database comprises a graph structure linking the existing scenario metadata to the available machine learning models, and wherein the new scenario metadata is compared against the graph structure to select one of the available machine learning models disclosed by Wang because all techniques address the same field of machine learning and by incorporating Wang into Coull and Gur further automates feature selection processes saving time and effort [Wang ¶2, ¶62]
As to dependent claim 5, the rejection of claim 5 is incorporated, Coull, Gur and Wang further teach wherein the graph structure comprises:
a plurality of nodes each representing an entity associated with the existing scenarios; and, [Wang nodes for entities ¶31]
a plurality of edges linking pairs of the entities and representing features of the existing scenarios, [Wang edge relationships ¶31]
wherein the new scenario metadata is associated with one or more of the nodes by a comparison of features of the new scenario metadata against features represented by the respective edges in order to select one of the available machine learning models. [Wang graph used to select features for training model via mapping ¶9]
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Gur, as applied in the rejection of claim 1 above, and further in view of Nagaraju et al. (US 20200012966 A1) hereinafter Nagaraju.
As to dependent claim 7, Coull and Gur teach the method of claim 1 above that is incorporated, Coull and Gur further teach wherein selecting at least some of the transfer learning data comprises selecting one or more of the subsets of data based on a comparison between features of the existing and new scenarios. [Coull compares for selection including overlap/similarity ¶22-23]
Coull and Gur do not specifically teach wherein the transfer learning data comprises subsets of data each associated with a respective event of the existing scenarios.
However, Nagaraju teaches wherein the transfer learning data comprises subsets of data each associated with a respective event of the existing scenarios, [events for data items used by ML algorithm updates ¶105-106 "store events, as detailed above, which can be searched by the search head 38 to extract data items. Further, the extracted data items can be used by the machine learning algorithms 54 to update/train the global model 22."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Gur by incorporating the wherein the transfer learning data comprises subsets of data each associated with a respective event of the existing scenarios disclosed by Nagaraju because all techniques address the same field of machine learning and by incorporating Nagaraju into Coull and Gur alleviates the resources required by edge devices and improves model predictions [Nagaraju ¶20].
As to dependent claim 8, the rejection of claim 7 is incorporated, Coull, Gur and Nagaraju further teach wherein each subset of data comprises at least one of:
hyperparameters of the trained machine learning models associated with the respective event; [gur model parameters ¶40]
neural network weights of the trained machine learning models associated with the respective event; and, [Coull weights ¶16]
training data used to train the trained machine learning models and associated with the respective event. [Nagaraju event training data ¶105-106]
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Gur, as applied in the rejection of claim 1 above, and further in view of Pezzillo et al. (US 20190370687 A1) hereinafter Pezzillo.
As to dependent claim 9, Coull and Gur teach the method of claim 1 above that is incorporated.
Coull and Gur do not specifically teach wherein the system is implemented in a communications network comprising a plurality of edge computing devices and at least one cloud-based computing device, wherein each of the plurality of existing agents and the new agent are implemented in respective edge computing devices and the database is implemented in the at least one cloud-based computing device.
However, Pezzillo teaches wherein the system is implemented in a communications network comprising a plurality of edge computing devices and at least one cloud-based computing device, wherein each of the plurality of existing agents and the new agent are implemented in respective edge computing devices and the database is implemented in the at least one cloud-based computing device. [machine learning in the cloud ¶3 edge devices connected to a manager with repository ¶19-20 "register the ML models with the ML model manager 108, uploading the ML models to a model repository managed by the ML model manager 108"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Gur by incorporating the wherein the system is implemented in a communications network comprising a plurality of edge computing devices and at least one cloud-based computing device, wherein each of the plurality of existing agents and the new agent are implemented in respective edge computing devices and the database is implemented in the at least one cloud-based computing device disclosed by Pezzillo because all techniques address the same field of machine learning and by incorporating Pezzillo into Coull and Gur better leverages computing resources for improved model performance [Pezzillo ¶3].
Claims 10-11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros (US 20200134374 A1).
As to independent claim 10, Coull teaches a transfer learning method for a system comprising a plurality of agents each implemented on one or more computer processors and each including a trained machine learning model for modelling a respective machine learning scenario, the method being performed by a first agent of the plurality of agents and comprising steps of: [compute devices (agents) fig. 2 210-214) for transfer learning ¶22]
receiving new training data different from previous training data used to train the trained machine learning model currently deployed by the first agent; [receive second dataset ¶3]
retraining the currently-deployed machine learning model using at least the received new training data; and,[trains via transfer learning and second dataset ¶3 " A second trained machine learning model is generated, via a transfer learning process, using (1) at least one trainable layer from the multiple trainable layers of the first trained machine learning model, and (2) a second dataset different from the first dataset"]
identifying transfer learning data, transfer learning data being data associated with the retrained machine learning model that is not associated with the currently-deployed machine learning model; and, [datasets associated with a new domain for transfer learning and layer data ¶17 " At least one trainable layer from the plurality of trainable layers of the fully-trained extra-domain machine learning model is used in the training of a machine learning model for the domain of interest"]
transmitting the identified transfer learning data to a database of the system, the database being accessible by second agents, different from the first agent, of the plurality of agents to retrieve the transmitted data for use in retraining the respective trained machine learning models of the second agents. [network for access by all Fig. 2 220 the database Fig. 2 215 that stores models for computing devices ¶22-24 " trained ML models from each of the compute devices 210-214 are described, with reference to FIG. 2, as being stored in the trained ML model repository 215, alternatively or in addition, some or all of the trained ML models generated anywhere within the networked system 200 can be stored at any or all of the compute devices 210-216. For example, the domain D compute device 216 may store trained ML models generated by some or each of compute devices 210-214 without retrieving those trained ML models from the trained ML model repository 215."]
Coull does not specifically teach comparing the retrained machine learning model against the currently-deployed machine learning model to determine whether to deploy the retrained machine learning model, wherein, if it is determined to deploy the retrained machine learning model.
However, Oros teaches comparing the retrained machine learning model against the currently-deployed machine learning model to determine whether to deploy the retrained machine learning model, wherein, if it is determined to deploy the retrained machine learning model [Compares retrained model to threshold when deploying ¶33 "When the performance of the retrained AWL model meets expectations (e.g., achieves a 90% accuracy), exceeds performance of the currently executing version, or both, the retrained AI/ML model may be automatically deployed (i.e., updated) without user interaction."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Coull by incorporating the comparing the retrained machine learning model against the currently-deployed machine learning model to determine whether to deploy the retrained machine learning model, wherein, if it is determined to deploy the retrained machine learning model disclosed by Oros because both techniques address the same field of machine learning and by incorporating Oros into Coull improves approaches to updating models for better predictions [Oros ¶2-3]
As to dependent claim 11, the rejection of claim 10 is incorporated, Coull and Oros further teach wherein comparing the retrained and currently-deployed machine learning models comprises determining one or more metrics indicative of performance of the retrained model and comparing against corresponding metrics of the currently-deployed model. [Oros accuracy ¶33]
As to dependent claim 15, the rejection of claim 10 is incorporated, Coull and Oros further teach wherein retraining the trained machine learning model comprises using at least some of the previous training data. [Coull smaller subset ¶20]
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros, as applied in the rejection of claim 10 above, and further in view of Frtunikj et al. (US 20220164602 A1) hereinafter Frtunikj.
As to dependent claim 12, Coull and Oros teach the method of claim 10 above that is incorporated. Coull and Oros further teach wherein identifying transfer learning data comprises identifying data in the received new training data that is different from the previous training data, [Coull incompatibility (different) ¶17]
Coull and Oros do not specifically teach wherein the identified different data is identified as transfer learning data if a difference between the identified different data and the previous training data is greater than a prescribed threshold.
However, Frtunikj teaches wherein the identified different data is identified as transfer learning data if a difference between the identified different data and the previous training data is greater than a prescribed threshold. [underrepresented are greater than a confidence score ¶35 " construction labels are underrepresented in the existing training dataset (class data distribution in FIG. 3A), and there is an object in the image classified as construction; (2) the object detection model is uncertain with respect to the classification of the orange scooter (low confidence score of 0.6 for classifying it as a construction). Therefore, the importance function will assign a high scores to the data log with respect to the features label class count and confidence score. This is because if the training dataset included more construction labels, and more objects that look like construction labels but not in fact construction labels (i.e. the orange scooter which is a false positive construction label), then the detection algorithm would be less likely to mistake orange objects as construction cones in the future."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Oros by incorporating the wherein the identified different data is identified as transfer learning data if a difference between the identified different data and the previous training data is greater than a prescribed threshold disclosed by Frtunikj because all techniques address the same field of machine learning and by incorporating Frtunikj into Coull and Oros reduces the amount of data required for learning and finds useful data for training [Frtunikj ¶3]
Claims 13 is rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros, as applied in the rejection of claim 10 above, and further in view of DU et al. (US 20200125938 A1) hereinafter Du.
As to dependent claim 13, Coull and Oros teach the method of claim 10 above that is incorporated. Coull and Oros further teach wherein identifying transfer learning data comprises identifying model weights of the retrained machine learning model different from corresponding model weights of the currently-deployed machine learning model, and [Coull weights ¶16]
Coull and Oros do not specifically teach wherein the identified model weights are identified as transfer learning data if a change in the identified model weights caused by retraining is greater than a prescribed threshold.
However, Du teaches wherein the identified model weights are identified as transfer learning data if a change in the identified model weights caused by retraining is greater than a prescribed threshold. [Gradient value is weight and used for threshold ¶62 "determine whether to send gradients and data to be operated to the operation module according to a gradient determination condition. Alternatively, the gradient determination condition may include a threshold determination condition or a function mapping determination condition. Alternatively, the gradient determination condition may include: being less than a given threshold, being greater than a given threshold, being within a given value range, or being outside a given value range."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Oros by incorporating the wherein the identified model weights are identified as transfer learning data if a change in the identified model weights caused by retraining is greater than a prescribed threshold disclosed by Du because all techniques address the same field of machine learning and by incorporating Du into Coull and Oros accelerate the training processes while conserving energy consumption [Du ¶2-3].
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros, as applied in the rejection of claim 10 above, and further in view of Karpathy (US 20210271259 A1).
As to dependent claim 14, Coull and Oros teach the method of claim 10 above that is incorporated.
Coull and Oros do not specifically teach transmitting scenario metadata to the database along with the identified transfer learning data, the scenario metadata being indicative of a feature of the machine learning scenario being modelled by the first agent associated with the identified transfer learning data.
However, Du teaches transmitting scenario metadata to the database along with the identified transfer learning data, the scenario metadata being indicative of a feature of the machine learning scenario being modelled by the first agent associated with the identified transfer learning data. [teaches sensor and metadata like location for a server to create a dataset ¶29 "data and metadata are transmitted to a computer data server where it is used for creating a new training data set"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Oros by incorporating the transmitting scenario metadata to the database along with the identified transfer learning data, the scenario metadata being indicative of a feature of the machine learning scenario being modelled by the first agent associated with the identified transfer learning data disclosed by Karpathy because all techniques address the same field of machine learning and by incorporating Karpathy into Coull and Oros alleviates issues with collecting data and resource use [Karpathy ¶4]
Claims 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros.
As to independent claim 16, Coull teaches a transfer learning method for a system comprising a plurality of agents each implemented on one or more computer processors and each including a trained machine learning model for modelling a respective machine learning scenario, the method being performed by a first agent of the plurality of agents and comprising steps of: [compute devices (agents) fig. 2 210-214) for transfer learning ¶22 " transfer learning system, according to an embodiment. The system 200 of FIG. 2 includes multiple compute devices each associated with a different domain"]
querying a database of the system to obtain transfer learning data, the transfer learning data being data not associated with the trained machine learning model that is currently deployed by the first agent; [query ¶22-23 "the compute device 216 can retrieve, or receive in response to a query of the trained ML model repository 215, and as part of a transfer learning process, one or more of the trained ML models stored in the trained ML model repository 215"]
updating the trained machine learning model of the first agent using the obtained transfer learning data; and, [trains accordingly for new scenario ¶22-23 "produce a trained ML model for the new domain “D.”"]
Coull does not specifically teach comparing the updated machine learning model against the currently-deployed machine learning model, and determining whether to deploy the updated machine learning model based on the comparison.
However, Oros teaches comparing the updated machine learning model against the currently-deployed machine learning model, and determining whether to deploy the updated machine learning model based on the comparison. [Compares retrained model to threshold when deploying ¶33 "When the performance of the retrained AWL model meets expectations (e.g., achieves a 90% accuracy), exceeds performance of the currently executing version, or both, the retrained AI/ML model may be automatically deployed (i.e., updated) without user interaction."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the training features by Coull by incorporating the comparing the updated machine learning model against the currently-deployed machine learning model, and determining whether to deploy the updated machine learning model based on the comparison disclosed by Oros because both techniques address the same field of machine learning and by incorporating Oros into Coull improves approaches to updating models for better predictions [Oros ¶2-3]
As to dependent claim 17, the rejection of claim 16 is incorporated, Coull and Oros further teach wherein the obtained transfer learning data is new training data different from previous training data used to train the currently-deployed machine learning model, and wherein updating the trained machine learning model comprises retraining the trained machine learning model to obtain the updated machine learning model. [Coull smaller subset for retraining ¶20-21]
As to dependent claim 20, the rejection of claim 16 is incorporated, Coull and Oros further teach wherein comparing the updated and currently-deployed machine learning models comprises determining one or more metrics indicative of performance of the updated model and comparing against corresponding metrics of the currently-deployed model. [Oros accuracy metric ¶33]
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Coull in view of Oros, as applied in the rejection of claim 16 above, and further in view of Nagaraju et al. (US 20200012966 A1) hereinafter Nagaraju.
As to dependent claim 19, Coull and Oros teach the method of claim 16 above that is incorporated,
Coull and Oros do not specifically teach wherein the obtained transfer learning data is one or more updated parameters of the machine learning model different from corresponding parameters of the currently-deployed machine learning model, and wherein updating the trained machine learning model comprises replacing one or more parameters in the trained machine learning model with the corresponding received updated parameters.
However, Nagaraju teaches wherein the obtained transfer learning data is one or more updated parameters of the machine learning model different from corresponding parameters of the currently-deployed machine learning model, and wherein updating the trained machine learning model comprises replacing one or more parameters in the trained machine learning model with the corresponding received updated parameters. [updated parameter values (replaces) ¶126 "the local model is replaced with the global model or updated with parameter values that modify the local model accordingly."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning pipeline disclosed by Coull and Oros by incorporating the wherein the obtained transfer learning data is one or more updated parameters of the machine learning model different from corresponding parameters of the currently-deployed machine learning model, and wherein updating the trained machine learning model comprises replacing one or more parameters in the trained machine learning model with the corresponding received updated parameters disclosed by Nagaraju because all techniques address the same field of machine learning and by incorporating Nagaraju into Coull and Oros alleviates the resources required by edge devices and improves model predictions [Nagaraju ¶20].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Chen et al. (US 20220172038 A1) teaches AutoML with transfer learning (see ¶35)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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