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 .
Remarks
This action is in response to the applicant’s response filed 2 January 2026, which is in response to the USPTO office action mailed 7 October 2025. Claims 1, 11, 16 and 20 are amended. Claims 1-20 are currently pending.
Response to Arguments
With respect to the 35 USC §103 rejections of claims 1-20, the applicant’s arguments are moot in view of a new grounds of rejection, as necessitated by the applicant's amendments.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14, 6-9, 11-13, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over PADGETT et al., US 2025/0298978 A1 (hereinafter “Padgett”) in view of TONG et al., US 2024/0419950 A1 (hereinafter “Tong”).
Claim 1: Padgett teaches a computer-implemented method comprising:
identifying a plurality of prior human-to-computer dialogs stored in association with a user (Padgett, [0110] note At an operation 402, the system maintains a conversation history for an ongoing conversation session. For example, the conversation history may be maintained in a memory of the system by the conversation engine 250 as the conversation history data object 256, which grows as new messages are added to the conversation session);
processing, using a machine learning model, the plurality of prior human-to-computer dialogs respectively to determine a topic for each of the plurality of prior human-to-computer dialogs (Padgett, [0084] note When a new message is added to the ongoing conversation session, whether an input message or output message based on LLM generated output, the new message may be processed using the topic identifier module 258 to identify one or more topics associated with the new message);
storing, in one or more databases, the topic for each of the plurality of prior human-to-computer dialogs (Padgett, [0110] note The conversation history contains conversation segments of the ongoing conversation session, where each conversation segment includes one or more previous messages of the conversation session and each conversation segment is associated with at least one topic); and
subsequent to determining the topic for each of the plurality of human-to-computer dialogs: receiving a user query via an interface of a client device (Padgett, [0111] note At an operation 404, a new message for the ongoing conversation session is received (e.g., from a client system that is in communication with the system, in some cases via a UI provided by the conversation engine 250). The new message may contain text, image(s), audio and/or other suitable media);
processing, using a machine learning model, at least the user query and the topics determined from the plurality of human-to-computer dialogs to generate output that indicates whether the user query is related to a given topic from among the topics determined from the plurality of human-to-computer dialogs (Padgett, [0112] note At an operation 406, the system determines one or more topics associated with the new message. The operation 406 may be performed using the topic identifier module 258 of the conversation engine 250 as described above, for example, [0084] note The topic identifier module 258 may identify one or more topics associated with the new message using, for example, a clustering approach, using an LLM); and
in response to determining that the user query is related to the given topic (Padgett, [0116] note At the operation 408, it is determined, based on the topic(s) associated with the new message, that a particular conversation segment in the conversation history that is temporally closest to the new message is associated with at least one topic that is similar to or the same as at least one of the topic(s) associated with the new message. This means that the message(s) stored in the most recent conversation segment are relevant to the topic(s) associated with the new message):
processing, using the machine learning model, the user query and that is based on both the user query and a transcript including a plurality of dialog turns from a given dialog, from among the prior human-to-computer dialogs, that is associated with the given topic to generate a response that is responsive to the user query (Padgett, [0118] note At the operation 412, the conversation history is filtered based on relevance to the topic(s) associated with the new message, [0124] note Optionally, at an operation 414, a summary of at least one of the excluded conversation segments may be generated, [0125] note At an operation 416, a prompt is provided to a generative language model (e.g., a LLM as discussed above) based on the filtered conversation history and the new message, [0126] note the output generated by the generative language model in response to the prompt); and
causing the response to be provided for presentation to the user as part of the new human-to-computer dialog (Padgett, [0126] note At an operation 418, a message is provided to the conversation session (e.g., provided as a message to the client system, or outputted to be viewed on the user device that is the client system, etc.), based on the output generated by the generative language model in response to the prompt).
Padgett does not explicitly teach a first machine learning model; and a second machine learning model.
However, Tong teaches this (Tong, [0054] note methods that leverage specialized machine learning (ML) models, such as specialized large language models (LLMs), [0057] note The orchestrator may select an ML relatively smaller ML agent for certain tasks and relatively larger ML agents for other tasks. The orchestrator may utilize various hardware for running one or more ML agents, for example the end-user's computer, a small local server providing lower cost and/or faster response but which potentially may produce less high fidelity answers, and/or a more powerful remote server, depending on requirements for a given prompt and/or based on available compute across various systems, [0070] note One or more of the machine learning models included in the ML agents 120 forming a given expert 122 may be relatively small, specialized machine learning models (e.g., a small language model trained using fewer parameters, higher level of quantization than a relatively larger language model, such as an LLM like GPT4)).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the machine learning model of Padgett with the machine learning models of Tong according to known methods (i.e. implementing machine learning models including relatively small and relatively larger machine learning models). Motivation for doing so is a small local server may provide lower cost and/or faster response whereas a more powerful remote server may provide more high fidelity answers (Tong, [0057]).
Claim 2: Padgett and Tong teach the method of claim 1, wherein the first machine learning model is a generative model (Tong, [0054] note methods that leverage specialized machine learning (ML) models, such as specialized large language models (LLMs)).
Claim 3: Padgett and Tong teach the method of claim 2, wherein the second machine learning model is an additional generative model having fewer parameters than the first generative model (Tong, [0070] note One or more of the machine learning models included in the ML agents 120 forming a given expert 122 may be relatively small, specialized machine learning models (e.g., a small language model trained using fewer parameters, higher level of quantization than a relatively larger language model, such as an LLM like GPT4)).
Claim 4: Padgett and Tong teach the method of claim 1, further comprising:
causing the given dialog, or a link to the given dialog, to be rendered with respect to the user query (Padgett, [Fig. 5A]-[FIG. 5C], [0130] note FIGS. 5A-5C illustrate an example of a simplified chatbot UI, which may be implemented by an example of the conversation engine 250 as disclosed herein (e.g., using the example method 400), in the context of conversation session that is a chat-based session).
Claim 6: Padgett and Tong teach the method of claim 1, further comprising:
detecting that the new human-to-computer starting with the user query comes to an end, processing the new human-to-computer, using the first machine learning model, to generate an additional topic that summarizes the new human-to-computer, and updating a topic list of the topics determined from the plurality of human-to-computer dialogs, to include the additional topic (Padgett, [0084] note When a new message is added to the ongoing conversation session, whether an input message or output message based on LLM generated output, the new message may be processed using the topic identifier module 258 to identify one or more topics associated with the new message, [0110] note The conversation history contains conversation segments of the ongoing conversation session, where each conversation segment includes one or more previous messages of the conversation session and each conversation segment is associated with at least one topic).
Claim 7: Padgett and Tong teach the method of claim 6, wherein processing the new human-to-computer to generate the additional topic that summarizes the new human-to-computer is in response to detecting that the new human-to-computer starting with the user query has come to an end and is response to the output indicating that the user query is not related to any topic from the determined topics (Padgett, [0117] note At the operation 410, it is determined, based on the topic(s) associated with the new message, that all of the topic(s) associated with a particular conversation segment in the conversation history that is temporally closest to the new message are dissimilar to the topic(s) associated with the new message. This means that the message(s) stored in the most recent conversation segment are less relevant or irrelevant to the topic(s) associated with the new message. Instead of storing the new message with the existing most recent conversation segment, a new conversation segment is created in the conversation history (e.g., by creating a new record in the conversation history data object 256). The newly created conversation segment is associated with the topic(s) associated with the new message and the new message is stored to the newly created conversation segment).
Claim 8: Padgett and Tong teach the method of claim 1, wherein processing, using the second machine learning model, at least the user query and the topics determined from the plurality of human-to-computer dialogs comprises:
processing, using the second machine learning model, the user query, the topics, and one or more training examples (Padgett, [0118] note At the operation 412, the conversation history is filtered based on relevance to the topic(s) associated with the new message, [0124] note Optionally, at an operation 414, a summary of at least one of the excluded conversation segments may be generated… The summary may be generated using any suitable technique, such as using a trained NLP model, or a trained LLM model, among other possibilities, [0125] note At an operation 416, a prompt is provided to a generative language model (e.g., a LLM as discussed above) based on the filtered conversation history and the new message, [0126] note the output generated by the generative language model in response to the prompt).
Claim 9: Padgett and Tong teach the method of claim 8, wherein the one or more training examples include a first training example having a first portion that corresponds to a first example user query, a second portion corresponding to the topics or a list of different topics, and a third portion corresponding to an indication that indicates the first example user query is related to a particular topic, from the topics or the list of different topics (Padgett, [0048] note Training typically requires the use of a training dataset, which is a set of data that is relevant to the target behavior of the ML model… to train a ML model that is intended to classify images, the training dataset may be a collection of images. Training data may be annotated with ground truth labels (e.g. each data entry in the training dataset may be paired with a label), [0049] note Training a ML model generally involves inputting into an ML model (e.g. an untrained ML model) training data to be processed by the ML model, processing the training data using the ML model, collecting the output generated by the ML model (e.g. based on the inputted training data), and comparing the output to a desired set of target values. If the training data is labeled, the desired target values may be, e.g., the ground truth labels of the training data).
Claim 11: Padgett teaches a computer-implemented method, comprising:
receiving a user query, via an interface of a client device, that initiates a new human-to-computer dialog (Padgett, [0111] note At an operation 404, a new message for the ongoing conversation session is received (e.g., from a client system that is in communication with the system, in some cases via a UI provided by the conversation engine 250). The new message may contain text, image(s), audio and/or other suitable media);
in response to receiving the user query that initiates the new human-to-computer dialog: processing, using a machine learning model, the user query and a plurality of topics determined from prior human-to-computer dialogs, to generate an output indicating whether the user query is related to any topic from the plurality of topics, wherein the corresponding plurality of topics are determined based on previously processing the prior human-to-computer dialogs (Padgett, [0110] note The conversation history contains conversation segments of the ongoing conversation session, where each conversation segment includes one or more previous messages of the conversation session and each conversation segment is associated with at least one topic, [0112] note At an operation 406, the system determines one or more topics associated with the new message. The operation 406 may be performed using the topic identifier module 258 of the conversation engine 250 as described above, for example, [0084] note The topic identifier module 258 may identify one or more topics associated with the new message using, for example, a clustering approach, using an LLM); and
in response to the output indicating that the user query is related to a given topic, from among the plurality of corresponding topics: processing, using the machine learning model, the user query and a transcript including a plurality of dialog turns from a given dialog, from among the prior human-to-computer dialogs, that is associated with the given topic to generate a response that is responsive to the user query (Padgett, [0118] note At the operation 412, the conversation history is filtered based on relevance to the topic(s) associated with the new message, [0124] note Optionally, at an operation 414, a summary of at least one of the excluded conversation segments may be generated, [0125] note At an operation 416, a prompt is provided to a generative language model (e.g., a LLM as discussed above) based on the filtered conversation history and the new message, [0126] note the output generated by the generative language model in response to the prompt);
causing the response to be provided for presentation to the user as part of the new human-to-computer dialog (Padgett, [0126] note At an operation 418, a message is provided to the conversation session (e.g., provided as a message to the client system, or outputted to be viewed on the user device that is the client system, etc.), based on the output generated by the generative language model in response to the prompt).
Padgett does not explicitly teach with an additional machine learning model.
However, Tong teaches this (Tong, [0054] note methods that leverage specialized machine learning (ML) models, such as specialized large language models (LLMs), [0057] note The orchestrator may select an ML relatively smaller ML agent for certain tasks and relatively larger ML agents for other tasks. The orchestrator may utilize various hardware for running one or more ML agents, for example the end-user's computer, a small local server providing lower cost and/or faster response but which potentially may produce less high fidelity answers, and/or a more powerful remote server, depending on requirements for a given prompt and/or based on available compute across various systems, [0070] note One or more of the machine learning models included in the ML agents 120 forming a given expert 122 may be relatively small, specialized machine learning models (e.g., a small language model trained using fewer parameters, higher level of quantization than a relatively larger language model, such as an LLM like GPT4)).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the machine learning model of Padgett with the machine learning models of Tong according to known methods (i.e. implementing machine learning models including relatively small and relatively larger machine learning models). Motivation for doing so is a small local server may provide lower cost and/or faster response whereas a more powerful remote server may provide more high fidelity answers (Tong, [0057]).
Claim 12: Padgett and Tong teach the method of claim 11, comprising:
in response to the output indicating that the user query is not related to any topic from the determined topics and in response to detecting one or more conditions being satisfied, processing the new human-to-computer dialog starting with the user query, using a generative model, to generate an additional topic that summarizes the new human-to-computer dialog, and storing the additional topic in association with the user of the user query, along with the plurality of topics (Padgett, [0117] note At the operation 410, it is determined, based on the topic(s) associated with the new message, that all of the topic(s) associated with a particular conversation segment in the conversation history that is temporally closest to the new message are dissimilar to the topic(s) associated with the new message. This means that the message(s) stored in the most recent conversation segment are less relevant or irrelevant to the topic(s) associated with the new message. Instead of storing the new message with the existing most recent conversation segment, a new conversation segment is created in the conversation history (e.g., by creating a new record in the conversation history data object 256). The newly created conversation segment is associated with the topic(s) associated with the new message and the new message is stored to the newly created conversation segment).
Claim 13: Padgett and Tong teach the method of claim 12, wherein the one or more conditions include a first condition indicating whether the new human-to-computer dialog has come to an end (Padgett, [0079] note a conversation session ends (e.g., by the client system terminating the session, by a timeout, etc.)).
Claim 16: Padgett and Tong teach the method of claim 11, wherein processing the user query and the plurality of topics determined from prior human-to-computer dialogs using the machine learning model comprises:
generating a first prompt to include the user query and the plurality of topics, and processing the first prompt as input, using the machine learning model, to generate the output indicating whether the user query is related to any topic from the plurality of topics (Padgett, [0125] note At an operation 416, a prompt is provided to a generative language model (e.g., a LLM as discussed above) based on the filtered conversation history and the new message).
Claim 17: Padgett and Tong teach the method of claim 11, wherein the first prompt further includes one or more training examples (Padgett, [0069] note A prompt can include one or more examples of the desired output, which provides the LLM with additional information to enable the LLM to better generate output according to the desired output).
Claim 18: Padgett and Tong teach the method of claim 17, wherein the one or more training examples include a first training example having a first portion that corresponds to a first example user query, a second portion corresponding to the topics or a list of different topics, and a third portion corresponding to an indication that indicates the first example user query is related to a particular topic, from the topics or the list of different topics (Padgett, [0050] note training data may be a subset of a larger data set. For example, a data set may be split into three mutually exclusive subsets: a training set, a validation (or cross-validation) set, and a testing set. The three subsets of data may be used sequentially during ML model training, [0051] note Backpropagation is an algorithm for training a ML model. Backpropagation is used to adjust (also referred to as update) the value of the parameters in the ML model, with the goal of optimizing the objective function. For example, a defined loss function is calculated by forward propagation of an input to obtain an output of the ML model and comparison of the output value with the target value).
Claim 20: Padgett teaches a system comprising one or more processors and a memory storing instructions that, when executed, cause one or more of the processors to:
receive a user query, via an interface of a client device, that initiates a new human-to-computer dialog (Padgett, [0111] note At an operation 404, a new message for the ongoing conversation session is received (e.g., from a client system that is in communication with the system, in some cases via a UI provided by the conversation engine 250). The new message may contain text, image(s), audio and/or other suitable media);
in response to receiving the user query that initiates the new human-to-computer dialog: process, using a machine learning model, the user query and a plurality of topics determined from prior human-to-computer dialogs, to generate an output indicating whether the user query is related to any topic from the plurality of topics, wherein the corresponding plurality of topics are determined based on previously processing the prior human-to-computer dialogs (Padgett, [0110] note The conversation history contains conversation segments of the ongoing conversation session, where each conversation segment includes one or more previous messages of the conversation session and each conversation segment is associated with at least one topic, [0112] note At an operation 406, the system determines one or more topics associated with the new message. The operation 406 may be performed using the topic identifier module 258 of the conversation engine 250 as described above, for example, [0084] note The topic identifier module 258 may identify one or more topics associated with the new message using, for example, a clustering approach, using an LLM); and
in response to the output indicating that the user query is related to a given topic, from among the plurality of corresponding topics: process, using the machine learning model, the user query and a transcript including a plurality of dialog turns from a given dialog, from among the prior human-to-computer dialogs, that is associated with the given topic to generate a response that is responsive to the user query (Padgett, [0118] note At the operation 412, the conversation history is filtered based on relevance to the topic(s) associated with the new message, [0124] note Optionally, at an operation 414, a summary of at least one of the excluded conversation segments may be generated, [0125] note At an operation 416, a prompt is provided to a generative language model (e.g., a LLM as discussed above) based on the filtered conversation history and the new message, [0126] note the output generated by the generative language model in response to the prompt);
cause the response to be provided for presentation to the user as part of the new human-to-computer dialog (Padgett, [0126] note At an operation 418, a message is provided to the conversation session (e.g., provided as a message to the client system, or outputted to be viewed on the user device that is the client system, etc.), based on the output generated by the generative language model in response to the prompt).
Padgett does not explicitly teach with an additional machine learning model.
However, Tong teaches this (Tong, [0054] note methods that leverage specialized machine learning (ML) models, such as specialized large language models (LLMs), [0057] note The orchestrator may select an ML relatively smaller ML agent for certain tasks and relatively larger ML agents for other tasks. The orchestrator may utilize various hardware for running one or more ML agents, for example the end-user's computer, a small local server providing lower cost and/or faster response but which potentially may produce less high fidelity answers, and/or a more powerful remote server, depending on requirements for a given prompt and/or based on available compute across various systems, [0070] note One or more of the machine learning models included in the ML agents 120 forming a given expert 122 may be relatively small, specialized machine learning models (e.g., a small language model trained using fewer parameters, higher level of quantization than a relatively larger language model, such as an LLM like GPT4)).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the machine learning model of Padgett with the machine learning models of Tong according to known methods (i.e. implementing machine learning models including relatively small and relatively larger machine learning models). Motivation for doing so is a small local server may provide lower cost and/or faster response whereas a more powerful remote server may provide more high fidelity answers (Tong, [0057]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Padgett and Tong in further view of Chowdhary et al., US 12,259,896 B1 (hereinafter “Chowdhary”).
Claim 5: Padgett and Tong do not explicitly teach the method of claim 1, further comprising: in response to the output indicating that the user query is not related to any topic from the topics determined from the plurality of human-to-computer dialogs, processing the user query, using the first machine learning model or a third machine learning model, to generate an alternative model output indicating an alternative response responsive to the user query, and causing the alternative response to be rendered in response to the user query.
However, Chowdhary teaches this (Chowdhary, [Fig. 3] note 312, 316, 318, 320, [Col. 10 Lines 63-67]-[Col. 11 Lines 1-5] note In Block 316, an iterative feedback re-indexing via the feedback service for the search engine is initiated… Namely, the process may be activated when comparison step of Block 312 determines that no vector index has a composite score that is higher than the composite score threshold, [Col. 11 Lines 22-26] note In Block 320, the answer to the user query is generated by the answer generation model. In one embodiment, the user query answer service provides the result embeddings obtained from Block 318 as input data to the answer generation model).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the machine learning model training of Padgett and Tong with the iterative feedback re-indexing of Chowdhary according to known methods (i.e. training machine learning models based on iterative feedback re-indexing). Motivation for doing so is that this continuous update process improves the quality, relevance and accuracy of the generated answer (Chowdhary, [Col. 8 Lines 66-67]).
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Padgett and Tong in further view of Jeong et al., US 2021/0125003 A1 (hereinafter “Jeong”).
Claim 10: Padgett and Tong teach the method of claim 1, wherein the output that indicates whether the user query is related to the given topic is one or more of: a vector (Padgett, [0063] note Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56).
Padgett and Tong do not explicitly teach a one-hot vector, or a continuous vector.
However, Jeong teaches this (Jeong, [0020] note generating domain information to be learned through the generative model into domain vector blocks, [0045] note the domain module 104 may vectorize the domain information to be learned through a generative model into a one-hot vector. hat is, the domain module 104 may assign a unique index to each domain type and generate a one-hot vector in which a vector value corresponding to an index of a domain vector is 1 and vector values corresponding to the remaining indices are 0 in the vector of the dimensions corresponding to the number of domain types).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the vectors of Padgett and Tong with the one-hot vectors of Jeong according to known methods (i.e. vectorizing domain information into a one-hot vector). Motivation for doing so is that this prevents a decrease in learning speed even with the increased number of domain information (Jeong, [0022]).
Claim 19: Padgett and Tong teach the method of claim 18, wherein the indication is one or more of: a vector (Padgett, [0063] note Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56).
Padgett and Tong do not explicitly teach a one-hot vector, or a continuous vector.
However, Jeong teaches this (Jeong, [0020] note generating domain information to be learned through the generative model into domain vector blocks, [0045] note the domain module 104 may vectorize the domain information to be learned through a generative model into a one-hot vector. hat is, the domain module 104 may assign a unique index to each domain type and generate a one-hot vector in which a vector value corresponding to an index of a domain vector is 1 and vector values corresponding to the remaining indices are 0 in the vector of the dimensions corresponding to the number of domain types).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the vectors of Padgett and Tong with the one-hot vectors of Jeong according to known methods (i.e. vectorizing domain information into a one-hot vector). Motivation for doing so is that this prevents a decrease in learning speed even with the increased number of domain information (Jeong, [0022]).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Padgett and Tong in view of Chakraborty et al., US 2018/0012135 A1 (hereinafter “Chakraborty”).
Claim 14: Padgett and Tong do not explicitly teach the method of claim 12, wherein the one or more conditions include a second condition indicating a time of the day.
However, Chakraborty teaches this (Chakraborty, [0016] note The present embodiments use information from multiple sources to calculate a conditional probability that the query can be positively satisfied, for example including the device's location, movement trajectory, data or image content, time, etc, [0025] note topic modeling, [0033] note At each node 106, block 210 determines whether the local content matches the query, [0033] note the system transforms the keywords of the search query into a vector and the model is used to determine the probability that the generative topic model would generate the search vector if sampled from the posterior).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the conversation sessions of Padgett and Tong with the conditional probability that the query can be positively satisfied of Chakraborty according to known methods (i.e. conducting a conversation session based on a conditional probability such as a time being positively satisfied). Motivation for doing so is that this uses information regarding what the query is searching for, the locations of the mobile devices, the time, and other contextual factors to find query results with minimal involvement from the users of the mobile devices and from the user who issued the query (Chakraborty, [0015]).
Claim 15: Padgett and Tong do not explicitly teach the method of claim 12, wherein the one or more conditions include a third condition indicating a battery level of the client device.
However, Chakraborty teaches this (Chakraborty, [0016] note The present embodiments use information from multiple sources to calculate a conditional probability that the query can be positively satisfied, [0025] note topic modeling, [0033] note At each node 106, block 210 determines whether the local content matches the query, [0033] note the system transforms the keywords of the search query into a vector and the model is used to determine the probability that the generative topic model would generate the search vector if sampled from the posterior, [0034] note Even if a match is found, however, block 212 determines whether the node 106 will respond. In particular, this determination can be based on the device's battery level, the computational resources available, and the type of information being sought in the query).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the conversation session of Padgett and Tong with the conditional probability that the query can be positively satisfied of Chakraborty according to known methods (i.e. conducting a conversation session based on a conditional probability such as a battery level being positively satisfied). Motivation for doing so is that this uses information regarding what the query is searching for, the locations of the mobile devices, the time, and other contextual factors to find query results with minimal involvement from the users of the mobile devices and from the user who issued the query (Chakraborty, [0015]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/GIUSEPPI GIULIANI/Primary Examiner, Art Unit 2153