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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/2/2026 has been entered.
Priority
This application is a continuation of previous application 16622404 filed 12/13/2019 which is a
continuation of PCT/US17/38056 filed on 6/18/2017.
Status of claims
Claims 1, 5, 7, 10, 12, 16 and 18 are amended. Claims 1-20 are presented for examination.
Response to Arguments
Applicant’s arguments filed on 2/2/2026 have been reviewed. Following are the response:
Double Patenting Rejection
In view of the filed terminal disclaimer, the nonstatutory double patenting rejection is withdrawn.
35 U.S.C. 103 Rejections
Applicant noted that “In an effort to advance prosecution and without conceding the propriety of the Office Action's 103 rejections, independent claims 1 and 12 are amended herein.” However, the amendment did not change the scope of invention. It mention an alternate language and application describes neural network can be different kind of neural networks which is also taught by Perez, however under broadest reasonable interpretation, only one trained model is needed.
Applicant argues “For example, paragraphs [0076]-[0078] of Perez set forth that "text" of a "current utterance" can be "tokenized to form a sequence of tokens", from which "[n]oun phrase
(NP) chunks are then identified". The "NP Chunks" are "referred to as mentions", which are then used to "search... the index of ontology values" to "extract candidate matches" in the form of "a tuple of (TOPIC, SLOT, VALUE)". However, the Applicant's attorney respectfully submits that using "mentions" in a "current utterance" to "search... the index of ontology values" to "extract candidate matches" in the form of "a tuple of (TOPIC, SLOT, VALUE)", as set forth in the cited portions of Perez, fails to teach or suggest that a"slot descriptor embedding... is determined based on the application of at least one slot descriptor as input to a trained neural network model" as set forth in independent claim 1, as amended.
However, the Applicant’s argument is respectfully traversed. Under the Broadest Reasonable Interpretation, the "mentions" described in Perez are analogous to the "slot value descriptor" claimed.
Perez discloses that the "mentions” are derived from the current utterance, and these mentions are used to identify potential values associated with slots. Paragraphs [0078]-[0080] and [0110] of Perez indicate that these mentions (representing slots and values) are used to query an index of ontology values—such as a tuple (TOPIC, SLOT, VALUE) and the value can be for e.g. destination, and these mentions are input to a ranking model to update the belief state and get the results.
Applicant specification describes -- The "slot descriptor" such as "destination," which corresponds directly to the "SLOT" and "VALUE" components extracted from the mentions in Perez. Therefore, generating mentions from an utterance, which include topic/slot/value information used to query an index for candidate matches, constitutes a "slot descriptor embedding... determined based on the application of at least one slot descriptor as input to a trained neural network model" because the mentions function as the input describing the topic/slot/value and its value to the ranking model( neural network) . Thus, the cited portions of Perez read on the limitations of independent claim 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Perez (US 20180121415) and further in view of Gillespie ( US 10229680)
Regarding claim 1, Perez teaches a method implemented by one or more processors, comprising: receiving natural language input generated based on user interface input during a human-to-automated assistant dialog ( input utterance, Para 0029); generating a token embedding of tokens determined based on the natural language input ( token, Para 0073, 0077, 0117); selecting a domain based on the human-to-automated assistant dialog( domain specific corpus, Para 0082); determining at least one slot descriptor embedding for at least one textual descriptor of a slot assigned to the selected domain ( detect mentions in the text, Para 0076-0077), wherein the at least one textual slot descriptor embedding (value, topic and slot is based on the “mentions” which is an input to the language model ( for e.g. n-gram ontology model ) is determined based on the application of at least one slot descriptor as input to a trained neural network model (tuple is determined based on mentions using ontology ( n-gram model), Para 0078; which is then input to the ranking model to determine the slot filling and ranking model is a neural network model; neural network can be any combination ( alternative) of neural network look at the e.g. Para 0065) determining, based on application of the token embedding and the slot descriptor embedding as input to the trained neural network model or an alternate trained neural network model, that one or more of the tokens correspond to the slot assigned to the selected domain ( determine slots based on mentions and token, Fig 2, Para 0078-0080; which is an input the ranking model to rank and updating the belief of the dialog state, Par 0110; alternate model is presented as an OR statement, hence the trained model is the model which is being used. ) , wherein the token embedding is applied as input to the trained neural network model or the alternate trained neural network model based on being determined based on the tokens determined based on the natural language input (tokenized sequence, Para 0078), and wherein the slot descriptor embedding is applied as input to the trained neural network model or the alternate trained neural network model based on being of the slot assigned to the selected domain ( slot, value based on topic, Para 0078-0082); generating an command that includes a slot value for the slot that is based on the token determined to correspond to the slot ( ( task wished by user, Para 0054; info for the slot, Para 0120); and wherein the agent command causes the agent to generate responsive content and transmit the responsive content over one or more networks ( execute task, Fig 2, Para 0053-0054)
Perez does not explicitly teaches generating an agent command ; and transmitting the agent command to an agent over one or more networks, transmitting the agent command to an agent over one or more networks, wherein the agent command causes the agent to generate responsive content and transmit the responsive content over one or more networks
However, Gillespie teaches generating an agent command (providing the content, contextual metadata may be generated by the domain using formatting logic. The contextual metadata may be generated such that text corresponding to the content being displayed is formatted into slots and values associated with those slots, where the slots correspond to the slots associated with the domain's intent, Col 38, line 20-38) ; and transmitting the agent command to an agent over one or more networks, transmitting the agent command to an agent over one or more networks, wherein the agent command causes the agent to generate responsive content and transmit the responsive content over one or more networks ( sending out the data to be executed, Claim 1; for e.g. an orchestrator of the speech-processing system may send a request to a multi-domain functionality system that inquires which domain is currently responsible for providing the displayed content to the electronic device. After determining the particular domain, the orchestrator may receive or otherwise cause the natural language understanding system to receive contextual metadata representing content displayed on the client device by the domain., Col 1, line 50-60)
It would have been obvious having the teachings of Perez to further include the concept of Gillespie before effective filing date to resolve one or more declared slots for a particular intent and invoke a particular domain to execute a particular task for e.g. play a song etc. ( Col 38, line 20-38, Gillespie)
Regarding claim 2, Perez as above in claim 1, teaches wherein selecting the domain comprises selecting the agent based on the human-to-automated assistant dialog, and wherein the at least one slot descriptor embedding is determined based on the at least one slot descriptor or the slot descriptor embedding being assigned to the agent ( assigned to a domain, Para 0076)
Regarding claim 3, Perez as above in claim 1, teaches , further comprising: receiving the responsive content generated by the agent ( task executed, Fig 2, Perez; play a song e.g. Col 38, line 20-38)
Regarding claim 4, Gillespie as above in claim 3, teaches : transmitting, to a client device at which the user interface input was provided, output that is based on the responsive content generated by the agent ( an orchestrator of the speech-processing system may send a request to a multi-domain functionality system that inquires which domain is currently responsible for providing the displayed content to the electronic device…. For e.g. song name slot, Col 1, line 50-67, Col 2, line 1-10)
Regarding claim 12, arguments analogous to claim 1, are applicable. In addition, Perez teaches a system ( fig 1)
Regarding claim 13, arguments analogous to claim 2, are applicable.
Regarding claim 14, arguments analogous to claim 3, are applicable.
Regarding claim 15, arguments analogous to claim 4, are applicable.
Claims 5-6 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Perez (US 20180121415) and further in view of Gillespie ( US 10229680) and further in view of Deoras ( US 20150066496 )
Regarding claim 5, Perez modified by Gillespie as above in claim 1, does not explicitly teaches wherein determining, based on application of the token embedding and the slot descriptor embedding to the trained neural network model or the alternate trained neural network model, that one or more of the tokens correspond to the slot assigned to the selected domain comprises: applying both the token embedding and the slot descriptor embedding to a combining layer of the trained neural network model
However, Deoras teaches applying both the token embedding and the slot descriptor embedding to a combining layer of the trained neural network model or the alternate trained neural network model(combined training of token and context, RNN and DNN, Para 0004, 0007, 0064 Fig 9-10)
It would have been obvious having the teachings of Perez and Gillespie to further include the concept of Deoras to have more improved slot filing ( Para 0006, Deoras)
Regarding claim 6, Deoras as above in claim 5, teaches wherein the combining layer is a feed forward layer ( feed forward, Para 0006)
Regarding claim 16, arguments analogous to claim 5, are applicable.
Regarding claim 17, arguments analogous to claim 6, are applicable.
Claims 7-11 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Perez (US 20180121415) and further in view of Gillespie ( US 10229680) and further in view of Hashimoto ( US 20180121799)
Regarding claim 7, Perez modified by Gillespie as above in claim 1, does not explicitly teach , wherein generating the token embedding of the tokens of the natural language input comprises: applying the tokens to a memory layer of the trained neural network model or the alternate trained neural network model to generate the token embedding
However, Hashimoto teaches wherein generating the token embedding of the tokens of the natural language input comprises: applying the tokens to a memory layer of the trained neural network model or the alternate trained neural network model to generate the token embedding ( token embedding using the bi-directional LSTM ( stacked) network, Para 0186)
It would have been obvious having the teachings of Perez and Gillespie to further include the teachings of Hashimoto before effective filing to improve the processing of words
Regarding claim 8, Hashimoto as above in claim 7, teaches wherein the memory layer is a bi-directional memory layer comprising a plurality of memory units ( stacked, Para 0186)
Regarding claim 9, Hashimoto as above in claim 7, wherein generating the token embedding of the tokens of the natural language input further comprises: applying one or more annotations of one or more of the tokens to the memory layer to generate the token embedding ( annotated words ( tokens) , Para 0099, 0142)
Regarding claim 10, Hashimoto as above in claim 7, teaches wherein the combining layer is downstream from the memory layer, and upstream from one or more additional layers of the neural network model or the alternate trained neural network model ( fig 4a; stacked lstm )
Regarding claim 11, Hashimoto as above in claim 10, teaches wherein the one or more additional layers include at least one of: an additional memory layer; and an affine layer ( lstm inherently have affine layer , Fig 4)
Regarding claim 18, arguments analogous to claim 7, are applicable.
Regarding claim 19, arguments analogous to claim 8, are applicable.
Regarding claim 20, arguments analogous to claim 9, are applicable.
Conclusion
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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