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 .
DETAILED ACTION
This office action is in response to correspondence 02/10/26 regarding application 18/425,795, in which claims 1, 7, 11, and 17 were amended, claims 6 and 16 were amended, and new claims 21-22 were added. Claims 1-5, 7-15, and 17-22 are pending in the application and have been considered.
Response to Arguments
Amended independent claims 1 and 11 overcome the 35 U.S.C. 101 rejections of claims 1-3, 10-13, and 20 as being directed to non-statutory subject matter. Specifically, as amended these claims recite the limitations previously found in dependent claims 6 and 16 which were indicated as eligible in the 09/04/25 Office Action because they cannot be practically performed as a mental process.
The examiner agrees with Applicant on page 10 that no new matter was added via the amendments to claims 1, 7, 11, and 17.
Since the 35 U.S.C. 101 rejections are withdrawn as noted above, the arguments on pages 10-11 regarding these rejections are moot.
Applicant’s arguments on pages 11-12 regarding the 35 U.S.C. 103 rejection based in part on Mathias have been considered but are not persuasive. In particular, Applicant argues that Mathias does not disclose a “predefined conversation flow”, allegedly because nothing in the example interaction discussed at [0116] of Matthias is predefined. Applicant’s arguments seem to attempt to draw a distinction between the “expected” entities described in Applicant’s specification, and regular entities determined during the interactions of Mathias.
In response, the examiner respectfully disagrees with Applicant’s interpretation of Mathias that nothing in the example interaction discussed at [0116] of Matthias is predefined. Applicant’s specification at para [0038] describes a situation in which “system 310 can determine the entity associated with the conversational input further based on one or more expected entities. The one or more expected entities can be used to supplement a missing entity in a conversational input. For instance, in the example above, if the user enters an input #4, “Make it 2,” system 310 can determine or extract the expected entities from the immediate prior one or more conversational inputs”. This is exactly what Mathias is doing in the interaction example cited in the office action. When the user asks “are there any Mexican restaurants there?” in [0115], the system determines the intent is <localsearch> which requires data to fill a <placetype> slot as well as data to fill a <city> slot, i.e. expected entities. Mathias fills these slots by looking back at previous conversation context, similar to Applicant, see [0113]-[0115]. Applicant argues that nothing about the above scenario is predefined, but clearly the evidence in Mathias suggests that the slots associated with the intent <localsearch> are predefined. Before the conversation even begins, the dialog system in Mathias appears to know that if it encounters a <localsearch> intent, it will need to fill a <placetype> and <city> slot, presumably in order to know what exactly to search for and in which city. In order to fill the <city> slot, the Mathias system looks back at the conversation history dialog turns. In other words, because the intents in Mathias have predefined expected slots associated with them, they are fairly considered to be “predefined conversational flows”. While the slots are filled dynamically upon detecting the user’s intent, the slots themselves are predefined. Further, for the sake of argument, and solely for the sake of expediting prosecution, it is noted that the previous dialog turns in Mathias providing context (e.g. what is the weather in san Francisco) are fairly considered one or more “predefined conversation flows” since they have already occurred, i.e. are predefined, at the time of the current dialog turn. In other words, the term “predefined conversation flows” as currently used in the specification and claims does not even rule out previous turns in the conversation.
The examiner is also not persuaded by Applicant’s argument on page 12 that the new limitation “accessing one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model” clearly distinguishes the claims from the conversation inputs previously received from the dialog system and the user. In Matthias, the previously conversation history consists of utterances from a human user and a machine learning dialog system (i.e. manually and machine learning generated conversational flows). It is therefore unclear why “accessing one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model” is somehow different from Matthias merely accessing the conversation history. However, solely to expedite prosecution, a new rejection based in part on the newly discovered reference to Friedlander et al. (US 20230026945) is made, which discloses a user interface for explicitly generated predefined dialog flows manually.
Applicant’s arguments on pages 12-13 regarding the dependent claims, as well as new claims 21 and 22 are not persuasive for similar reasons to those discussed above regarding claims 1 and 11.
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 1-5, 7-15, and 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over Mathias et al. (US 20210142794) in view of Suwandy et al. (US 20210327413), in further view of Friedlander et al. (US 20230026945).
Consider claim 1, Mathias discloses a system comprising: one or more processors (devices and servers having processors, [0128]); and
one or more non-transitory computer-readable media storing computing instructions configured to, when run on the one or more processors (non-transitory computer readable storage medium with instructions executed by a processor, [0133]), cause the one or more processors to perform:
accessing one or more predefined conversational flows (expected slots that need to be filled for the current intent, e.g. <placetype> and <city>, a predefined conversation flow, [0116]);
upon receiving, from a computer network, a conversational input from a user device for a user, determining a context based on one or more contextual units, wherein the one or more contextual units are associated with immediate prior one or more conversational inputs relative to the conversational input (system receives utterance 524 “any Mexican restaurants there”, [0116], over network 199 from smart device, [0130], during conversation between user and system, [0112], and determines user utterance history, including previous utterance “what is the weather in san Francisco?”, [0113]-[0115]);
determining an intent associated with the conversational input based on the context (determining that the user wishes to perform a Local Search for San Francisco based on the previous utterance from the history, i.e. context, [0116]) by generating, by an embedding layer, a token vector for the conversational input (e.g. user utterance embeddings, [0104], Fig 5); and determining, by an intent classification layer, the intent based on the token vector and a single multi-dimensional context vector for the context (decoder 570 determines a score that corresponds to whether a particular candidate value pair corresponds to slot needed to execute the intent, [0080], performing intent classification, [0057]);
determining one or more entities associated with the conversational input based on the context and one or more expected entities determined based on one or more predefined conversation flows (scoring entities for slots that need to be filled for the current intent, e.g. <placetype> and <city>, a predefined conversation flow, [0116]);
determining an output based on the intent and the one or more entities (“La Taqueria is a mile away”, [0117], [0116]); and
transmitting, via the computer network, the output on the user device (results are transmitted from server over network to devices for output, [0114], [0130], [0117]).
Mathias does not specifically mention output to be displayed on the user device.
Suwandy discloses output to be displayed on the user device (chat window 608 displays output from conversational bot on web browser 604 of computing device 602, [0071], Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias by including output to be displayed on the user device in order to address the increasing need for conversational bots or assistants to handle requests and commands, predictably resulting in helping entities assist their customers with goods and services, as suggested by Suwandy ([0001]). The references cited are analogous art in the same field of natural language understanding.
Mathias and Suwandy do not specifically mention one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model.
Friedlander discloses one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model (dynamic intelligent response templates may be generated automatically from actual conversations, imported from external dialog flow design systems, or created manually from scratch, [0379], [0380], Fig. 10, Fig 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias and Suwandy such that the predefined conversational flows that have been manually generated or automatically generated using a machine learning model in order to shorten design, implementation and maintenance of applications based on the service, as suggested by Friedlander ([0038]), predictably increasing usability and adoption of such applications, as suggested by Friedlander ([0038]). The references cited are analogous art in the same field of natural language understanding.
Consider claim 11, Mathias discloses a method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media (non-transitory computer readable storage medium with instructions to perform a method executed by a processor, [0133]), the method comprising:
accessing one or more predefined conversational flows (expected slots that need to be filled for the current intent, e.g. <placetype> and <city>, a predefined conversation flow, [0116]);
upon receiving, from a computer network, a conversational input from a user device for a user, determining a context based on one or more contextual units, wherein the one or more contextual units are associated with immediate prior one or more conversational inputs relative to the conversational input (system receives utterance 524 “any Mexican restaurants there”, [0116], over network 199 from smart device, [0130], during conversation between user and system, [0112], and determines user utterance history, including previous utterance “what is the weather in san Francisco?”, [0113]-[0115]);
determining an intent associated with the conversational input based on the context (determining that the user wishes to perform a Local Search for San Francisco based on the previous utterance from the history, i.e. context, [0116]) by generating, by an embedding layer, a token vector for the conversational input (e.g. user utterance embeddings, [0104], Fig 5); and determining, by an intent classification layer, the intent based on the token vector and a single multi-dimensional context vector for the context (decoder 570 determines a score that corresponds to whether a particular candidate value pair corresponds to slot needed to execute the intent, [0080], performing intent classification, [0057]);
determining one or more entities associated with the conversational input based on the context and one or more expected entities determined based on one or more predefined conversation flows (scoring entities for slots that need to be filled for the current intent, e.g. <placetype> and <city>, a predefined conversation flow, [0116]);
determining an output based on the intent and the one or more entities (“La Taqueria is a mile away”, [0117], [0116]); and
transmitting, via the computer network, the output on the user device (results are transmitted from server over network to devices for output, [0114], [0130], [0117]).
Mathias does not specifically mention output to be displayed on the user device.
Suwandy discloses output to be displayed on the user device (chat window 608 displays output from conversational bot on web browser 604 of computing device 602, [0071], Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias by including output to be displayed on the user device for reasons similar to those for claim 1.
Mathias and Suwandy do not specifically mention one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model.
Friedlander discloses one or more predefined conversational flows that have been manually generated or automatically generated using a machine learning model (dynamic intelligent response templates may be generated automatically from actual conversations, imported from external dialog flow design systems, or created manually from scratch, [0379], [0380], Fig. 10, Fig 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias and Suwandy such that the predefined conversational flows that have been manually generated or automatically generated using a machine learning model for reasons similar to those for claim 1.
Consider claim 2, Mathias discloses: each of the one or more contextual units comprises: a respective context conversational input for each of the immediate prior one or more conversational inputs (e.g. previous utterance “what is the weather in san Francisco?”, which generates <getweather> intent which requires a <location> slot, [0113]-[0115]); a respective context intent vector for a respective context intent associated with the respective context conversational input (e.g. <getweather> and <location>, the pair considered a context intent vector, [0113]; these are embedded in vector form by Dialog Tracker 590, Fig 5); and a respective context entities vector for one or more respective context entities associated with the respective context conversational input (entity-key pairs embeddings, [0013], Fig. 5, element 590).
Consider claim 3, Mathias discloses: the respective context intent vector is encoded based on the respective context intent and predefined intent vector values (e.g. the slots belonging to <getweather> and <localsearch>, [0113]-[0115], Fig 5); and the respective context entities vector is encoded based on the one or more respective context entities and predefined entity tags (e.g. the entity candidates to fill the <placetype>, <city> slots, etc., [0113]-[0115], Fig 5).
Consider claim 4, Mathias discloses: determining the context further comprises: generating, by an embedding layer, a respective context token vector for each of the one or more contextual units based on the respective context conversational input of the each of the one or more contextual units (Encoder 550 generates context embeddings, Fig 5, [0113-0115]); generating, by a feedforward layer, a respective consolidated vector for each of the one or more contextual units based on the respective context token vector, the respective context intent vector, and the respective context entities vector for the each of the one or more contextual units (e.g. <getweather> and <location>, the pair considered a context intent vector, [0113]; these are embedded in vector form by Dialog Tracker 590, Fig 5, for each utterance turn); and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into a single multi-dimensional context vector (word attention vectors concatenated into per-stream context vector computed by attention models, [0104], Fig 5).
Consider claim 5, Mathias does not, but Suwandy discloses wherein one or more of:
the embedding layer comprises a pre-trained BERT model (embeddings encoded via BERT, a well known pre-trained model, [0026]); or the respective context token vector for each of the one or more contextual units further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias such that the embedding layer comprises a pre-trained BERT model for reasons similar to those for claim 1.
Consider claim 7, Mathias discloses one or more of:
the embedding layer comprises a pre-trained BERT model (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the token vector for the conversational input further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”); the
intent classification layer comprises a first feedforward layer and a softmax layer (Dense Layer 574 and Softmax 576, Fig 5, [0108]); or
the single multi-dimensional context vector for the context is determined by: generating, by the embedding layer, a respective context token vector for each of the one or more contextual units based on a respective context conversational input of the each of the one or more contextual units; generating, by a second feedforward layer, a respective consolidated vector for each of the one or more contextual units based on: (a) the respective context token vector for the each of the one or more contextual units, (b) a respective context intent vector for a respective context intent associated with the respective context conversational input of the each of the one or more contextual units, and (c) a respective context entities vector for one or more respective context entities associated with the respective context conversational input of the each of the one or more contextual units; and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into the single multi-dimensional context vector (noting that this limitation is not required by the claim language, which only requires “one or more of”).
Consider claim 8, Mathias discloses: determining the one or more entities associated with the conversational input further comprises: generating, by an embedding layer, a token vector for the conversational input (e.g. user utterance embeddings, [0104], Fig 5); concatenating the token vector, a single multi-dimensional context vector for the context, and an expected entities vector for the one or more expected entities into a consolidated entity vector (vector generated by encoder 550, which concatenates vectors, including slot embedding i.e. expected entities vector, and utterance history embeddings, [0108-0109], Fig 5); and determining, by an entity recognizing layer, a respective entity tag for each of the one or more entities based on the consolidated entity vector (score for particular candidate keyvalue pair under consideration for the particular slot needed to operate the current intent, [0109], e.g. “San Francisco”, [0113]-[0115]).
Consider claim 9, Mathias discloses wherein one or more of:
the single multi-dimensional context vector for the context is determined by: generating, by the embedding layer, a respective context token vector for each of the one or more contextual units based on a respective context conversational input of the each of the one or more contextual units (noting that this limitation is not required by the claim language, which only requires “one or more of”); generating, by a third feedforward layer, a respective consolidated vector for each of the one or more contextual units based on: (a) the respective context token vector for the each of the one or more contextual units, (b) a respective context intent vector for a respective context intent associated with the respective context conversational input of the each of the one or more contextual units, and (c) a respective context entities vector for one or more respective context entities associated with the respective context conversational input of the each of the one or more contextual units; and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into the single multi-dimensional context vector (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the expected entities vector is encoded based on the one or more expected entities and predefined entity tags (e.g. the entity candidates to fill the <placetype>, <city> slots, etc., [0113]-[0115], Fig 5);
the embedding layer comprises a pre-trained BERT model (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the token vector for the conversational input further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”); or
the entity recognizing layer comprises a fourth feedforward layer and a softmax layer (noting that this limitation is not required by the claim language, which only requires “one or more of”).
Consider claim 10, Mathias discloses: the immediate prior one or more conversational inputs and the conversational input occur in a time session of a conversation (the context data includes time data, such as time of receipt of the audio data, [0063], for a dialog session with multiple utterances, [0070]).
Consider claim 12, Mathias discloses: each of the one or more contextual units comprises: a respective context conversational input for each of the immediate prior one or more conversational inputs (e.g. previous utterance “what is the weather in san Francisco?”, which generates <getweather> intent which requires a <location> slot, [0113]-[0115]); a respective context intent vector for a respective context intent associated with the respective context conversational input (e.g. <getweather> and <location>, the pair considered a context intent vector, [0113]; these are embedded in vector form by Dialog Tracker 590, Fig 5); and a respective context entities vector for one or more respective context entities associated with the respective context conversational input (entity-key pairs embeddings, [0013], Fig. 5, element 590).
Consider claim 13, Mathias discloses: the respective context intent vector is encoded based on the respective context intent and predefined intent vector values (e.g. the slots belonging to <getweather> and <localsearch>, [0113]-[0115], Fig 5); and the respective context entities vector is encoded based on the one or more respective context entities and predefined entity tags (e.g. the entity candidates to fill the <placetype>, <city> slots, etc., [0113]-[0115], Fig 5).
Consider claim 14, Mathias discloses: determining the context further comprises: generating, by an embedding layer, a respective context token vector for each of the one or more contextual units based on the respective context conversational input of the each of the one or more contextual units (Encoder 550 generates context embeddings, Fig 5, [0113-0115]); generating, by a feedforward layer, a respective consolidated vector for each of the one or more contextual units based on the respective context token vector, the respective context intent vector, and the respective context entities vector for the each of the one or more contextual units (e.g. <getweather> and <location>, the pair considered a context intent vector, [0113]; these are embedded in vector form by Dialog Tracker 590, Fig 5, for each utterance turn); and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into a single multi-dimensional context vector (word attention vectors concatenated into per-stream context vector computed by attention models, [0104], Fig 5).
Consider claim 15, Mathias does not, but Suwandy discloses wherein one or more of:
the embedding layer comprises a pre-trained BERT model (embeddings encoded via BERT, a well known pre-trained model, [0026]); or the respective context token vector for each of the one or more contextual units further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mathias such that the embedding layer comprises a pre-trained BERT model for reasons similar to those for claim 1.
Consider claim 17, Mathias discloses one or more of:
the embedding layer comprises a pre-trained BERT model (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the token vector for the conversational input further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”); the
intent classification layer comprises a first feedforward layer and a softmax layer (Dense Layer 574 and Softmax 576, Fig 5, [0108]); or
the single multi-dimensional context vector for the context is determined by: generating, by the embedding layer, a respective context token vector for each of the one or more contextual units based on a respective context conversational input of the each of the one or more contextual units; generating, by a second feedforward layer, a respective consolidated vector for each of the one or more contextual units based on: (a) the respective context token vector for the each of the one or more contextual units, (b) a respective context intent vector for a respective context intent associated with the respective context conversational input of the each of the one or more contextual units, and (c) a respective context entities vector for one or more respective context entities associated with the respective context conversational input of the each of the one or more contextual units; and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into the single multi-dimensional context vector (noting that this limitation is not required by the claim language, which only requires “one or more of”).
Consider claim 18, Mathias discloses: determining the one or more entities associated with the conversational input further comprises: generating, by an embedding layer, a token vector for the conversational input (e.g. user utterance embeddings, [0104], Fig 5); concatenating the token vector, a single multi-dimensional context vector for the context, and an expected entities vector for the one or more expected entities into a consolidated entity vector (vector generated by encoder 550, which concatenates vectors, including slot embedding i.e. expected entities vector, and utterance history embeddings, [0108-0109], Fig 5); and determining, by an entity recognizing layer, a respective entity tag for each of the one or more entities based on the consolidated entity vector (score for particular candidate keyvalue pair under consideration for the particular slot needed to operate the current intent, [0109], e.g. “San Francisco”, [0113]-[0115]).
Consider claim 19, Mathias discloses wherein one or more of:
the single multi-dimensional context vector for the context is determined by: generating, by the embedding layer, a respective context token vector for each of the one or more contextual units based on a respective context conversational input of the each of the one or more contextual units (noting that this limitation is not required by the claim language, which only requires “one or more of”); generating, by a third feedforward layer, a respective consolidated vector for each of the one or more contextual units based on: (a) the respective context token vector for the each of the one or more contextual units, (b) a respective context intent vector for a respective context intent associated with the respective context conversational input of the each of the one or more contextual units, and (c) a respective context entities vector for one or more respective context entities associated with the respective context conversational input of the each of the one or more contextual units; and concatenating, by an attention layer, the respective consolidated vector for each of the one or more contextual units into the single multi-dimensional context vector (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the expected entities vector is encoded based on the one or more expected entities and predefined entity tags (e.g. the entity candidates to fill the <placetype>, <city> slots, etc., [0113]-[0115], Fig 5);
the embedding layer comprises a pre-trained BERT model (noting that this limitation is not required by the claim language, which only requires “one or more of”);
the token vector for the conversational input further comprises one or more CLS tokens (noting that this limitation is not required by the claim language, which only requires “one or more of”); or
the entity recognizing layer comprises a fourth feedforward layer and a softmax layer (noting that this limitation is not required by the claim language, which only requires “one or more of”).
Consider claim 20, Mathias discloses: the immediate prior one or more conversational inputs and the conversational input occur in a time session of a conversation (the context data includes time data, such as time of receipt of the audio data, [0063], for a dialog session with multiple utterances, [0070]).
Consider claim 21, Mathias discloses the one or more entities and the one or more expected entities comprise at least one of a meaningful or known entity or an empty context entity (<city> is an expected entity for the slot associated with the intent, [0116], and “San Francisco” is a known entity that fills the slot since it was previously encountered in the recent dialog history, [0113]-[0115]).
Consider claim 22, Mathias discloses the one or more entities and the one or more expected entities comprise at least one of a meaningful or known entity or an empty context entity (<city> is an expected entity for the slot associated with the intent, [0116], and “San Francisco” is a known entity that fills the slot since it was previously encountered in the recent dialog history, [0113]-[0115]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse Pullias whose telephone number is 571/270-5135. The examiner can normally be reached on M-F 8:00 AM - 4:30 PM. The examiner’s fax number is 571/270-6135.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Andrew Flanders can be reached on 571/272-7516.
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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 05/26/26
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