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 .
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method for processing natural language requests, comprising steps of receiving a natural language request, obtaining parameters associated with the natural language request, wherein a first parameter of the obtained parameters is extracted or inferred from the natural language request, and generating a parametrized representation of the natural language request based on the obtained parameters. Each of these steps could be performed in the human mind and therefore comprise abstract ideas. For example, receiving a natural language request covers observations performed in the human mind by a human hearing or reading a natural language request. Obtaining parameters associated with the natural language request covers mental evaluations, judgements, or opinions performed in the human mind by a human mentally “extracting or inferring” parameters from the observed natural language request. A “parameter” is simply a description of the semantic content of the natural language sentence, such as determining the target entities in a natural language sentence (see paragraph [0028] of Applicant’s specification). Finally, generating a parameterized representation would cover a human writing, speaking, or otherwise expressing the parameters mentally determined by the human. Since each of these steps cover a mental process, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the only additional elements recited in the claim are the “computer-implemented” recitation in the preamble of the claim, and the digital assistance device that receives the natural language request. The recitation of “computer-implemented” in the preamble merely links the use of the judicial exception to a particular technological environment, and amounts to mere instructions to implement the abstract idea using a generic computer. The digital assistance device is used only for necessary data gathering, and recited at a high level of generality, and thus the step of receiving the natural language request is mere insignificant extra-solution activity. Since neither the “computer” recited in the preamble of the claim nor the “digital assistance device” implement the claimed steps of obtaining parameters from the natural language request and generating a parametrized representation of the natural language request, the claim as a whole does not integrate the abstract idea into a practical application and the claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted above, the “computer” recited in the preamble merely identifies a field of use and does not amount to significantly more. The step of receiving a natural language request by a digital assistance device is considered insignificant extra-solution activity. Additionally, as evidenced by the rejections below, receiving natural language requests by a digital assistance device represents well understood, routine, and conventional activity. Even when considered in combination, these elements do not amount to significantly more than the abstract idea itself.
Claim 1 is therefore directed to an abstract idea without significantly more.
Of the dependent claims, only claims 4 and 10 recite any additional elements beyond those recited in claim 1.
Claim 4 recites determining the first constituent element using an artificial intelligence. Reviewing the specification, the only descriptions related to artificial intelligence occur in paragraph [0060], which merely identifies “neural networks” as a type of artificial intelligence. The specification and claims do not include any specific information about how artificial intelligence is applied to determine a constituent element. The recitation in claim 4 is therefore no more than mere instructions to implement the abstract idea in a particular technological environment. Claim 4 therefore does not integrate the abstract idea into a practical application or provide significantly more.
Claim 10 recites the first parameter is obtained based on the location of the digital assistance device. It is noted that the location of the device is not necessarily determined by the digital assistance device as claimed, but merely “based on” its location. A human could mentally determine the location of a digital assistance device when determining the first parameter by observation. Claim 10 therefore also does not integrate the abstract idea into a practical application or provide significantly more.
Independent claims 13 and 20 are directed to computer-readable media and a generic system to perform the steps recited in claim 1. Claims 13 and 20 do not integrate the abstract idea into a practical application or provide significantly more for substantially the same reasons as claim 1.
The remaining dependent claims do not recite any further additional elements or include any steps that could not be performed mentally by a human, and therefore do not integrate the abstract idea into a practical application or provide significantly more for substantially the same reasons as claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-17 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roberts (U.S. Patent Application Pub. No. 2015/0293904).
In regard to claim 1, Roberts discloses a computer-implemented method for processing natural language requests (Fig. 3, 300 and Fig. 4, 400, paragraph [0043]), the method comprising:
receiving a natural language request captured at a digital assistant device (a natural language sentence is received from a user, step 302 and/or step 402, paragraphs [0044] and [0052]);
obtaining parameters associated with the natural language request, wherein a first parameter of the obtained parameters is extracted or inferred from the natural language request (a set of parameters are determined for the natural language sentence, steps 306-308 and/or 406, paragraphs [0044-0045] and [0052]); and
generating a parametrized representation of the natural language request based on the obtained parameters (the parameters are passed to an action model to determine arguments that correspond to the parameters, step 310 and/or step 408, paragraphs [0047] and [0053]),
wherein the obtained parameters comprise at least one of:
a type of the natural language request (the extracted parameters may be related to the type of input sentence, e.g. a ticket booking sentence or question-and-answer sentence, paragraph [0046]),
an identity of one or more responders that are to be interacted with to fulfill the natural language request (the extracted parameters include destination location or target recipient parameters, paragraph [0046]),
an identity of one or more target entities and one or more aspects of the one or more target entities to which the natural language request applies (the extracted parameters include a subject entity, e.g., “Barack Obama”, and an aspect related to that subject, e.g. “height”, paragraph [0046]),
an indication of how to handle the natural language request (if the system has never encountered the input natural language sentence, the system engages in a dialog with the user to elicit parameters from the sentence in order to properly respond to the sentence, paragraph [0050]).
In regard to claim 2, Roberts discloses determining a first constituent element of the natural language request (natural language processing determines sentence structure identifying subject, verb, object, etc., paragraphs [0044-0046]); and
mapping the first constituent element to the first parameter (the sentence structures are used to map to entries in a database to identify the parameters, paragraphs [0045-0046]).
In regard to claim 3, Roberts discloses the first constituent element is determined based on a series of requests from a particular user (context information from prior requests from the user are stored in a foreground knowledge graph to process a current request, paragraph [0049]).
In regard to claim 4, Roberts discloses the first constituent element is determined using an artificial intelligence (an intelligent agent-based interface, paragraph [0019]).
In regard to claim 5, Roberts discloses determining a format suitable for interacting with the one or more responders, wherein the parametrized representation is generated in the format suitable for interacting with the one or more responders (selected action modules receive the parameterized arguments from the natural language processing and format a request according to a web service’s API, paragraphs [0028-0030] and [0054-0055]).
In regard to claim 7, Roberts discloses the parameters associated with the natural language request further comprise at least one of an identity of a user who initiated the natural language request or an identity of an owner of the digital assistant device (user identity, paragraph [0032]).
In regard to claim 8, Roberts discloses the first parameter is obtained by parsing and extracting the first parameter from the natural language request (using a natural language parser, paragraph [0044]).
In regard to claim 9, Roberts discloses the first parameter is obtained based on a user configuration or a responder configuration (user context information, paragraph [0032]).
In regard to claim 10, Roberts discloses the first parameter is obtained by inference based on a location of the digital assistant device (using user location, paragraph [0032]).
In regard to claim 11, Roberts discloses the natural language request specifies information to be retrieved in association with a target entity (e.g., height information of target entity Barack Obama, paragraph [0046]).
In regard to claim 12, Roberts discloses the natural language request specifies an action to be performed in association with a target entity (e.g., booking a ticket, paragraph [0046]).
In regard to claim 13, Roberts discloses a non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to process natural language requests (paragraph [0060]) by performing the steps of:
receiving a natural language request captured at a digital assistant device (a natural language sentence is received from a user, step 302 and/or step 402, paragraphs [0044] and [0052]);
obtaining parameters associated with the natural language request, wherein a first parameter of the obtained parameters is extracted or inferred from the natural language request (a set of parameters are determined for the natural language sentence, steps 306-308 and/or 406, paragraphs [0044-0045] and [0052]); and
generating a parametrized representation of the natural language request based on the obtained parameters (the parameters are passed to an action model to determine arguments that correspond to the parameters, step 310 and/or step 408, paragraphs [0047] and [0053]),
wherein the obtained parameters comprise at least one of:
a type of the natural language request (the extracted parameters may be related to the type of input sentence, e.g. a ticket booking sentence or question-and-answer sentence, paragraph [0046]),
an identity of one or more responders that are to be interacted with to fulfill the natural language request (the extracted parameters include destination location or target recipient parameters, paragraph [0046]),
an identity of one or more target entities and one or more aspects of the one or more target entities to which the natural language request applies (the extracted parameters include a subject entity, e.g., “Barack Obama”, and an aspect related to that subject, e.g. “height”, paragraph [0046]),
an indication of how to handle the natural language request (if the system has never encountered the input natural language sentence, the system engages in a dialog with the user to elicit parameters from the sentence in order to properly respond to the sentence, paragraph [0050]).
In regard to claim 14, Roberts discloses determining a first constituent element of the natural language request (natural language processing determines sentence structure identifying subject, verb, object, etc., paragraphs [0044-0046]); and
mapping the first constituent element to the first parameter (the sentence structures are used to map to entries in a database to identify the parameters, paragraphs [0045-0046]).
In regard to claim 15, Roberts discloses the first constituent element is determined based on a series of requests from a particular user (context information from prior requests from the user are stored in a foreground knowledge graph to process a current request, paragraph [0049]).
In regard to claim 16, Roberts discloses the first constituent element is determined using an artificial intelligence (an intelligent agent-based interface, paragraph [0019]).
In regard to claim 17, Roberts discloses determining a format suitable for interacting with the one or more responders, wherein the parametrized representation is generated in the format suitable for interacting with the one or more responders (selected action modules receive the parameterized arguments from the natural language processing and format a request according to a web service’s API, paragraphs [0028-0030] and [0054-0055]).
In regard to claim 19, Roberts discloses the parameters associated with the natural language request further comprise at least one of an identity of a user who initiated the natural language request or an identity of an owner of the digital assistant device (user identity, paragraph [0032]).
In regard to claim 20, Roberts discloses a system for processing natural language requests (Fig. 5, 500), comprising:
a memory storing instructions (504); and
a processor executing the instructions to (502) perform the steps of:
receiving a natural language request captured at a digital assistant device (a natural language sentence is received from a user, step 302 and/or step 402, paragraphs [0044] and [0052]);
obtaining parameters associated with the natural language request, wherein a first parameter of the obtained parameters is extracted or inferred from the natural language request (a set of parameters are determined for the natural language sentence, steps 306-308 and/or 406, paragraphs [0044-0045] and [0052]); and
generating a parametrized representation of the natural language request based on the obtained parameters (the parameters are passed to an action model to determine arguments that correspond to the parameters, step 310 and/or step 408, paragraphs [0047] and [0053]),
wherein the obtained parameters comprise at least one of:
a type of the natural language request (the extracted parameters may be related to the type of input sentence, e.g. a ticket booking sentence or question-and-answer sentence, paragraph [0046]),
an identity of one or more responders that are to be interacted with to fulfill the natural language request (the extracted parameters include destination location or target recipient parameters, paragraph [0046]),
an identity of one or more target entities and one or more aspects of the one or more target entities to which the natural language request applies (the extracted parameters include a subject entity, e.g., “Barack Obama”, and an aspect related to that subject, e.g. “height”, paragraph [0046]),
an indication of how to handle the natural language request (if the system has never encountered the input natural language sentence, the system engages in a dialog with the user to elicit parameters from the sentence in order to properly respond to the sentence, paragraph [0050]).
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.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts, in view of Kannan et al. (U.S. Patent Application Pub. No. 2016/0155442, hereinafter “Kannan”).
In regard to claims 6 and 18, Roberts discloses the obtaining parameters associated with the natural language request comprises:
determining that the natural language request requires but does not specify a responder that is to be interacted with to fulfill the natural language request (each action requires selection of a particular module to implement the action, and an appropriate module is interacted with without the user explicitly specifying the module, paragraphs [0028-0030]).
Roberts does not expressly disclose determining a default responder that is to be interacted with to fulfill the natural language request.
Kannan discloses a method for determining a responder to be interacted with to fulfill a natural language request, comprising determining that the natural language request requires but does not specify a responder that is to be interacted with to fulfill the natural language request and determining a default responder that is to be interacted with to fulfill the natural language request (a user requests a task to be performed without specifying the provider to complete the task, and a default provider is selected to interact with the user to fulfill the requested task, paragraphs [0208-209]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a default responder to interact with to fulfill the natural language request when the natural language request did not specify a responder, because it would allow the user to specify their preferred responder for fulfilling a particular type of natural language request, as taught by Kannan (paragraphs [0070], [0169], and [0184]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Photos et al., Zhao, Nahum et al., Indukuri, Ceugniet et al., Hays et al., Mese et al., Schmidt et al., Baray et al., Imielinski et al., and Busch et al. disclose additional methods of parameterizing natural language.
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BLA 1/10/26
/BRIAN L ALBERTALLI/Primary Examiner, Art Unit 2656
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