DETAILED ACTION
Notice of 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 .
Response to Arguments
Applicant’s arguments and remarks submitted on 9/8/2026 have been considered. Claims 1-20 are pending. No claims were amended in this communication.
On pages 7-9 of Applicant’s 9/8/2026 Arguments and remarks, Applicant asserts that paras. 0076-0079 of the instant specification explain a technical problem “padding tokens left in the data structure can be learned by the second model, reducing prediction accuracy) and a specific technical solution (normalize the stored utterance with padding, generate a padding-free converted data structure before the second model operates on it, and call the second model only when the first model has been determined that the utterance lacks sufficient information.”
The examiner respectfully disagrees. As explained in the detailed rejections, the following limitations relied upon by Applicant are mental processes:
store each word of the utterance in a corresponding slot of a data structure to create a stored utterance
normalize a size of the stored utterance by adding padding to the data structure
generate a converted data structure with the padding removed
Applicant does not attempt to rebut that these limitations are mental processes. Instead, Applicant proceeds to Step 2A, Prong 2 (but cites to MPEP 2106.05(a)), and argues that these limitations reflect an improvement to the functioning of the machine learning models. However, MPEP 2106.05(a) states: “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” Here, Applicant is citing to the judicial exception for the improvements without identifying any additional elements that integrate the judicial exception into a practical application. The recited “vehicle”, “processor”, “audio sensor”, and “first ML model” and “second ML model” are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. (See MPEP 2106.05(f)).
On page 9 of Applicant’s 9/8/2026 Arguments and remarks, Applicant argues that the examiner has not evaluated the “claim as an ordered combination.”
The examiner respectfully disagrees. Every limitation has been considered. The entire analysis under 35 U.S.C. 101 is consistent with the MPEP and the USPTO’s Subject Matter Eligibility Examples, which perform a limitation-by-limitation analysis similar to what was performed in the office action. Moreover, page 9 of the 7/16/2026 office action specifically explains that “Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.” (emphasis added).
Applicant specifically argues that the mental processes of storing, padding, and padding-removal should have been “considered together with the two ML models when determining whether the claim reflects an improvement.” However, implementing mental processes (the “storing, padding, and padding-removal”) using generic computing components (the 2 ML models) is not a technological improvement or any improvement to computer technology or other technical field.
On pages 9-10 of Applicant’s 9/8/2026 Arguments and remarks, Applicant argues that the “Examiner’s reliance on MPEP 2106.05(f) is misplaced.”
The examiner respectfully disagrees. As explained above, Applicant’s arguments merely refer to the implementation of mental processes using generic machine learning models. As explained by MPEP 2106.05(f), “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.”
A higher-level review of the claims confirms this. In general, the claim relates to the mental concept of determining if an utterance lacks sufficient information to determine a task. This mental concept is applied to the field-of-use of vehicles, and generic computing components (processors and 2 ML models) are used implement the mental process. Applicant has attempted to recite a particular “data structure”, with padding and padding removal steps, but these limitations merely reflect the mental store and processes that a human can utilize when determining whether an utterance lacks specific information. This data structure is not a technological improvement.
On page 10 of Applicant’s 9/8/2026 Arguments and remarks, Applicant now argues that “the claim cannot be performed mentally.”
The examiner respectfully disagrees. Applicant’s argument is essentially that because Applicant has chosen to claim that a ML model performs certain limitations (storing, padding, and padding removal-steps), that the examiner erred by considering whether such limitations, without the ML models, could also be performed mentally in the human mind. The examiner respectfully disagrees. The MPEP is clear that merely using a generic computer does not render an abstract idea eligible. MPEP 2106.05(f). The examiner properly considered whether the storing, padding, and padding-removal steps are an abstract idea that is merely implemented using a generic computer.
On pages 10-11 of Applicant’s 9/8/2026 Arguments and remarks, with respect to Step 2B, Applicant argues that the examiner “provides no finding or evidence that the combination of normalizing a stored utterance by adding padding, generating a converted data structure with the padding removed, and causing a second ML model called by a first ML model to provide a prompt based on the converted data structure is well-understood, routine, and conventional.”
The examiner respectfully disagrees. As explained by MPEP 2106.05(d), the “well-understood, routine, and conventional” analysis is merely a consideration under Step 2B. It is not a standalone or mandated test. The examiner agrees that there is no factual evidence, either way, about whether the present claims recite “well-understood, routine, and conventional” activities or not. Therefore, the examiner finds that this consideration does not favor, nor disfavor, a finding of eligible subject matter.
On pages 12-14 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, “Applicant objects to the Examiner’s piecemeal examination of the above feature of claim 1.”
The examiner respectfully disagrees. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In particular, Applicant argues that the mapping based on BACHRACH and DANG is “internally inconsistent.” The examiner respectfully disagrees.
BACHRACH is relied upon to show that utterances can be stored in a particular data structure. See pages 34-37 of the 9/8/2026 office action. However, BACHRACH does fail to explicitly teach the entirety of the “generate a converted data structure with the padding removed” limitation because BACHRACH does not teach padding, but the concept of generating a data structure has already been taught. Then, as explained by the office action, it’s the combination of BALIGAR, ATLURI, and BACHRACH that teaches the “cause, by the second ML model, a prompt to be provided to the user based on the converted data structure” because ATLURI teaches converting speech to text using the information of the data structure of BACHRACH, where the future removal of padding (as taught by DANG) is trivial because the data structure of BACHRACH has all of the information that the teachings of ATLURI and BACHRACH need to cause the prompt to be provided to the user. In other words, DANG merely teaches the unremarkable proposition of removing padding from a data structure. The structure of BACHRACH has all the information that is needed to create a prompt, and one of ordinary skill would understand that.
On pages 15-17 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that DANG does not teach the “generate a converted data structure with the padding removed” limitation.
The examiner respectfully disagrees. In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
BACHRACH teaches the concept of generating a data structure that has padding that may even use a special <PAD> token. DANG teaches the unremarkable concept of removing padded information in para. 0112. DANG does not need to specifically teach “operat[ing] on a padded structure to remove the pads” as argued by Applicant, as one of ordinary skill would understand, given the BACHRACH and DANG disclosures, that DANG’s teachings of padding removal could be applied to the padded data structure of BACHRACH.
On pages 17-18 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant makes several arguments with respect to the “in response to the utterance lacking the sufficient information.”
To the best that the examiner can understand Applicant’s arguments, the examiner disagrees as Applicant appears to be misreading the office action. The “in response to the utterance lacking the sufficient information” limitation is taught by BALIGAR as disclosed on page 25 of the 7/16/2026 office action. ATLURI, BACHRACH, and DANG are not relied upon for that particular limitation.
The combination of BALIGAR, ATLURI, BACHRACH, and DANG is relied upon for the “generate a converted data structure with the padding removed” as explained above, and that limitation is where the examiner’s note states that “BACHRACH discloses storing text data in character arrays (corresponding to recited “data structure”); the BALIGAR-ATLURI-BACHRACH-DANG combination now removes padding as disclosed by DANG).” As explained above, BACHRACH teaches the concept of a data structure having padding, and DANG simply teaches removing the padding.
Applicant then proceeds to argue:
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The examiner does not agree with this argument. The proposed combination relies on text being stored in the character arrays of BACHRACH, and <PAD> tokens can be used as explained by BACHRACH. The claim language and combination do not rely on any such “stripping punctuation and similar characters” or there being a “shorter padded array.” There is simply an array, having characters and pads, and the teachings of DANG can be used to remove such pads.
Applicant then argues:
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Applicant goes backwards, and applies the statement of the motivation to combine BACHRACH with ATLURI and BALIGAR for different limitations. Applicant’s manufactured arguments assume that the padding of BACHRACH was adopted “for efficiency,” without providing any cites or evidence why. The examiner acknowledges that having a pre-sized character array can be efficient in its own way. As known to one of ordinary skill in the art, an array typically requires memory allocation at time of instantiation in order to reserve continuous blocks of memory. One of ordinary skill would also understand that arrays are efficient precisely because of this memory allocation, as opposed to other data structures that can dynamically grow, such as linked lists, and that there are tradeoffs for such efficiency. One of ordinary skill would understand that one of these tradeoffs is that the padding is taking up unnecessary space in memory. As explained on page 38 of the 7/16/2026 office action, one of ordinary skill would have been motivated to use DANG to remove padding “in order to reduce unwanted tokens from being processed by a machine learning model, which would require additional computational resources.” The fact that Applicant asserts that one of ordinary skill may have found reasons NOT to remove the padding is immaterial. Reasonable minds may disagree, but the examiner has identified a particular reason why one of ordinary skill would have been motivated to remove the padding, and that motivation is enough to provide the combination.
In response to Applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning (see page 18), it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Finally, on page 18, Applicant argues that the “combination also supplies no connection between the condition and the operation.” However, Applicant’s arguments ignore the office action’s explanation for why BACHRACH would be used to store data in a character array. Moreover, Applicant overlooks that a person of ordinary skill is a person of “ordinary creativity, not an automaton.” MPEP 2141. Applicant’s attempts to distinguish the previous prior art of record (BALIGAR and ATLURI), by claiming a data structure with padding (and then removing the padding), ignores the level of ordinary skill in the art, where such basic computer science techniques are commonplace. The examiner has presented the BACHRACH and DANG references to demonstrate such basic features, but other references could easily be substituted for such basic concepts.
On pages 18-20 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the ATLURI reference does not teach the “call, by the first ML model, a second ML model” limitation.
The examiner respectfully disagrees. Pages 32-33 of the 7/16/2026 office action explain that the first ML model is action handler component 330, which may invoke other components, including Q&A component 334, corresponding to the second ML model.
On page 19, Applicant argues that “the Examiner cites ATLURI col. 4, lines 11-32, regarding the action handler’s role in the insufficiency determination.” In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant has ignored that the combination of ATLURI and BALIGAR teaches this, where BALIGAR teaches the general concept of determining if there is sufficient information for a task, and ATLURI just applies machine learning models to perform what the generic computing of BALIGAR does.
Applicant then points to ATLURI, col. 12, lines 49-54, which is not cited by the office action, and argues that the action handler of ATLURI does not make the decision about whether the utterance is sufficient, but rather, outsources that to an argument filling component 226. The examiner respectfully disagrees. While one of ordinary skill could certainly envision various ways to achieve the objective of determining whether information is sufficient, Applicant’s arguments ignore the combination actually proposed in the office action, where the teachings of BALIGAR are used to determine the sufficiency of information, and those teachings are now implemented in the action handler of ATLURI which is a machine learning model.
On page 20 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that one of ordinary skill would not have been motivated to combine the BALIGAR and ATLURI references.
The examiner respectfully disagrees. Again, Applicant is making a strawman argument, manufacturing an overly-complicated combination and arguing that one of ordinary skill wouldn’t be motivated to implement such an artificially complicated combination. But as explained on pages 31-32 of the 7/16/2026 office action, ATLURI teaches that its components can be generally implemented using ML techniques, and the rejection explains that the voice assistant of BALIGAR can use the “action handler component 330 as in ATLURI to determine tasks of the vehicle (such as asking the vehicle, including the assistant of the vehicle, to perform a task such as looking up information)”, where ATLURI is relied upon: (1) for the field-of-use of applying a voice assistant to a vehicle, and (2) that software components can be implemented using generic machine learning. The present claims simply add generic machine learning models, without any specific limitations about how the machine learning models are constructed. Therefore, the rejection is similarly at a high-level – the determination of sufficiency of information is taught by BALIGAR, and is now implemented using machine learning techniques in a vehicle as in ATLURI. The office action sets forth one reason one of ordinary skill would have been motivated to combine the references as explained on page 34 of the 7/16/2026 office action, and Applicant has not rebutted that specific motivation to combine.
On pages 21-22 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the prior art does not teach the “cause, by the second ML model, a prompt to be provided to the user based on the converted data structure.”
The examiner respectfully disagrees for the same reasons explained above. BACHRACH teaches a data structure having all of the information needed to generate a prompt using the teachings of BALIGAR and ATLURI as explained above.
In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant’s arguments do not address the actual combination set forth in the office action.
On pages 22-24 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues, in general, that the prior art combination would not “result in the feature” (without identifying a claimed feature) and saying there is no motivation to combine.
The examiner respectfully disagrees. Again, Applicant creates a strawman argument that is divorced from the actual claim language, and manufactures overly-complicated scenarios and argues that one of ordinary skill would not combine the art in the overly-complicated manner asserted by Applicant. Applicant argues that “The Examiner states that in the combination the Q&A component 334 ‘can iteratively request information as taught by BALIGAR’, and that BACHRACH’s character array ‘is utilized by the Q&A module 334 of ATLURI.”
Page 33 of the 7/16/2026 office action does explain, in Examiner’s Notes, that the BALIGAR-ATLURI combination teaches applying the teachings of BALIGAR (as explained on pages 24-26) using the machine learning model implementations of ATLURI. The examiner stands by this high-level explanation in response to high-level claim language. But Applicant attempts to go farther and argues why the implementation details of ATLURI would prevent it from implementing the teachings of BALIGAR. The examiner respectfully disagrees. One of ordinary skill as of the effective filing date of the invention would understand that basic computer techniques (such as those taught by BALIGAR to identify if additional information is needed to perform a task) can now be implemented using machine learning models (as disclosed by ATLURI). The office action is not relying on the particular implementation details that ATLURI uses to solve the specific problem ATLURI was trying to solve.
On pages 24-26 of Applicant’s 9/8/2026 Arguments and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the remaining dependent claims should be allowed for the same reasons argued with respect to claim 1.
The examiner respectfully disagrees for the same reasons argued with respect to claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-8 are directed to an apparatus (a machine), Claims 9-16 are directed to a method (a process), and Claims 17-20 are directed to a “non-transitory computer-readable medium” (an article of manufacture) which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “audio sensor”, “processor”, “machine learning model”, and “virtual assistant”).
determine, ... that the utterance lacks sufficient information to determine a task to be performed by the vehicle (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can listen/read an utterance mentally and then mentally determine that there is not enough information available to determine a task to be performed by the vehicle, such as the human hearing an utterance of “umm umm umm” and knowing there is not enough information to determine a task)
determine, ... that sufficient information is available to determine the action based on the additional utterance (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can listen/read additional utterances mentally and then mentally determine that there is now enough information available to determine an action, e.g., the human assistant mentally determines that the human assistant has enough information in order to understand and carry-out the user’s request)
store each word of the utterance in a corresponding slot of a data structure to create a stored utterance (under the broadest reasonable interpretation, a human can perform this limitation mentally or using pencil and paper, such as by writing down each word of the utterance in an array on a piece of paper)
normalize a size of the stored utterance by adding padding to the data structure (under the broadest reasonable interpretation, a human can perform this limitation mentally or using pencil and paper, such as by writing down each word of the utterance in an array on a piece of paper, and normalizing the array by adding padding so that each array is the same size)
in response to the utterance lacking the sufficient information, ... (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can listen/read additional utterances mentally and then mentally determine that there is now enough information available to determine an action)
generate a converted data structure with the padding removed (under the broadest reasonable interpretation, a human can perform this limitation mentally or using pencil and paper, such as by writing down each word of the utterance in an array on a piece of paper, and normalizing the array by adding padding so that each array is the same size, and then deleting such padding at a later point in time by erasing them)
determine, ... the task based on the utterance and the additional utterances (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human can mentally determine the task to be performed based on the utterances, e.g., can predict that the user wants the virtual assistant of the vehicle to answer a question)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “receiver”, “processor”, “machine learning model”, and “virtual assistant”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “A vehicle” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (e.g., the mental processes are now limited to being performed within a vehicle). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “a processor is configured to” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a processor. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “call a first machine learning (ML) model in response to an audio sensor receiving an utterance from a user” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of calling a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (calling a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “via the first ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Regarding the “call, by the first ML model, a second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of calling a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (calling a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “cause, by the second ML model, a prompt to be provided to the user based on the converted data structure” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “receive, via the audio sensor, an additional utterance from the user in response to the prompt” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “cause the vehicle to perform the task” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “receiver”, “processor”, “machine learning model”, and “virtual assistant”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “A vehicle” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding the “a processor is configured to” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “call a first machine learning (ML) model in response to an audio sensor receiving an utterance from a user” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “via the first ML model” limitations, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “call, by the first ML model, a second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “cause, by the second ML model, a prompt to be provided to the user based on the converted data structure” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “receive, via the audio sensor, an additional utterance from the user in response to the prompt” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “cause the vehicle to perform the task” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Regarding Claim 2
Step 2A, Prong 1
prompt the user for the additional utterance based on content included in a most recently received utterance (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can listen/read additional utterances mentally and then mentally determine that additional information is needed, and therefore make a mental decision to prompt the user for an additional utterance)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 3
Step 2A, Prong 2
Regarding the “wherein the prompt is provided by a virtual assistant of the vehicle, and the user is a passenger within the vehicle” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (automotive assistants). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the prompt is provided by a virtual assistant of the vehicle, and the user is a passenger within the vehicle” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 4
Step 2A, Prong 1
request the user to confirm content included in a previously received. (under the broadest reasonable interpretation, this limitation merely relates to the social activity of having a conversation with another person, and requesting the user to confirm content in a previously received utterance)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
predict an intent of the user after each utterance based on an aggregation of utterances during a conversation with the user (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can review the aggregation of utterances and then mentally predict an intent of the user, e.g., the intent of the user is to receive information)
when ... determines that the sufficient information is available ... determine that the sufficient information is available based on the aggregation of utterances (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can mentally determine that there is enough information available in order to understand and carry-out the user’s request)
Step 2A, Prong 2
Regarding the “the processor” and “the processor is configured to” limitations, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of a processor. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components (a processor). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “the processor” and “the processor is configured to” limitations, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 6
Step 2A, Prong 1
determine, ... that a sequence of utterances is required to make a prediction based on the utterance. (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can mentally determine that multiple utterances will be needed to collect sufficient information from a user)
Step 2A, Prong 2
Regarding the “via the first ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “via the first ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 7
Step 2A, Prong 1
predict, ... a next action to be taken ... based on the utterance and a previously received utterance associated with a same task (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can mentally predict a next action to be taken based on the received utterances, e.g., can mentally predict that the next action will be to do research to find an answer to the user’s question)
Step 2A, Prong 2
Regarding the “wherein the prompt is provided by a virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a virtual assistant. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a virtual assistant). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “via the second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “by the virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a virtual assistant. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a virtual assistant). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the prompt is provided by a virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “via the second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “by the virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 8
Step 2A, Prong 1
identify, ... a word of interest within the additional utterance and an additional question ... to ask the user based on the word of interest (under the broadest reasonable interpretation, a human can perform this limitation mentally, e.g., a human such as a human assistant can identify a word of interest within an utterance and then based on such word of interest, form an additional question mentally to ask the user)
Step 2A, Prong 2
Regarding the “wherein the prompt is provided by a virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a virtual assistant. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a virtual assistant). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “via the second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “for the virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a virtual assistant. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a virtual assistant). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the prompt is provided by a virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “via the second ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “for the virtual assistant” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 9
Step 2A, Prong 1
Claim 9 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (e.g., “machine learning model”, and “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 9 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (e.g., “machine learning model”, and “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 9 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (e.g., “machine learning model”, and “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2B
Claims 10-16 depend from claim 9, and correspond to the apparatuses of claims 2-8, and are therefore rejected for the same reasons explained above with respect to claim 9 and claims 2-8, respectively.
Regarding Claim 17
Step 2A, Prong 1
Claim 17 recites a non-transitory computer-readable medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (e.g., “non-transitory computer-readable medium”, “processor”, “machine learning model”, “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 17 recites a non-transitory computer-readable medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (e.g., “non-transitory computer-readable medium”, “processor”, “machine learning model”, “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 17 recites a non-transitory computer-readable medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (e.g., “non-transitory computer-readable medium”, “processor”, “machine learning model”, “virtual assistant”, and “audio sensor”), such additional generic computing components do not change the analysis under Step 2B.
Claims 18-20 depend from claim 17, and correspond to the apparatuses of claims 2-4, and are therefore rejected for the same reasons explained above with respect to claim 17 and claims 2-4, respectively.
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 1-5, 7, 9-13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 10997963 B1, hereinafter referenced as BALIGAR in view of US 12204866 B1, hereinafter referenced as ATLURI, and further in view of US 20190236155 A1, hereinafter referenced as BACHRACH, and further in view of US 20230259717 A1, hereinafter referenced as DANG.
Regarding Claim 1
BALIGAR teaches:
a processor configured to (BALIGAR, col. 5, lines 48-57: “FIG. 2 is a block diagram of an illustrative computing architecture 200 of a voice assistant service, such as the voice assistant service 102. The computing architecture 200 may be implemented in a distributed or non-distributed computing environment. The computing architecture 200 may include one or more processors 202 and one or more computer-readable media 204 that stores various modules, applications, programs, or other data. The computer-readable media 204 may include instructions that, when executed by the one or more processors 202, cause the processors to perform the operations described herein.”)
determine, ... that the utterance lacks sufficient information to determine a task to be performed, (BALIGAR, col. 8, line 60 – col. 9, line 5: “At 408, the voice assistant service 102 may determine whether to request more information to determine a context of an audio request. For example, when the voice assistant service 102 includes enough information to understand and respond to an audio request that is supplemented by context information derived from the context queue, then the voice assistant service 102 may not request additional information from the user. When the voice assistant service 102 does not include enough information to understand and respond to the audio request that is supplemented by context information derived from the context queue (following the “yes” route from the decision operation 408), then the process 400 may advance to an operation 410.”;
Examiner’s Note: the voice assistant determines if enough information is available to understand and respond to the user’s audio request in order for the assistant to “respond to an audio request” (where such response corresponds to the recited “performance of a task by the system”))
in response to the utterance lacking the sufficient information: (BALIGAR, col. 9, lines 6-13: “At 410, the voice assistant service 102 may request additional information from the user by sending an audio request to the user via the voice assistant device or voice assistant application. The request may be a choice, such as “did you mean “buy a toaster or diapers”. In some instances, the response may be a request for more specific information, such as “Please provide more information so I can fulfill your request”.”;
BALIGAR, col. 9, lines 14-23: “When the voice assistant service 102 does include enough information to understand and respond to the audio request that is supplemented by context information derived from the context queue (following the “no” route from the decision operation 408), then the process may advance to an operation 412. At 412, the voice assistant service 102 may implement supplemented audio request. For example, the voice assistant service 102 may provide an audio response to the user via the voice assistant device or voice assistant application.”;
Examiner’s Note: BALIGAR discloses requesting additional information from the user, which is received via user speech input, when additional information is required to fulfill the user’s request)
cause, ... a prompt to be provided to the user ... (BALIGAR, col. 9, lines 6-13: “) At 410, the voice assistant service 102 may request additional information from the user by sending an audio request to the user via the voice assistant device or voice assistant application. The request may be a choice, such as “did you mean “buy a toaster or diapers”. In some instances, the response may be a request for more specific information, such as “Please provide more information so I can fulfill your request”)
receive, via the audio sensor, an additional utterance from the user in response to the prompt, (BALIGAR, col. 4, lines 1-5: “Instead, the voice assistant device 110 may receive input from users by receiving spoken commands, which are converted to signals by the voice assistant device 110 and/or by a cloud service, and then processed, such as by an exchange of data with voice assistant service 102.”;
BALIGAR, col. 2, lines 11-13: “The voice assistant system may include a user device that typically includes at least a network interface, a microphone, and a speaker.”
BALIGAR, col. 5, lines 10-13: “Meanwhile, the voice assistant service 102 may receive a message 122 of the audible request of “buy this”, which was received via a microphone of the voice assistance device 110.”;
BALIGAR, col. 8, line 60 – col. 9, line 5: “At 408, the voice assistant service 102 may determine whether to request more information to determine a context of an audio request. For example, when the voice assistant service 102 includes enough information to understand and respond to an audio request that is supplemented by context information derived from the context queue, then the voice assistant service 102 may not request additional information from the user. When the voice assistant service 102 does not include enough information to understand and respond to the audio request that is supplemented by context information derived from the context queue (following the “yes” route from the decision operation 408), then the process 400 may advance to an operation 410.”;
Examiner’s Note: voice assistant device 110 has a microphone (corresponding to recited “audio sensor”) that receives additional speech inputs from users in response to the request for more information in operation 410)
determine, ... the task based on the utterance and the additional utterance, and (BALIGAR, col. 9, lines 19-23: “At 412, the voice assistant service 102 may implement supplemented audio request. For example, the voice assistant service 102 may provide an audio response to the user via the voice assistant device or voice assistant application.”;
Examiner’s Note: for example, a task may be to provide the user with information in the form of an audio response)
... perform the task. (BALIGAR, col. 12, lines 13-18: “At 618, the voice assistant service 102 may transmit a request to the content provider to cause a refresh of information served to a device associated with the user who made the audio request, to cause output of more books, such as by graphically outputting recommended books similar to the book referenced via the contextual information.”;
Examiner’s Note: BALIGAR provides several examples of a voice assistant transmitting requests to other systems (such as a content provider) to perform a task requested by the user, such as causing the system to output an audio book)
However, BALIGAR fails to explicitly teach:
A vehicle comprising:
call a first machine learning (ML) model in response to an audio sensor receiving an utterance from a user
... via the first machine learning (ML) model ... by the vehicle ...
store each word of the utterance in a corresponding slot of a data structure to create a stored utterance
normalize a size of the stored utterance by adding padding to the data structure
generate a converted data structure with the padding removed
call, by the first ML model, a second ML model
by the second ML model ... based on the converted data structure,
... controlled by a second ML model, different than the first ML model ...
... via the first ML model ...
cause the vehicle ...
However, in a related field of endeavor (automated speech recognition and natural language understanding, see col. 2, lines 7-18), ATLURI teaches and makes obvious:
A vehicle comprising: (ATLURI, col. 45, lines 7-13: “For example, a speech-controllable device 110a, a smart phone 110b, a smart watch 110c, a tablet computer 110d, a vehicle 110e, a speech-controllable display device 110f, a smart television 110g, a washer/dryer 110h, a refrigerator 110i, and/or a microwave 110j may be connected to the network(s) 199 through a wireless service provider, over a Wi-Fi or cellular network connection, or the like.”;
Examiner’s Note: the BALIGAR-ATLURI combination now implements the voice assistant of BALIGAR into the vehicle of ATLURI as depicted in Fig. 11 of ATLURI)
call a first machine learning (ML) model in response to an audio sensor receiving an utterance from a user (ATLURI, col. 5, lines 11-32: “The search skill component 190a may include at least an action handler component 330 and a suggested user input component 340, which are described in further detail below in relation to FIGS. 3-5. The search skill component 190a may process the ASR data and determine an action to be performed in response to the user input. The action handler component 330 may determine to search one or more data sources for information relating to an entity included in the user input. For example, for the user input “show me gifts for mom,” the action handler component 330 may determine to search a retail catalog for products. As another example, for the user input “what is happening in California,” the action handler component 330 may determine to search news articles. As described below in relation to FIG. 3, the action handler component 330 may invoke one or more components to retrieve item results corresponding to the information requested in the user input. The action handler component 330 may determine which of the retrieved item results are to be present to the user 105. Such determination may be based on user profile data associated with the user 105.”
ATLURI, col. 8, lines 32-38: “The input data 202 may be generated by a user via a keyboard, touchscreen, microphone, camera, or other such input device associated with the device 110. In other embodiments, the input data 202 is generated by the ASR component 150, as described herein, from audio data corresponding to a spoken input received from the user 105.”;
ATLURI, col. 21, lines 26-32: “In some embodiments, the action handler component 330 may use rules-based processing and/or ML models to determine which component(s) to invoke based on the action data 322. The action handler component 330 may process with respect to each of the actions included in the N-best list in the action data 322. In some embodiments, the rules and/or ML models may be domain-specific.”
ATLURI, col. 42, lines 48-54: “One or more of the components described herein may employ a machine learning (ML) model(s). Generally, ML models may be trained and operated according to various machine learning techniques. Such techniques may include, for example, neural networks (such as deep neural networks and/or recurrent neural networks), inference engines, trained classifiers, etc.”;
Examiner’s Note: ATLURI discloses that components, including the action handler component 330 (corresponding to the recited “first ML model”) can be implemented using machine learning models and techniques; the BALIGAR-ATLURI combination now implements the voice assistant of BALIGAR into the vehicle of ATLURI, such that the voice assistant of BALIGAR utilizes ASR as in ATLURI to determine tasks of the vehicle (such as asking the vehicle, including the assistant of the vehicle, to perform a task such as looking up information))
determine, via the first machine learning (ML) model, that the utterance lacks sufficient information to determine a task to be performed, by the vehicle (ATLURI, col. 5, lines 11-32: “The search skill component 190a may include at least an action handler component 330 and a suggested user input component 340, which are described in further detail below in relation to FIGS. 3-5. The search skill component 190a may process the ASR data and determine an action to be performed in response to the user input. The action handler component 330 may determine to search one or more data sources for information relating to an entity included in the user input. For example, for the user input “show me gifts for mom,” the action handler component 330 may determine to search a retail catalog for products. As another example, for the user input “what is happening in California,” the action handler component 330 may determine to search news articles. As described below in relation to FIG. 3, the action handler component 330 may invoke one or more components to retrieve item results corresponding to the information requested in the user input. The action handler component 330 may determine which of the retrieved item results are to be present to the user 105. Such determination may be based on user profile data associated with the user 105.”
ATLURI, col. 8, lines 32-38: “The input data 202 may be generated by a user via a keyboard, touchscreen, microphone, camera, or other such input device associated with the device 110. In other embodiments, the input data 202 is generated by the ASR component 150, as described herein, from audio data corresponding to a spoken input received from the user 105.”;
ATLURI, col. 42, lines 48-54: “One or more of the components described herein may employ a machine learning (ML) model(s). Generally, ML models may be trained and operated according to various machine learning techniques. Such techniques may include, for example, neural networks (such as deep neural networks and/or recurrent neural networks), inference engines, trained classifiers, etc.”;
Examiner’s Note: ATLURI discloses that components, including the action handler component 330 (corresponding to the recited “first ML model”) can be implemented using machine learning models and techniques; the BALIGAR-ATLURI combination now implements the voice assistant of BALIGAR into the vehicle of ATLURI, such that the voice assistant of BALIGAR utilizes the action handler component 330 as in ATLURI to determine tasks of the vehicle (such as asking the vehicle, including the assistant of the vehicle, to perform a task such as looking up information))
call, by the first ML model, a second ML model (ATLURI, col. 21, lines 26-32: “In some embodiments, the action handler component 330 may use rules-based processing and/or ML models to determine which component(s) to invoke based on the action data 322. The action handler component 330 may process with respect to each of the actions included in the N-best list in the action data 322. In some embodiments, the rules and/or ML models may be domain-specific.”;
Examiner’s Note: the BALIGAR-ATLURI combination now has the action handler component 330 of ATLURI call a second model that can be implemented using machine learning, such as Q&A module 334 as depicted in Fig. 3)
cause, by the second ML model, a prompt to be provided to the user (ATLURI, col. 25, lines 37-42: “For example, the action handler component 330 may select the system response to be “AnswerQuestion” and may invoke the Q&A component 334 to retrieve item results, and may use the received item results to determine the system response data 348 (without invoking the item retrieval component 332 or the navigation component 336).”;
Examiner’s Note: the BALIGAR-ATLURI combination now has the action handler component 330 of ATLURI invoke Q&A module 334 as depicted in Fig. 3 to perform Q&A tasks, where such tasks can iteratively request additional information as taught by BALIGAR)
determine, via the first ML model, the task based on the utterance and the additional utterance (ATLURI, col. 25, lines 37-42: “For example, the action handler component 330 may select the system response to be “AnswerQuestion” and may invoke the Q&A component 334 to retrieve item results, and may use the received item results to determine the system response data 348 (without invoking the item retrieval component 332 or the navigation component 336).”;
Examiner’s Note: the BALIGAR-ATLURI combination now has the action handler component 330 of ATLURI invoke Q&A module 334 as depicted in Fig. 3 to perform Q&A tasks, where such tasks can iteratively request additional information as taught by BALIGAR, where the original request corresponds to the recited “utterance” and the follow-up requests of BALIGAR correspond to the recited “additional utterance”)
cause the vehicle to perform the task (ATLURI, col. 45, lines 7-13: “For example, a speech-controllable device 110a, a smart phone 110b, a smart watch 110c, a tablet computer 110d, a vehicle 110e, a speech-controllable display device 110f, a smart television 110g, a washer/dryer 110h, a refrigerator 110i, and/or a microwave 110j may be connected to the network(s) 199 through a wireless service provider, over a Wi-Fi or cellular network connection, or the like.”;
Examiner’s Note: the BALIGAR-ATLURI combination now implements the voice assistant of BALIGAR into the vehicle of ATLURI as depicted in Fig. 11 of ATLURI in order to perform tasks, such as Q&A tasks as disclosed by ATLURI)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI as explained above. As disclosed by ATLURI, one of ordinary skill would have been motivated to do so in order to provide “techniques for enabling a conversational search and browsing experience for a user, where the user may search and explore items or topics of interest using a multi-modal interface and a multi-turn dialog exchange. A system of the present disclosure can receive a spoken input from a user, respond to the spoken input by displaying search results, and receive a further spoken input interacting with the displayed search results.” (col. 2, lines 56-63).
However, BALIGAR and ATLURI fail to explicitly teach:
store each word of the utterance in a corresponding slot of a data structure to create a stored utterance
normalize a size of the stored utterance by adding padding to the data structure
generate a converted data structure with the padding removed
... based on the converted data structure
However, in a related field of endeavor (text processing by conversational agents, see para. 0001), BACHRACH teaches and makes obvious:
store each word of the utterance in a corresponding slot of a data structure to create a stored utterance (BACHRACH, para. 0033: “In certain examples, each text string 215 may be pre-processed. One method of pre-processing is text tokenization. Text tokenization splits a continuous sequence of characters into one or more discrete sets of characters, e.g. where each character is represented by a character encoding. ... FIG. 2 shows an example result of text tokenization performed on the sequence of strings 210 in the form of character set arrays 220. For example, character set array 225—[‘how’, ‘can’, ‘i’, ‘help’, ‘?’]— is generated by tokenizing string 215—“How can I help?”. Each character set array may be of a different length. In certain cases, a maximum array length may be set, e.g. as 50 or 100 tokens. In these cases, entries in the array that follow the end of a message may be padded with a special token (e.g. <PAD>).”
Examiner’s Note: BACHRACH discloses storing text data in character arrays (corresponding to recited “data structure”); the BALIGAR-ATLURI-BACHRACH combination now uses the ASR of ATLURI to convert speech to text, and then stores such text utterances in character arrays as disclosed by BACHRACH)
normalize a size of the stored utterance by adding padding to the data structure (BACHRACH, para. 0033: “In certain examples, each text string 215 may be pre-processed. One method of pre-processing is text tokenization. Text tokenization splits a continuous sequence of characters into one or more discrete sets of characters, e.g. where each character is represented by a character encoding. ... FIG. 2 shows an example result of text tokenization performed on the sequence of strings 210 in the form of character set arrays 220. For example, character set array 225—[‘how’, ‘can’, ‘i’, ‘help’, ‘?’]— is generated by tokenizing string 215—“How can I help?”. Each character set array may be of a different length. In certain cases, a maximum array length may be set, e.g. as 50 or 100 tokens. In these cases, entries in the array that follow the end of a message may be padded with a special token (e.g. <PAD>).”
Examiner’s Note: BACHRACH discloses that each character array can be set as a specific size, and using special <PAD> tokens for any blanks; the BALIGAR-ATLURI-BACHRACH combination now uses the ASR of ATLURI to convert speech to text, and then stores such text utterances in character arrays as disclosed by BACHRACH, where the character arrays are in a specific size and filled with <PAD> tokens)
cause, by the second ML model, a prompt to be provided to the user based on the converted data structure (BACHRACH, para. 0033: “In certain examples, each text string 215 may be pre-processed. One method of pre-processing is text tokenization. Text tokenization splits a continuous sequence of characters into one or more discrete sets of characters, e.g. where each character is represented by a character encoding. ... FIG. 2 shows an example result of text tokenization performed on the sequence of strings 210 in the form of character set arrays 220. For example, character set array 225—[‘how’, ‘can’, ‘i’, ‘help’, ‘?’]— is generated by tokenizing string 215—“How can I help?”. Each character set array may be of a different length. In certain cases, a maximum array length may be set, e.g. as 50 or 100 tokens. In these cases, entries in the array that follow the end of a message may be padded with a special token (e.g. <PAD>).”
Examiner’s Note: BACHRACH discloses storing text data in character arrays (corresponding to recited “data structure”); the BALIGAR-ATLURI-BACHRACH combination now uses the ASR of ATLURI to convert speech to text, and then stores such text utterances in character arrays as disclosed by BACHRACH, where such character array is utilized by the Q&A module 334 of ATLURI when determining Q&A task information)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI and BACHRACH as explained above. As disclosed by BACHRACH, one of ordinary skill would have been motivated to do so in order to “efficiently provide responses to a large number of user queries.” (para. 0005). One of ordinary skill would understand that using a character array for storage is a straightforward and efficient structure that allows for direct access to looking up data.
However, BALIGAR, ATLURI, and BACHRACH fail to explicitly teach:
generate a converted data structure with the padding removed
However, in a related field of endeavor (natural language processing, see para. 0004), DANG teaches and makes obvious:
generate a converted data structure with the padding removed (DANG, para. 0112: “This is because, even if unwanted characters and pads to be removed or reduced by regularization are included in mini-batches, they would be given very small attention weights under optimized parameter values and, thus, contribute little to model accuracy.”;
Examiner’s Note: BACHRACH discloses storing text data in character arrays (corresponding to recited “data structure”); the BALIGAR-ATLURI-BACHRACH-DANG combination now removes padding as disclosed by DANG)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI, BACHRACH, and DANG as explained above. One of ordinary skill would be motivated to do so in order to reduce unwanted tokens from being processed by a machine learning model, which would require additional computational resources.
Regarding Claim 2
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
prompt the user for the additional utterance based on content included in a most recently received utterance. (BALIGAR, col. 8, lines 60-67: “At 408, the voice assistant service 102 may determine whether to request more information to determine a context of an audio request. For example, when the voice assistant service 102 includes enough information to understand and respond to an audio request that is supplemented by context information derived from the context queue, then the voice assistant service 102 may not request additional information from the user”;
Examiner’s Note: As shown in Fig. 4, in the first iteration the request for information is based on the initial utterance (corresponding to recited “most recently received utterance”) and can request more information at step 408 for an additional audio input from the user)
Regarding Claim 3
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
wherein the prompt is provided by a virtual assistant ... (BALIGAR, col. 2, lines 1-21: “The voice assistant system may include any system and/or device that receives audio commands from a user, processes the audio, possibly using speech to text algorithms and/or natural language processing (NLP) algorithms, to determine text, returns a reply based on the text, converts the reply to an audio output using text to speech algorithms, and causes a speaker to output the audio output. Examples of voice assistant systems include Alexa® provided by Amazon.com® of Seattle, Wash., Siri® provided by Apple Corp.® of Cupertino, Calif., and Cortana® provided by Microsoft Corp.® of Redmond, Wash. The voice assistant system may include a user device that typically includes at least a network interface, a microphone, and a speaker. The user device may be a smart phone, a dedicated device, and/or other devices controlled by users and located proximate to the users. The voice assistant system may include a service engine, which may be stored in a remote location (e.g., via remote computing devices such as in a cloud computing configuration, etc.), stored in a local device (e.g., a smartphone, a dedicated voice assistant device, etc.) and/or a combination of both.”)
However, BALIGAR fails to explicitly teach:
... of the vehicle, and the user is a passenger within vehicle
However, in a related field of endeavor (“establishing hands-free communications between an electronic device and a user via a software assistant” see para. 0002), NOY teaches:
... of the vehicle, and the user is a passenger within vehicle (ATLURI, col. 45, lines 7-13: “For example, a speech-controllable device 110a, a smart phone 110b, a smart watch 110c, a tablet computer 110d, a vehicle 110e, a speech-controllable display device 110f, a smart television 110g, a washer/dryer 110h, a refrigerator 110i, and/or a microwave 110j may be connected to the network(s) 199 through a wireless service provider, over a Wi-Fi or cellular network connection, or the like.”;
Examiner’s Note: the BALIGAR-ATLURI-BACHRACH-DANG combination now implements the voice assistant of BALIGAR into the vehicle of ATLURI as depicted in Fig. 11 of ATLURI)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI, BACHRACH, and DANG as explained above. As disclosed by ATLURI, one of ordinary skill would have been motivated to do so in order to provide “techniques for enabling a conversational search and browsing experience for a user, where the user may search and explore items or topics of interest using a multi-modal interface and a multi-turn dialog exchange. A system of the present disclosure can receive a spoken input from a user, respond to the spoken input by displaying search results, and receive a further spoken input interacting with the displayed search results.” (col. 2, lines 56-63).
Regarding Claim 4
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
request the user to confirm content included in a previously received utterance. (BALIGAR, col. 4, lines 16-21: “As discussed herein, the voice assistant service 102 may be configured to engage in a dialog to receive an order of one or more items from the user, including facilitating selection and confirmation of items, and cause those items to be fulfilled and delivered to the use, or for other tasks or fulfillment of audible requests.”;
BALIGAR, col. 11, lines 14-16: “A confirmation page or reply (possibly via audio) may confirm completion of this action.”;
Examiner’s Note: BALIGAR discloses asking the user to confirm ordered items via dialog)
Regarding Claim 5
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
wherein the processor is configured to predict an intent of the user after each utterance based on an aggregation of utterances during a conversation with the user, and (BALIGAR, col. 9, lines 19-23: “At 412, the voice assistant service 102 may implement supplemented audio request. For example, the voice assistant service 102 may provide an audio response to the user via the voice assistant device or voice assistant application.”;
Examiner’s Note: for example, the voice assistant can predict that the user has asked a question that needs to be responded to (e.g., the “intent” is the request for information), and as shown by Fig. 4, this can be after a number of iterations for additional information, where the cumulative user speech inputs corresponds to the recited “aggregation of utterances during a conversation”)
when the processor determines that the sufficient information is available, the processor is configured to determine that sufficient information is available based on the aggregation of utterances. (BALIGAR, col. 8, lines 60-67: “At 408, the voice assistant service 102 may determine whether to request more information to determine a context of an audio request. For example, when the voice assistant service 102 includes enough information to understand and respond to an audio request that is supplemented by context information derived from the context queue, then the voice assistant service 102 may not request additional information from the user”;
Examiner’s Note: As shown in Fig. 4, after step 410 (request for more information via audio in instances where not enough information is available), the flow returns to step 406 and then step 408 to analyze whether the additional information provided by the user is sufficient to understand and respond to the user’s request, where such repeated iterations resulting in cumulative user speech inputs corresponds to the recited “aggregation of utterances”)
Regarding Claim 7
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
predict, ... a next action to be taken by the virtual assistant based on the utterance and a previously received utterance associated with a same task. (BALIGAR, col. 9, lines 19-23: “At 412, the voice assistant service 102 may implement supplemented audio request. For example, the voice assistant service 102 may provide an audio response to the user via the voice assistant device or voice assistant application.”;
Examiner’s Note: for example, the voice assistant can predict that the user has asked a question that needs to be responded to and then predict the response, and as shown by Fig. 4, this can be after a number of iterations for additional information, where the cumulative user speech inputs corresponds to the recited “previously received utterance associated with a same task”)
However, BALIGAR fails to explicitly teach:
via the second ML model, ...
However, in a related field of endeavor (automated speech recognition and natural language understanding, see col. 2, lines 7-18), ATLURI teaches and makes obvious:
predict, via the second ML model, a next action to be taken by the virtual assistant based on the utterance and a previously received utterance associated with a same task. (ATLURI, col. 25, lines 37-42: “For example, the action handler component 330 may select the system response to be “AnswerQuestion” and may invoke the Q&A component 334 to retrieve item results, and may use the received item results to determine the system response data 348 (without invoking the item retrieval component 332 or the navigation component 336).”;
Examiner’s Note: the BALIGAR-ATLURI-BACHRACH-DANG combination now has the action handler component 330 of ATLURI invoke Q&A module 334 as depicted in Fig. 3 to perform Q&A tasks, where the next action is to provide the answer based on the utterances having sufficient information)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI, BACHRACH, and DANG as explained above. As disclosed by ATLURI, one of ordinary skill would have been motivated to do so in order to provide “techniques for enabling a conversational search and browsing experience for a user, where the user may search and explore items or topics of interest using a multi-modal interface and a multi-turn dialog exchange. A system of the present disclosure can receive a spoken input from a user, respond to the spoken input by displaying search results, and receive a further spoken input interacting with the displayed search results.” (col. 2, lines 56-63).
Claim 9 recites a method that corresponds to the vehicle of claim 1 and is therefore rejected for the same reasons explained above with respect to claim 1.
Claim 10 depends from claim 9 and claims a method that corresponds to the apparatus of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 9.
Claim 11 depends from claim 9 and claims a method that corresponds to the apparatus of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 9.
Claim 12 depends from claim 9 and claims a method that corresponds to the apparatus of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 9.
Claim 13 depends from claim 9 and claims a method that corresponds to the apparatus of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 9.
Claim 15 depends from claim 9 and claims a method that corresponds to the apparatus of claim 7, and is therefore rejected for the same reasons explained above with respect to claims 7 and 9.
Claim 17 recites a non-transitory computer-readable medium that corresponds to the vehicle of claim 1 and is therefore rejected for the same reasons explained above with respect to claim 1.
Claim 18 depends from claim 17 and claims a non-transitory computer-readable medium that corresponds to the apparatus of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 17.
Claim 19 depends from claim 17 and claims a non-transitory computer-readable medium that corresponds to the apparatus of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 17.
Claim 20 depends from claim 17 and claims a non-transitory computer-readable medium that corresponds to the apparatus of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 17.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over BALIGAR in view of PANDEY and NOY and further in view of US 20170132199 A1, hereinafter referenced as VESCOVI.
Regarding Claim 6
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. However, BALIGAR, ATLURI, BACHRACH, and DANG fail to explicitly teach:
wherein the processor is configured to determine, via the first ML model, that a sequence of utterances is required to make a prediction based on the utterance.
However, in a related field of endeavor, (virtual assistants, see para. 0003), VESCOVI teaches:
wherein the processor is configured to determine, via the first ML model, that a sequence of utterances is required to make a prediction based on the utterance. (VESCOVI, para. 0277: “As illustrated in the example of FIG. 8A, the user requests that the digital assistant let a visitor (in this example, Tomas) into his apartment when that visitor arrives. The digital assistant determines whether the user request corresponds to at least one of a plurality of plan templates 802, as described below in greater detail relative to FIGS. 9A-9F. A plan template 802 includes a set of instructions 804 and corresponding inputs/outputs 806. As illustrated in the example of FIG. 8B, a generic plan template 802 includes a set of ordered instructions 804, beginning with one or more instructions 804 to gather information.”;
VESCOVI, para. 0283: “Referring to FIGS. 8E-8H, the inputs 806 associated with the “time expected” and “date expected” instructions in this particular example are not optional. Because those inputs 806 are not optional, the digital assistant has insufficient information to generate a plan with this plan template 802 if no input 806 is received in association with either of the “time expected” or “date expected” instructions 804. “Sufficient information” is the minimum information with which the digital assistant can generate a plan. Because the digital assistant cannot generate a plan based on the plan template 802 if it does not receive inputs associated with both the “time expected” and “date expected” instructions 804, the digital assistant initiates communication with the user to request sufficient information to generate a plan based on the plan template. As shown in FIG. 8E, the digital assistant requests 810 from the user: “on what date is he coming?” As shown in FIG. 8F, the user replies 812 with “tonight.” The digital assistant recognizes that the word “tonight” is associated with the same date on which the user spoke the reply 812, and as a result obtains today's date from the calendar module 248 or other suitable source. The time at which the visitor is to arrive is still required, so as shown in FIG. 8G, the digital assistant requests 814 “what time is he coming?” As shown in FIG. 8H, the user replies with “about 8:00 p.m.” Having received information associated with both the “time expected” and “date expected” instructions 804, the digital assistant now has sufficient information to generate a plan based on the plan template 802.”;
Examiner’s Note: VESCOVI teaches a virtual assistant that uses plan templates 802, including plan templates that require multiple required inputs, in order to perform a task, where the multiple required inputs can be gathered based on a sequence of utterances (see Fig. 8H, requiring input utterances 812 and 816); the BALIGAR-ATLURI-BACRHACH-DANG-VESCOVI combination now modifies the voice assistant of BALIGAR to use the plan templates of VESCOVI to solicit multiple input utterances from users in order to carry out a prediction (e.g., answering a query as in BALIGAR) using the plan templates of VESCOVI.
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI, BACHRACH, DANG and VESCOVI as explained above. As disclosed by VESCOVI, one of ordinary skill would have been motivated to do so because such plan templates of VESCOVI enable “more complex actions, and actions that rely upon contingent inputs.” (para. 0302).
Claim 14 depends from claim 9 and claims a method that corresponds to the apparatus of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 9.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over BALIGAR in view of ATLURI, BACHRACH, and DANG and further in view of US 20230353516 A1, hereinafter referenced as BANERJEE.
Regarding Claim 8
BALIGAR, ATLURI, BACHRACH, and DANG disclose the vehicle of claim 1 as explained above. BALIGAR further teaches:
wherein the prompt is provided by a virtual assistant (BALIGAR, col. 9, lines 6-13: “) At 410, the voice assistant service 102 may request additional information from the user by sending an audio request to the user via the voice assistant device or voice assistant application. The request may be a choice, such as “did you mean “buy a toaster or diapers”. In some instances, the response may be a request for more specific information, such as “Please provide more information so I can fulfill your request”)
However, BALIGAR, ATLURI, BACHRACH, and DANG fail to explicitly teach:
wherein the processor is configured to: identify, via the second ML model, a word of interest within the additional utterance and an additional question for the virtual assistant to ask the user based on the word of interest.
However, in a related field of endeavor (AI voice systems for interacting with users, see para. 0001), BANERJEE teaches:
identify, via the second ML model, a word of interest within the additional utterance and an additional question for the virtual assistant to ask the user based on the word of interest. (BANERJEE, para. 0092: “In either case, the machine learning algorithm determines the next question or action based on the user response. In the case of a first question in the form of a menu selection, the next question or action from the system is likely predetermined based on the selection. In the case of a first question asking for a free-form spoken or text input by the user, the system may analyze the response for keywords or keyword patterns to determine the next question or action.”);
Examiner’s Note: BANERJEE teaches using a machine learning algorithm to analyze a user speech input for keywords or keyword patterns (corresponding to recited “word of interest within an additional utterance”) and using such keywords to determine a next question or action (corresponding to recited “additional question for the virtual assistant to ask the user based on the identified word of interest”); the BALIGAR-ATLURI-BACHRACH-DANG-BANERJEE combination now modifies the voice assistant of BALIGAR to use the machine learning algorithms of BANERJEE to analyze user speech for keywords and then to determine the next question for the voice assistant of BALIGAR to ask based on such identified keyword of BANERJEE).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BALIGAR with the teachings of ATLURI, BACHRACH, DANG, and BANERJEE as explained above. As disclosed by BANERJEE, one of ordinary skill would have been motivated to do so in order to use an “AI algorithm [that] adaptively guides the dialog to achieve the most favorable outcome based on the current status of the dialog.” (para. 0005).
Claim 16 depends from claim 9 and claims a method that corresponds to the apparatus of claim 8, and is therefore rejected for the same reasons explained above with respect to claims 8 and 9.
Conclusion
THIS ACTION IS MADE FINAL. 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 MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
