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 .
Claim Objections
Claim 20 objected to because of the following informalities:
The claim recites “wherein one or more neural network models perform inference on the scene graph to determining that the scene graph fails and to generate the final task plan”, which contains grammatical/typographical errors. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Francis et al (US 20240104308, hereinafter Francis).
Regarding Claim 19, Francis teaches:
a method performed by a computing device (see at least method in par. 0004) , the method comprising:
accessing a scene graph having a hierarchical structure of nodes and connections therebetween that represent the hierarchical structure of a space, (see at least "The systems and methods described herein may be configured to, when the agent needs to find an object, calculate, using the agent, a statistical distribution of belief over the set of least-likely to most-likely regions where that object might be found (e.g., by leveraging object-region relationships captured by a knowledge graph and a pre-train visual classifier)." in par. 0026 and “The systems and methods described herein may be configured to answer these questions using the appropriate external knowledge resources that encode object-object, object-region, region-region relationships, and/or the like. The responses to these self-talk queries (e.g., cars are located in garages, cars are located in the driveway, cars are located on streets) indicate intermediate navigation sub-goals for the agent (e.g., navigate to the garage, navigate to the driveway, navigate to the street).” In par. 0028 and “The systems and methods described herein may be configured to generate a custom domain knowledge-graph, which captures the regions and objects that the agent is likely to encounter. The systems and methods described herein may be configured to extend this knowledge graph, using public resources, to obtain additional connections to other likely objects and regions. The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding.” In par. 0029)
the nodes representing respective objects or spaces in the space, wherein images of the objects or spaces are respectively associated with the nodes thereof; (see at least “The systems and methods described herein may be configured to generate a custom domain knowledge-graph, which captures the regions and objects that the agent is likely to encounter. The systems and methods described herein may be configured to extend this knowledge graph, using public resources, to obtain additional connections to other likely objects and regions. The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding.” In par. 0029 and " The at least one functional map may correspond to a neural functional approximator and/or may correlate one or more maps associated with one or more image inputs with corresponding region and object labels. " in par. 0058)
determining that the scene graph fails to produce a task plan for completion of a task by an agent within the space; (see at least "The episodic rewards may be provided to the ego-agent by the environment, based on the degree of task completion (e.g., navigation to a desired location or accuracy in answering the question)." in par. 0031 and “At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q.sub.0, the episodic reward, and relevant keywords from the most successful sub-goal.” In par. 0032 and “In the example, the machine-learning model 210 may process raw source data 216 and output an answer to a question. The machine-learning model 210 may generate a confidence level (e.g., a certainty value) or factor for each output generated. For example, a confidence value that exceeds a predetermined high-confidence threshold may indicate that the machine-learning model 210 is confident that the answer prediction will result in a desired answer. A confidence value that is less than a low-confidence threshold may indicate that the machine-learning model 210 has some uncertainty that the answer prediction will result in the desired answer.” In par. 0056)
based on the determining, identifying a node, among the nodes, that is relevant to performing the task; (see at least " For t=t.sub.i, the ego-agent interacts with a self-talk agent, which may leverage a knowledge-graph or a pre-trained language model. The self-talk agent receives queries (e.g., which may be referred to herein as sub-questions) from the ego-agent (e.g., such as “Where are cars located?”) and provides responses in accordance with explicit or implicit knowledge (KBs: [cars, at Location, driveway]”; LMs: [garage, driveway, street]) of the self-talk agent. Based on these responses (e.g., knowledge-grounded context), the ego-agent may generate sub-goal specifications, which aid in interaction with the environment. The ego-agent may retrieve from a memory module M, which stores keywords that were relevant to questions similar to initial question Q.sub.0." in par. 0032)
based on the identification of the node, extracting additional data about the space from the image associated with the identified node; (see at least “The systems and methods described herein may be configured to use an agent, which may interact with either a simulated or real environment (e.g., through navigating and/or exploring the environment, manipulating objects, communicating with other agents in the environment), in order to collect sufficient world knowledge for answering questions or engaging in grounded dialogue.” In par. 0023 and “The systems and methods described herein may be configured to, when asked questions about unseen objects, use the agent to leverage commonsense and spatial knowledge of the agent for reasoning about properties and locations of the new objects. For example, if the agent is spawned in a bedroom and asked the question, “Where is the television located?”, the agent may not navigate the environment to explore, but can rely of the knowledge of object properties and locations and can offer initial predictions of {bedroom, living room, den}, instead.” In par. 0030 and " The at least one action prediction may include an action to navigate at least a portion of the environment associated with the machine-learning model 210 and/or other suitable action. The system 200 may predict any suitable number of actions for traversing the environment and/or for answering the question." in par. 0058) and
generating a final task plan for completion of the task by the agent within the space, the final task plan generated based on the additional data and the scene graph. (see at least “Throughout the interaction of the ego-agent with the environment, π is periodically updated (e.g., from initialization π.sub.0 to some parameterization π.sup.(k)), through policy gradient updates, based on downstream episodic rewards. The episodic rewards may be provided to the ego-agent by the environment, based on the degree of task completion (e.g., navigation to a desired location or accuracy in answering the question).” In par. 0031 “Based on this progress score, the ego-agent uses a natural language generation module ƒgen to generate N clarification questions {Q1, . . . , Qm, . . . QN}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent. At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q0, the episodic reward, and relevant keywords from the most successful sub-goal.” In par. 0032 and "For example, the system 200, using the machine-learning model 210, may generate the at least one sub-goal based on the one or more answers." in par. 0068 and “At 414, the method 400 traverses an environment associated with the first machine-learning model based on the at least one sub-goal.” 0069)
Regarding Claim 20, Francis teaches:
The method of claim 19,
wherein one or more neural network models perform inference on the scene graph to determining that the scene graph fails and to generate the final task plan, and wherein the agent comprises a robot. (see at least " The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding. This state embedding is to be used as an input to downstream components of the machine-learning model associated with the agent, such as the navigation/manipulation policy." in par. 0029 and “At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q0, the episodic reward, and relevant keywords from the most successful sub-goal.” In par. 0032 and “In the example, the machine-learning model 210 may process raw source data 216 and output an answer to a question. The machine-learning model 210 may generate a confidence level (e.g., a certainty value) or factor for each output generated. For example, a confidence value that exceeds a predetermined high-confidence threshold may indicate that the machine-learning model 210 is confident that the answer prediction will result in a desired answer. A confidence value that is less than a low-confidence threshold may indicate that the machine-learning model 210 has some uncertainty that the answer prediction will result in the desired answer.” In par. 0056)
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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Francis et al (US 20240104308, hereinafter Francis) in view of Chen et al (US 20260186488, hereinafter Chen).
Regarding Claim 1, Francis teaches:
an electronic device (see at least "The system 200 may include at least one computing system 202. The computing system 202 may include at least one processor 204 that is operatively connected to a memory unit 208." in par. 0044) comprising:
one or more processors (see at least "The system 200 may include at least one computing system 202. The computing system 202 may include at least one processor 204 that is operatively connected to a memory unit 208." in par. 0044) ; and
a memory storing instructions that when executed by the one or more processors cause the electronic device (see at least "The system 200 may include at least one computing system 202. The computing system 202 may include at least one processor 204 that is operatively connected to a memory unit 208." in par. 0044 ) to:
acquire a prompt that describes a task for a robot to perform in a predefined space; (see at least "In some embodiments, the system 200 may, using a machine-learning model, such as the machine-learning model 210, receive input dialog captured by an input mechanism (e.g., such as a microphone, keyboard, and/or any other suitable input mechanism). The input dialog may include a text string corresponding to a question." in par. 0057)
generate, by a first machine-learning-based model, based on a scene graph corresponding to the predefined space and the prompt being inputted thereto, a first task plan; (see at least "In some scenarios, machine-learning model or agent generalization may be aided by the use of external domain knowledge, such as spatial and commonsense knowledge, typically available as external resources in the form of ontologies and knowledge graphs." in par. 0021 and “The systems and methods described herein may be configured to leveraging scene priors. The systems and methods described herein may be configured to, when the agent needs to find an object, calculate, using the agent, a statistical distribution of belief over the set of least-likely to most-likely regions where that object might be found (e.g., by leveraging object-region relationships captured by a knowledge graph and a pre-train visual classifier).” In par. 0026 and “The systems and methods described herein may be configured to extend this knowledge graph, using public resources, to obtain additional connections to other likely objects and regions. The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding. This state embedding is to be used as an input to downstream components of the machine-learning model associated with the agent, such as the navigation/manipulation policy.” In par. 0029)
based on the first task plan not being able to satisfy the task, provide (see at least " For t=t.sub.i, the ego-agent interacts with a self-talk agent, which may leverage a knowledge-graph or a pre-trained language model. The self-talk agent receives queries (e.g., which may be referred to herein as sub-questions) from the ego-agent (e.g., such as “Where are cars located?”) and provides responses in accordance with explicit or implicit knowledge (KBs: [cars, at Location, driveway]”; LMs: [garage, driveway, street]) of the self-talk agent. Based on these responses (e.g., knowledge-grounded context), the ego-agent may generate sub-goal specifications, which aid in interaction with the environment. The ego-agent may retrieve from a memory module M, which stores keywords that were relevant to questions similar to initial question Q.sub.0." in par. 0032)
generate a candidate node by a second machine-learning-based model, based on additional modal data of the relevant node and a node generation request based on the additional modal data being inputted to the second machine-learning-based model; and (see at least “The systems and methods described herein may be configured to use an agent, which may interact with either a simulated or real environment (e.g., through navigating and/or exploring the environment, manipulating objects, communicating with other agents in the environment), in order to collect sufficient world knowledge for answering questions or engaging in grounded dialogue.” In par. 0023 and “The systems and methods described herein may be configured to, when asked questions about unseen objects, use the agent to leverage commonsense and spatial knowledge of the agent for reasoning about properties and locations of the new objects. For example, if the agent is spawned in a bedroom and asked the question, “Where is the television located?”, the agent may not navigate the environment to explore, but can rely of the knowledge of object properties and locations and can offer initial predictions of {bedroom, living room, den}, instead.” In par. 0030 and " The at least one action prediction may include an action to navigate at least a portion of the environment associated with the machine-learning model 210 and/or other suitable action. The system 200 may predict any suitable number of actions for traversing the environment and/or for answering the question." in par. 0058)
generate a second task plan for the robot to perform the task by inputting the candidate node and the prompt to the first machine-learning-based model. (see at least “Throughout the interaction of the ego-agent with the environment, π is periodically updated (e.g., from initialization π.sub.0 to some parameterization π.sup.(k)), through policy gradient updates, based on downstream episodic rewards. The episodic rewards may be provided to the ego-agent by the environment, based on the degree of task completion (e.g., navigation to a desired location or accuracy in answering the question).” In par. 0031 “Based on this progress score, the ego-agent uses a natural language generation module ƒgen to generate N clarification questions {Q1, . . . , Qm, . . . QN}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent. At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q0, the episodic reward, and relevant keywords from the most successful sub-goal.” In par. 0032 and "For example, the system 200, using the machine-learning model 210, may generate the at least one sub-goal based on the one or more answers." in par. 0068 and “At 414, the method 400 traverses an environment associated with the first machine-learning model based on the at least one sub-goal.” 0069)
Francis does not appear to explicitly teach all of the following, but Chen does teach:
provide the first machine- learning-based model with a request to extract a relevant node to the task from among nodes of the scene graph; (see at least “At block 500, the system utilizes the determined object descriptor(s), determined in block 400, in determining robotic skill(s) for robot(s) to implement in performing the task specified in the FF NL instruction of block 400. As described above, the determined object descriptors can be a subset that describe objects that are both relevant to performing a task of an FF NL instruction and likely present in the environment. At block 500, the system can utilize such a subset, without other object descriptor(s), in determining robotic skill(s) for robot(s) to implement in performing the task. This can prevent wastefully considering robotic skill(s) that are specific to object(s) that do not correspond to the object descriptor(s) of the subset and/or wasteful erroneous selection (and implementation) of such robotic skills.” In par. 0052 and “At sub-block 454B, the system prompts an LLM, based on the FF NL instruction, to generate object descriptor(s). The LLM can be distinct from the LLM described in FIG. 5 or can be the same as that described in FIG. 5, but optionally primed and/or prompted differently.” In par. 0070)
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 modified the device taught by Francis to incorporate the teachings of Chen wherein the LLM model is prompted to identify objects in the environment and add them to a scene graph and extract a subset that is relevant to the current task. The motivation to incorporate the teachings of Chen would be to avoid wasting compute resources considering irrelevant objects or skills (see par. 0052)
Regarding Claim 2, Francis as modified by Chen teaches:
the electronic device of claim 1,
Francis further teaches: wherein the candidate node is inserted into the scene graph based on the second task plan being able to satisfy the task. (see at least " Based on this progress score, the ego-agent uses a natural language generation module ƒgen to generate N clarification questions {Q1, . . . , Qm, . . . QN}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent. At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q0, the episodic reward, and relevant keywords from the most successful sub-goal." in par. 0032)
Regarding Claim 3, Francis as modified by Chen teaches:
the electronic device of claim 1,
Francis further teaches: wherein the instructions are further configured to, when executed by the one or more processors, cause the electronic device to
command the robot to perform operations according to the second task plan in response to the second task plan being determined to be able to satisfy the task. (see at least "In the example, the machine-learning model 210 may process raw source data 216 and output an answer to a question. The machine-learning model 210 may generate a confidence level (e.g., a certainty value) or factor for each output generated. For example, a confidence value that exceeds a predetermined high-confidence threshold may indicate that the machine-learning model 210 is confident that the answer prediction will result in a desired answer." in par. 0056)
Regarding Claim 4, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1, wherein the instructions are further configured to, when executed by the one or more processors, cause the electronic device to:
command the robot to search for and capture audio data or image data in response to the second task plan not being able to satisfy the task; and insert the captured audio data or image data into the scene graph. (see at least "The systems and methods described herein may be configured to explore, using the agent, an associated environment to collect sufficient information and/or experience for answering the question correctly" in par. 0024 and “Based on this progress score, the ego-agent uses a natural language generation module ƒgen to generate N clarification questions {Q1, . . . , Qm, . . . QN}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent.” In par. 0032 and “In some embodiments, the one or more image inputs includes one or more of a red-green-blue input, a depth information input, and a semantic segmentation input. In some embodiments, traversing the environment associated with the first machine-learning model based on the at least one sub-goal is further based on at least one downstream episodic reward. In some embodiments, the output mechanism includes at least of a visual output mechanism and an audio output mechanism and wherein the at least one input mechanism includes at least one of a microphone and a keyboard.” In par. 0104)
Regarding Claim 5, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1, wherein the instructions are further configured to, when executed by the one or more processors, cause the electronic device to:
select the candidate node from among multiple generated candidate nodes; and generate a new task plan based on the selected candidate node and apply the new task plan to update the scene graph. (see at least " The ego-agent may retrieve from a memory module M, which stores keywords that were relevant to questions similar to initial question Q0. The similarity function ƒsim compares the sub-goals and the object and/or region labels from current visual context of the ego-agent. A resultant similarity score provides a strong basis for progress-monitoring capabilities, which may be a property that is largely missing from many EVLP solutions. Based on this progress score, the ego-agent uses a natural language generation module ƒgen to generate N clarification questions {Q1, . . . , Qm, . . . QN}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent. At the end of each episode, once the reward is calculated for having successfully completed the EQA task (or not), the agent stores in M the original question Q0, the episodic reward, and relevant keywords from the most successful sub-goal." in par. 0032)
Regarding Claim 6, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1,
wherein the scene graph, in an initial form, comprises levels of nodes, the levels including an object-level level (see at least "The systems and methods described herein may be configured to, when the agent needs to find an object, calculate, using the agent, a statistical distribution of belief over the set of least-likely to most-likely regions where that object might be found (e.g., by leveraging object-region relationships captured by a knowledge graph and a pre-train visual classifier)." in par. 0026 and “The systems and methods described herein may be configured to answer these questions using the appropriate external knowledge resources that encode object-object, object-region, region-region relationships, and/or the like. The responses to these self-talk queries (e.g., cars are located in garages, cars are located in the driveway, cars are located on streets) indicate intermediate navigation sub-goals for the agent (e.g., navigate to the garage, navigate to the driveway, navigate to the street).” In par. 0028 and “The systems and methods described herein may be configured to generate a custom domain knowledge-graph, which captures the regions and objects that the agent is likely to encounter. The systems and methods described herein may be configured to extend this knowledge graph, using public resources, to obtain additional connections to other likely objects and regions. The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding.” In par. 0029) , and
the additional modal data comprises image data or audio data representing a thing corresponding to a node of the object-level level. (see at least “In some embodiments, the one or more image inputs includes one or more of a red-green-blue input, a depth information input, and a semantic segmentation input. In some embodiments, traversing the environment associated with the first machine-learning model based on the at least one sub-goal is further based on at least one downstream episodic reward. In some embodiments, the output mechanism includes at least of a visual output mechanism and an audio output mechanism and wherein the at least one input mechanism includes at least one of a microphone and a keyboard.” In par. 0104)
Regarding Claim 7, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1, further comprising
a storage storing the additional modal data, which is mapped to the relevant node level in the scene graph (see at least multimodal question answering and stored functional mappings between image inputs and object classification in par. 0031 and “Classifier 514 of control system 502 of monitoring system 1000 may be configured to interpret the image and/or video data by matching identities of known people stored in non-volatile storage 516, thereby determining an identity of a person.” In par. 0100 ) , and
the instructions are further configured to, when executed by the one or more processors, cause the electronic device to generate the candidate node and load the additional modal data from the storage based on the additional modal data being mapped to the relevant node. (see at least “The ego-agent may retrieve from a memory module M, which stores keywords that were relevant to questions similar to initial question Q.sub.0. The similarity function ƒ.sub.sim compares the sub-goals and the object and/or region labels from current visual context of the ego-agent.” In par. 0032 and "The systems and methods described herein may be configured to use the agent to leverage object-region relationships captured by a knowledge graph and a pre-trained visual classifier. This allows for more efficient exploration, compared to breadth-first search (BFS), depth-first search (DFS), and other classical search algorithms. The systems and methods described herein may be configured to generate a custom domain knowledge-graph, which captures the regions and objects that the agent is likely to encounter. The systems and methods described herein may be configured to extend this knowledge graph, using public resources, to obtain additional connections to other likely objects and regions." in par. 0029 and)
Regarding Claim 8, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1, wherein the instructions are further configured to, when executed by the one or more processor, cause the electronic device to
generate the candidate node to comprise attribute information of a target object based on the prompt indicating an attribute of the target object in the task. (see at least “The systems and methods described herein may be configured to, when asked questions about unseen objects, use the agent to leverage commonsense and spatial knowledge of the agent for reasoning about properties and locations of the new objects. For example, if the agent is spawned in a bedroom and asked the question, “Where is the television located?”, the agent may not navigate the environment to explore, but can rely of the knowledge of object properties and locations and can offer initial predictions of {bedroom, living room, den}, instead.” In par. 0030 and “provides responses in accordance with explicit or implicit knowledge (KBs: [cars, at Location, driveway]”; LMs: [garage, driveway, street]) of the self-talk agent. Based on these responses (e.g., knowledge-grounded context), the ego-agent may generate sub-goal specifications, which aid in interaction with the environment. The ego-agent may retrieve from a memory module M, which stores keywords that were relevant to questions similar to initial question Q.sub.0. The similarity function ƒ.sub.sim compares the sub-goals and the object and/or region labels from current visual context of the ego-agent.” In par. 0032)
Regarding Claim 9, Francis as modified by Chen (references to Francis) teaches:
the electronic device of claim 1,
wherein the first machine-learning-based model is a text generation model (see at least " Based on this progress score, the ego-agent uses a natural language generation module ƒ.sub.gen to generate N clarification questions {Q.sub.1, . . . , Q.sub.m, . . . Q.sub.N}, throughout task execution, directed to the self-talk agent or an interactive dialogue agent." in par. 0032) , and
the instructions, when executed by the one or more processors, cause the electronic device to provide the first machine-learning based model with a serialized representation of the scene graph. (see at least "The systems and methods described herein may be configured to using this custom knowledge-graph, train a graph convolutional network to produce a semantics-aware and spatially-aware representation, which provides a rich state description, in the form of a latent vector embedding. This state embedding is to be used as an input to downstream components of the machine-learning model associated with the agent, such as the navigation/manipulation policy." in par. 0029)
Regarding Claim 10, Francis as modified by Chen teaches:
the electronic device of claim 1,
Francis does not appear to explicitly teach all of the following, but Chen does teach:
wherein the first machine-learning-based model and the second machine-learning-based model are the same multi-modal generation model. (see at least “At sub-block 454B, the system prompts an LLM, based on the FF NL instruction, to generate object descriptor(s). The LLM can be distinct from the LLM described in FIG. 5 or can be the same as that described in FIG. 5, but optionally primed and/or prompted differently.” In par. 0070)
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 modified the device taught by Francis to incorporate the teachings of Chen wherein the two LLM models are the same model but one is primed or prompted differently to perform a different role in the robot control process The motivation to incorporate the teachings of Chen would be to reduce the amount of memory needed to store the models (one set of weights rather than multiple), which improves resource usage efficiency.
Regarding Claim 11, Francis as modified by Chen also teaches:
a method for implementing the device of claim 1 (see Claim 1 analysis for rejection of the device)
Regarding Claim 12, Francis as modified by Chen teaches:
a method for implementing the device of claim 2 (see Claim 2 analysis for rejection of the device)
Regarding Claim 13, Francis as modified by Chen teaches:
a method for implementing the device of claim 3 (see Claim 3 analysis for rejection of the device)
Regarding Claim 14, Francis as modified by Chen teaches:
a method for implementing the device of claim 4 (see Claim 4 analysis for rejection of the device)
Regarding Claim 15, Francis as modified by Chen teaches:
a method for implementing the device of claim 5 (see Claim 5 analysis for rejection of the device)
Regarding Claim 16, Francis as modified by Chen teaches:
a method for implementing the device of claim 6 (see Claim 6 analysis for rejection of the device)
Regarding Claim 17, Francis as modified by Chen teaches:
a method for implementing the device of claim 7 (see Claim 7 analysis for rejection of the device)
Regarding Claim 18, Francis as modified by Chen teaches:
a method for implementing the device of claim 8 (see Claim 8 analysis for rejection of the device)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN M KATZ whose telephone number is (571)272-2776. The examiner can normally be reached Mon-Thurs. 8:00-6:00.
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/DYLAN M KATZ/Primary Examiner, Art Unit 3657
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