DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on February 10, 2025.
Claims 1-20 are pending in the application. As such, claims 1-20 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings were received on February 10, 2025. These drawings have been accepted and considered by the Examiner.
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 1-2, 9-12 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
A method implemented by one or more processors, the method comprising:
determining, at a computing device, that a user has provided a spoken utterance that is directed to an automated assistant that is accessible via the computing device, wherein the spoken utterance does not explicitly identify a name of any application that is different from the automated assistant;
processing, in response to determining that the user provided the spoken utterance, input data characterizing the spoken utterance to identify one or more operations that are associated with a request embodied in the spoken utterance, wherein the one or more operations are executable via a particular application that is separate from the automated assistant;
generating, based on processing the input data, one or more application inputs for the particular application,
wherein the one or more application inputs are generated using interaction data that provides a correlation between the one or more operations and the one or more application inputs; and
causing, based on generating the one or more application inputs, the automated assistant to provide the one or more application inputs to the particular application without interfacing with an application programming interface (API) of the particular application,
wherein providing the one or more application inputs to the particular application causes the particular application to perform the one or more operations and fulfill the request embodied in the spoken utterance.
The claim limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example,
“determining, at a computing device, that a user has provided a spoken utterance that is directed to an automated assistant that is accessible via the computing device, wherein the spoken utterance does not explicitly identify a name of any application that is different from the automated assistant” in the context of this claim encompasses a person listening to another person provide a command meeting these listed requirements,
“processing, in response to determining that the user provided the spoken utterance, input data characterizing the spoken utterance to identify one or more operations that are associated with a request embodied in the spoken utterance, wherein the one or more operations are executable via a particular application that is separate from the automated assistant” in the context of this claim encompasses a person deciding to use an application to respond to the command,
“generating, based on processing the input data, one or more application inputs for the particular application” in the context of this claim encompasses a person accessing an application,
“wherein the one or more application inputs are generated using interaction data that provides a correlation between the one or more operations and the one or more application inputs” in the context of this claim encompasses a person providing the correct data to the application,
“causing, based on generating the one or more application inputs, the automated assistant to provide the one or more application inputs to the particular application without interfacing with an application programming interface (API) of the particular application” in the context of this claim encompasses a person accessing the application without an API, such as by voice,
“providing the one or more application inputs to the particular application causes the particular application to perform the one or more operations and fulfill the request embodied in the spoken utterance” in the context of this claim encompasses a person feeding data to the application which yields a response.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
one or more processors
a computing device
an automated assistant.
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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim.
Claims 2 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein processing the input data to identify the one or more operations includes:
processing the input data, using a trained neural network model, to generate output that indicates a first location in an embedding space;
determining a distance measure between the first location in the embedding space and a second location, in the embedding space, that corresponds to the one or more operations; and
identifying the one or more operations based on the distance measure satisfying a distance threshold.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“processing the input data, using a trained neural network model, to generate output that indicates a first location in an embedding space” in the context of this claim encompasses a person processing the data and creating vectors,
“determining a distance measure between the first location in the embedding space and a second location, in the embedding space, that corresponds to the one or more operations” in the context of this claim encompasses a person identifying a distance between the vectors created,
“identifying the one or more operations based on the distance measure satisfying a distance threshold” in the context of this claim encompasses a person checking a threshold before proceeding.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a neural network model.
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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 9 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the one or more operations are capable of being initialized by the user via interaction with the particular application, and without the user initializing the automated assistant.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the one or more operations are capable of being initialized by the user via interaction with the particular application, and without the user initializing the automated assistant” in the context of this claim encompasses a person setting up an application and not setting up an assistant.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
automated assistant.
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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 10 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein processing the input data to identify the one or more operations that are associated with the request embodied in the spoken utterance includes:
determining that the particular application is causing an application GUI to be rendered in a foreground of a display interface of the computing device, and
determining that one or more selectable GUI elements of the application GUI are configured to initialize performance of the one or more operation in response to the user interacting with the one or more selectable GUI elements.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“determining that the particular application is causing an application GUI to be rendered in a foreground of a display interface of the computing device” in the context of this claim encompasses a person presenting the results on a piece of paper,
“determining that one or more selectable GUI elements of the application GUI are configured to initialize performance of the one or more operation in response to the user interacting with the one or more selectable GUI elements” in the context of this claim encompasses a person observing the other person point to the data presented on the paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a display interface
the computing device.
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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US Patent Pub. No. 20160349935 A1), hereinafter Gelfenbeyn, in view of Delgo et al. (US Patent Pub. No. 20180232443 A1), hereinafter Delgo.
Regarding claims 1 and 11, Gelfenbeyn teaches a method implemented by one or more processors and a system (Gelfenbeyn in [0007] teaches using a system which may include at least one processor and a memory),
the method comprising:
[claim 11 only] memory storing instructions (Gelfenbeyn in [0007] teaches using a system which may include at least one processor and a memory);
and
[claim 11 only] one or more processors operable to execute the instructions to (Gelfenbeyn in [0007] teaches using a system which may include at least one processor and a memory):
determining, at a computing device, that a user has provided a spoken utterance that is directed to an automated assistant that is accessible via the computing device, wherein the spoken utterance does not explicitly identify a name of any application that is different from the automated assistant (Gelfenbeyn in [0024] teaches using a “Dialog System” which refers to one or more of the following: a chat information system, a spoken dialog system, a conversational agent, a chatter robot, a chatterbot, a chatbot, a chat agent, a digital personal assistant, an automated online assistant, etc., and in [0028] teaches using a platform for maintaining multiple Dialog System Engines serving as backend services for Dialog System Interfaces, and in [0038] teaches the Dialog System receives a user request from the end user, the Dialog System may decide whether the user request is to be processed by the Dialog System itself or by any of the enabled plugins);
processing, in response to determining that the user provided the spoken utterance, input data characterizing the spoken utterance to identify one or more operations that are associated with a request embodied in the spoken utterance, wherein the one or more operations are executable via a particular application that is separate from the automated assistant (Gelfenbeyn in [0038] teaches the Dialog System receives a user request from the end user, the Dialog System may decide whether the user request is to be processed by the Dialog System itself or by any of the enabled plugins);
generating, based on processing the input data, one or more application inputs for the particular application (Gelfenbeyn in [0029] teaches acquiring user inputs and deliver dialog system outputs to the end users. Dialog System Engines, on the other hand, may support the Dialog System Interfaces by processing user inputs and generating corresponding responses to the user inputs. Thus, the Dialog System Engine, along with plugins for the Dialog System Engine, and the Dialog System Interface, when interacting with each other, form a Dialog System. One may refer to a Dialog System Interface running on or accessed from a client device as a “frontend” user interface, while a Dialog System Engine, which supports the operation of such Dialog System Interface, can be referred to as a “backend” service),
wherein the one or more application inputs are [generated using interaction data] that provides a correlation between the one or more operations and the one or more application inputs (Gelfenbeyn in [0086] teaches the Dialog System Engine may select the appropriate dialog system elements and process the user request using identified dialog system elements (such as one or more entities, one or more intents, one or more keywords, synonyms of the keywords, definitions of the keywords, reference values, dialog system trees, lists of terms, and the like) as retrieved);
and
causing, based on generating the one or more application inputs, the automated assistant to provide the one or more application inputs to the particular application without interfacing with an application programming interface (API) of the particular application (Gelfenbeyn in [0041] teaches the Dialog Systems can be implemented on a server such that their functionalities can be accessible for Dialog System Interfaces over the Internet, cellular networks, or any other communications means, and an online marketplace can also be implemented in “a cloud”),
wherein providing the one or more application inputs to the particular application causes the particular application to perform the one or more operations and fulfill the request embodied in the spoken utterance (Gelfenbeyn in [0054] teaches an answer received based on processing by the plugin may be provided to the end user).
Gelfenbeyn does not teach, however Delgo teaches
wherein the one or more application inputs are generated using interaction data [that provides a correlation between the one or more operations and the one or more application inputs] (Delgo in [Abstract] teaches an intelligent matching system network architecture with a software driven engine may establish one or more matches between properties attributed to an entity, for example, a service provider and a set of specified parameters, for example, the request properties of a customer, and the system analyzes user provided free-form text to generate an automated match of requirements specified by a user and a prospective service provider).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn further in view of Delgo to allow for generating an automated match of requirements specified by a user and a prospective service provider. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 2 and 12, Gelfenbeyn, as modified above, teaches the method and system of claims 1 and 11.
Gelfenbeyn further teaches
wherein processing the input data to identify the one or more operations includes:
[claim 12 only] one or more processors are to:
processing the input data, using a trained neural network model, [to generate output that indicates a first location in an embedding space] (Gelfenbeyn in [0092] teaches using statistical analysis, machine-learning algorithms (e.g., neural networks), heuristic analysis, and so forth).
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
processing the input data, [using a trained neural network model], to generate output that indicates a first location in an embedding space (Delgo in [0081] teaches a word embedding vector is obtained and associated with the token “based”, and measure the distance to the word vector associated with the named entity “Austin, Tex.” (by way of its type, “Country”) is measured. In this case, the distance is deemed to be under the set threshold, and the node corresponding to the token “based” is marked as potentially signifying the “offices Location” property of either the Client or the Service Provider)
determining a distance measure between the first location in the embedding space and a second location, in the embedding space, that corresponds to the one or more operations (Delgo in [0081] teaches a word embedding vector is obtained and associated with the token “based”, and measure the distance to the word vector associated with the named entity “Austin, Tex.” (by way of its type, “Country”) is measured. In this case, the distance is deemed to be under the set threshold, and the node corresponding to the token “based” is marked as potentially signifying the “offices Location” property of either the Client or the Service Provider);
and
identifying the one or more operations based on the distance measure satisfying a distance threshold (Delgo in [0081] teaches a word embedding vector is obtained and associated with the token “based”, and measure the distance to the word vector associated with the named entity “Austin, Tex.” (by way of its type, “Country”) is measured. In this case, the distance is deemed to be under the set threshold, and the node corresponding to the token “based” is marked as potentially signifying the “offices Location” property of either the Client or the Service Provider).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for measuring distances between vectors. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 3 and 13, Gelfenbeyn, as modified above, teaches the method and system of claims 2 and 12.
Gelfenbeyn, as modified above, teaches the neural network model.
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the [neural network model] is trained using instances of training data that are based on previous interactions between the user and the particular application (Delgo in [0047] teaches a classifier may be trained on labeled examples of word embedding vectors to arrive at a decision boundary which may be used for ascertaining whether a query word embedding vector matches well with the corresponding headword predicate).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for training a classifier. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 4 and 14, Gelfenbeyn, as modified above, teaches the method and system of claims 3 and 13.
Gelfenbeyn further teaches
a particular application graphical user interface (GUI) (Gelfenbeyn in [0045] teaches using a Dialog System Interface which may be as simple as a Graphical User Interface (GUI) enabling the dialog system end users to make inquiries, which may be then delivered to the backend service for processing by the corresponding Dialog System Engines, and to receive responses to the inquires generated by Dialog System Engines).
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the second location, in the embedding space, is generated based on processing, using an additional trained neural network model, one or more features of [a particular application graphical user interface (GUI)] of the particular application, wherein the one or more features correspond to the one or more operations (Delgo in [0056] teaches using a hierarchy of string matching algorithms, conditional on certain features of the input text may be used to supporting fuzzy matching, i.e. allowing for minor spelling variations (or mistakes), in the way certain surface forms associated with named entities may appear in user provided text, while still being able to match them, and in [0081] teaches other classification models may be used; Notably, a Recurrent Neural Network (RNN), or a variant of which called Long-Short Term Memory (LSTM) may be employed to obtain a (floating-point numerical) representation of the full sequence of tokens between the relation object, predicate, and subject).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for using an additional trained neural network model. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 5 and 15, Gelfenbeyn, as modified above, teaches the method and system of claims 4 and 14.
Gelfenbeyn further teaches
wherein the one or more features of the particular application GUI comprise a particular selectable element of the GUI, and wherein the one or more application inputs comprise an emulated selection of the particular selectable element (Gelfenbeyn in [0052] teaches using a marketplace interface which may enable the end users, through a number of GUI tools, to select one or more plugins and associate them with their custom Dialog System Engines).
Regarding claims 6 and 16, Gelfenbeyn, as modified above, teaches the method and system of claims 1 and 11.
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the interaction data includes a trained machine learning model that is trained based on prior interactions between one or more users and the particular application (Delgo in [0047] teaches a classifier may be trained on labeled examples of word embedding vectors to arrive at a decision boundary which may be used for ascertaining whether a query word embedding vector matches well with the corresponding headword predicate).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for training a classifier. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 7 and 17, Gelfenbeyn, as modified above, teaches the method and system of claims 6 and 16.
Gelfenbeyn further teaches
wherein [the trained machine learning model is trained] using at least one instance of training data that identifies a natural language input as training input and, as training output, an output operation capable of being performed by the particular application (Gelfenbeyn in [0005] teaches a dialog system interface may be responsible for receiving user inputs and delivering dialog system responses to the user, and the dialog system engine may be responsible for transforming voice user inputs into text inputs, interpreting text inputs, and generating corresponding responses to text inputs, and the process running on the dialog system engine is also known as “natural language processing” (NLP)).
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the trained machine learning model is trained using [at least one instance of training data that identifies a natural language input as training input and, as training output, an output operation capable of being performed by the particular application] (Delgo in [0047] teaches a classifier may be trained on labeled examples of word embedding vectors to arrive at a decision boundary which may be used for ascertaining whether a query word embedding vector matches well with the corresponding headword predicate).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for training a classifier. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 8 and 18, Gelfenbeyn, as modified above, teaches the method and system of claims 7 and 17.
Gelfenbeyn further teaches
[wherein the trained machine learning model is trained using] at least an instance of training data that includes, as training input, graphical user interface (GUI) data characterizing a particular application GUI and, as training output, an output operation capable of being initialized via user interaction with the particular application GUI (Gelfenbeyn in [0045] teaches using a Dialog System Interface which may be as simple as a Graphical User Interface (GUI) enabling the dialog system end users to make inquiries, which may be then delivered to the backend service for processing by the corresponding Dialog System Engines, and to receive responses to the inquires generated by Dialog System Engines).
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the trained machine learning model is trained using [at least an instance of training data that includes, as training input, graphical user interface (GUI) data characterizing a particular application GUI and, as training output, an output operation capable of being initialized via user interaction with the particular application GUI] (Delgo in [0047] teaches a classifier may be trained on labeled examples of word embedding vectors to arrive at a decision boundary which may be used for ascertaining whether a query word embedding vector matches well with the corresponding headword predicate).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for training a classifier. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 9 and 19, Gelfenbeyn, as modified above, teaches the method and system of claims 1 and 11.
Gelfenbeyn further teaches
[wherein the one or more operations are capable of being initialized by the user via interaction with the particular application, and] without the user initializing the automated assistant (Gelfenbeyn in [0041] teaches the Dialog Systems can be implemented on a server such that their functionalities can be accessible for Dialog System Interfaces over the Internet, cellular networks, or any other communications means, and an online marketplace can also be implemented in “a cloud”).
Gelfenbeyn, as modified above, does not teach, however Delgo teaches
wherein the one or more operations are capable of being initialized by the user via interaction with the particular application, and [without the user initializing the automated assistant] (Delgo in [0047] teaches a classifier may be trained on labeled examples of word embedding vectors to arrive at a decision boundary which may be used for ascertaining whether a query word embedding vector matches well with the corresponding headword predicate).
Delgo is considered to be analogous to the claimed invention because it is in the same field of matching available services to services determined to be needed. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gelfenbeyn, as modified above, further in view of Delgo to allow for training a classifier. Motivation to do so would allow for using corresponding word embedding vectors associated with words, which allow a continuous measure of similarity between a given query word (e.g. “based”) and a prototypical word associated with the relation ahead of time (Delgo [0110]).
Regarding claims 10 and 20, Gelfenbeyn, as modified above, teaches the method and system of claims 1 and 11.
Gelfenbeyn further teaches
wherein processing the input data to identify the one or more operations that are associated with the request embodied in the spoken utterance includes:
determining that the particular application is causing an application GUI to be rendered in a foreground of a display interface of the computing device (Gelfenbeyn in [0045] teaches using a Dialog System Interface which may be as simple as a Graphical User Interface (GUI) enabling the dialog system end users to make inquiries, which may be then delivered to the backend service for processing by the corresponding Dialog System Engines, and to receive responses to the inquires generated by Dialog System Engines),
and
determining that one or more selectable GUI elements of the application GUI are configured to initialize performance of the one or more operation in response to the user interacting with the one or more selectable GUI elements (Gelfenbeyn in [0045] teaches using a Dialog System Interface which may be as simple as a Graphical User Interface (GUI) enabling the dialog system end users to make inquiries, which may be then delivered to the backend service for processing by the corresponding Dialog System Engines, and to receive responses to the inquires generated by Dialog System Engines).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL J. MUELLER whose telephone number is (571)272-1875. The examiner can normally be reached M-F 9:00am-5:00pm (Eastern).
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PAUL MUELLER
Examiner
Art Unit 2657
/PAUL J. MUELLER/Examiner, Art Unit 2657
Prosecution Insights