DETAILED ACTION
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. PCT/KR2022/014593, filed on 9/28/2022.
Response to Amendment
This action is in response to applicant’s arguments and amendments filed 6/05/2026, which are in response to USPTO Office Action mailed 3/05/2026. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
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) 1 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza et al. (US Patent No.: 11,126,798; Date of Patent: Sep. 21, 2021) in view of GRUBER et al. (Australia Application Publication: AU 2019200296 A1; Pub. Date: Feb. 7, 2019)
Regarding independent claim 1,
Lewis Meza discloses an artificial intelligence device comprising: a display; See FIG. 1, (Disclosing an NLP system configured to extract meaning from a natural language message using improved parsing techniques. FIG. 1 illustrates a system 100 comprising conversational interfaces 120, 122, 124 configured to communicate with an Artificial Intelligence (AI) platform 104 via conversational gateway 102. Conversational interface 120 is labelled as a GUI, i.e. an artificial intelligence device comprising: a display;)
and a processor configured to control the display, wherein the processor is configured to receive a user input, See Col. 3, lines 36-46, (Conversational interfaces 120 may provide a GUI through which a user may type in a natural language message or provide a speech input, i.e. a processor configured to control the display, wherein the processor is configured to receive a user input (e.g. Note Col. 3, lines 64-65 wherein computer system 100 comprises one or more processors and memories).)
transmit the user input to a server, See Col. 6, lines 5-7, (Conversational gateway 102 may act as a traffic manager for receiving an input message 132 and routing the response from AI platform 104 to the appropriate channels. Note Col. 3, lines 61-63 wherein conversational interfaces may communicate with conversational gateway 102 over a network such as the Internet, i.e. transmit the user input to a server,
receive response information including intent analysis result information for the user input from the server, See FIG. 3A & Col. 6, lines 50-65, (FIG. 3A illustrates steps 302-308 of extracting the meaning from a received input message where step 308 comprises determining the intent of the message by translating a reduced message into control instructions for the Natural Language Generation (NLG) system, i.e. receive response information including intent analysis result information for the user input from the server (e.g. AI platform 104 receives control instructions for NLG system 108).)
and perform at least one operation among outputting information and executing a function according to the response information, See Col. 5, line 65 - Col. 6, line 3, (After extracting meaning from an input message 132 (e.g. such as via the method of FIG. 3A), NLP system 106 may provide control instructions to NLG system 108 in order to generate an appropriate response 16 to the input message, i.e. and perform at least one operation among outputting information (e.g. NLG system may generate a response to be delivered to a user in response to their input).)
wherein the intent analysis result information includes an intent result analysis for the user input that has been first processed based on at least one intent analysis factor transmitted to the server. See FIG. 3A & Col. 6, lines 50-65, (FIG. 3A illustrates steps 302-308 of extracting the meaning from a received input message where step 308 comprises determining the intent of the message by translating a reduced message into control instructions for the Natural Language Generation (NLG) system.) See Col. 11, lines 50-54, (The NLP system may determine the intent of a message based on rules that map aggregated named entities from reduced messages to intents, i.e. wherein the intent analysis result information includes an intent result analysis for the user input that has been first processed based on at least one intent analysis factor transmitted to the server (e.g. a user input is analyzed to determine control information for NLG system 108 by aggregating named entities according to rules used to generate an intent).)
Lewis Meza does not disclose the step wherein the at least one intent analysis factor comprises time information indicating a current time section associated with the user input,
wherein the processor is configured to cause the server to differently interpret an identical utterance according to the current time section,
wherein, based on the current time section corresponding to a day-transition period crossing midnight, the processor is configured to cause the server to reinterpret a temporal expression included in the user input according to user-relative time recognition instead of a device calendar date,
and wherein the response information includes different weather information corresponding to different semantic interpretations of the identical utterance according to the current time section,
and wherein the processor is configured to control the display to selectively output different user interfaces corresponding to the different semantic interpretations according to the current time section.
GRUBER discloses the step wherein the at least one intent analysis factor comprises time information indicating a current time section associated with the user input, See Paragraph [0519], (Disclosing an intelligent automated assistant system configured to engage with users via natural language dialog. The digital assistant may receive a speech input and determine context information associated with the speech input. Context information includes one or more of: a current location, a current time, and current or forecasted weather information, information extracted from one or more speech inputs previously received through the dialogue interface, information extracted from one or more non-verbal input previously received through the dialogue interface, i.e. wherein the at least one intent analysis factor comprises time information indicating a current time section associated with the user input.)
wherein the processor is configured to cause the server to differently interpret an identical utterance according to the current time section, See Paragraph [0548], (A user may provide a speech input having an associated vocabulary. The digital assistant may recognize the vocabulary and context information, including a current date, to determine which date the user is referring to in the speech input. For example, a sports-related input may use the term "tonight". Context information may be used to determine which date is being referred to by the word "tonight" as part of a disambiguation process. Note [0159] wherein the speech-to-text service 112 generates a set of candidate text interpretations 124 corresponding to a speech input, i.e. wherein the processor is configured to cause the server to differently interpret an identical utterance according to the current time section (e.g. candidate interpretations correspond to multiple versions of a single speech input).)
wherein, based on the current time section corresponding to a day-transition period crossing midnight, the processor is configured to cause the server to reinterpret a temporal expression included in the user input according to user-relative time recognition instead of a device calendar date, See Paragraph [0548], (A user may provide a speech input having an associated vocabulary. The digial assistant may recognize the vocabulary and context information, including a current date, to determine which date the user is referring to in the speech input. For example, a sports-related input may use the term "tonight". Context information may be used to determine which date is being referred to by the word "tonight" as part of a disambiguation process. Note [0017] wherein the assistant system may retrieve weather conditions and forecasts i.e. wherein, based on the current time section corresponding to a day-transition period crossing midnight (e.g. context information including a current time/date), the processor is configured to cause the server to reinterpret a temporal expression included in the user input according to user-relative time recognition instead of a device calendar date (e.g. a semantic interpretation of a speech input is determined based on context data including date/time information ).)
and wherein the response information includes different weather information corresponding to different semantic interpretations of the identical utterance according to the current time section, See Paragraph [0017], (The assistant system may retrieve weather information and forecasts.) See Paragraph [0336], (A user may provide a natural language input relating to local weather using context information. Note [0519] wherein context information includes one or more of: a current location, a current time, and current or forecasted weather information, information extracted from one or more speech inputs previously received through the dialogue interface, information extracted from one or more non-verbal input previously received through the dialogue interface.) See Paragraph [0517], (The digital assistant may process a speech input which includes supplementing and/or disambiguating the speech input wherein disambiguation includes determining that the speech input includes a term that has multiple reasonable interpretations and selecting one of the multiple reasonable interpretations based on the context information associated with the speech input, i.e. wherein the response information includes different weather information corresponding to different semantic interpretations of the identical utterance according to the current time section (e.g. via disambiguation which selects a most relevant interpretation from multiple interpretations of a speech input ).)
and wherein the processor is configured to control the display to selectively output different user interfaces corresponding to the different semantic interpretations according to the current time section. See Paragraph [0165], (Ranking component 126 may automatically select a highest-ranking speech interpretation from the plurality of speech interpretations associated with a speech input.) See Paragraph [0324], (The system may comprise a user interface for displaying results associated with user requests, i.e. wherein the processor is configured to control the display to selectively output different user interfaces corresponding to the different semantic interpretations according to the current time section (e.g. search results may be displayed on a user interface. Search results are determined based on the selected semantic interpretation of a speech input).)
Lewis Meza and GRUBER are analogous art because they are in the same field of endeavor, dialogue systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza to include the method of disambiguating text using context information as disclosed by GRUBER. Paragraph [0098] of GRUBER discloses that the system may use context information to generate more personalized results as well as improving efficiency for the user by automating steps.
Regarding independent claim 9,
The claim is analogous to the subject matter of independent claim 1 directed to a method or process and is rejected under similar rationale.
Claim(s) 2-3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER, as applied to claim 1 above, and further in view of Heere et al. (US PGPUB No. 2021/0089860; Pub. Date: Mar. 25, 2021).
Regarding dependent claim 2,
As discussed above with claim 1, Lewis Meza-GRUBER discloses all of the limitations.
Lewis Meza-GRUBER does not disclose the step the processor is configured to receive user feedback data according to the at least one operation performed, update the one or more intent analysis factors based on the feedback data, and transmit the updated intent analysis factors to the server.
Heere discloses the step the processor is configured to receive user feedback data according to the at least one operation performed, See Paragraph [0059], (Disclosing a digital assistant that provides proactive notifications to users. Digital assistant system 300 may receive explicit user feedback which is used to continuously update its internal knowledge representations, i.e. wherein the processor is configured to receive user feedback data according to the at least one operation performed)
update the one or more intent analysis factors based on the feedback data, See Paragraph [0060], (Digital assistant system 300 may receive explicit user feedback to update detected entities or user intents in conversations, i.e. update the one or more intent analysis factors based on the feedback data)
and transmit the updated intent analysis factors to the server. See FIG. 20 & Paragraph [0161], (FIG. 20 illustrates computer system 2000 for executing instructions 2024 for causing the system to perform the method. The system may be embodied as a networked deployment comprising server-client network environment.) See Paragraph [0074], (Conversation manager 320 may invoke a projection manager 340 when a detected intent requires execution or administration of a data processing job, i.e. and transmit the updated intent analysis factors to the server (e.g. intents are provided to conversation manager 320, which would include updated intents).)
Lewis Meza, GRUBER and Heere are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER to include the method of updating the digital assistant using user feedback as disclosed by Heere. Paragraph [0060] of Heere discloses that the system may facilitate a user’s ability to provide feedback such as via follow-up questions which allows the digital assistant system to improve its behavior by taking into account a current user content as gleaned from the user inputs.
Regarding dependent claim 3,
As discussed above with claim 2, Lewis Meza-GRUBER-Heere discloses all of the limitations.
Lewis Meza further discloses a memory configured to communicate with the processor and store data, and wherein the processor is configured to parse the response information and to read, from the memory, information to be output and information related to executing the function based on the parsed response information. See Col. 3, lines 64-65, (Computer system 100 comprises one or more processors and memories that implement the operations associated with the NLP system, i.e. a memory configured to communicate with the processor and store data.) See Col. 5, lines 60-61, (NLP system 106 may parse an input message 132 including natural language text in order to extract its meaning and formulate a message response 136, i.e. wherein the processor is configured to parse the response information and to read, from the memory, information to be output and information related to executing the function based on the parsed response information (e.g. message 132 is parsed to determine an intent which includes performing functions such as entity reduction and determining an intent in order to translate the message into control instructions for the NLG system).)
Regarding dependent claim 10,
The claim is analogous to the subject matter of dependent claim 2 directed to a method or process and is rejected under similar rationale.
Claim(s) 4 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER as applied to claim 4 above, and further in view of Madwed et al. (US PGPUB No. 2021/0375272; Pub. Date: Dec. 2, 2021).
Regarding dependent claim 4,
As discussed above with claim 3, Lewis Meza-GRUBER-Heere discloses all of the limitations.
Lewis Meza further discloses the step wherein when outputting information according to the intent analysis result information in the response information, the processor is configured to provide information configured as a first version or a second version based on the one or more intent analysis factors transmitted to the server. See FIGs. 10A-10B, (FIG. 10A illustrates a process wherein the NLP system may classify and learn in response to user messages. FIG. 10B illustrates an example conversation between a user and the system wherein the user may provide multiple responses as part of clarifying a user query, i.e. wherein when outputting information according to the intent analysis result information in the response information, the processor is configured to provide information configured as a first version (e.g. such as by responding to a a user message that is understood by the system) or a second version based on the one or more intent analysis factors transmitted to the server (e.g. the system may provide a response based on an updated ontology as in step 1010).)
Additionally, GRUBER further discloses the step wherein the first version and the second version correspond to different user interface configurations according to the current time section. See Paragraph [0163], (Candidate interpretations 124 correspond to potential outputs of the speech-to-text service that may be used to retrieve different information, i.e. wherein the first version and the second version correspond to different user interface configurations according to the current time section (e.g. context information is used to select a candidate text interpretation, which includes temporal information).)
Regarding dependent claim 11,
The claim is analogous to the subject matter of dependent claim 4 directed to a method or process and is rejected under similar rationale.
Claim(s) 5 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER and Heere as applied to claim 4 above, and further in view of Madwed et al. (US PGPUB No. 2021/0375272; Pub. Date: Dec. 2, 2021).
Regarding dependent claim 5,
As discussed above with claim 4, Lewis Meza-GRUBER-Heere discloses all of the limitations.
Lewis Meza-GRUBER-Heere does not disclose the step wherein when executing the function according to the intent analysis result information in the response information, the processor is configured to determine whether the function is capable of being executed, and when the determination result indicates that the function is not capable of being executed, provide recommended function information corresponding to the current time section and execute the recommended function instead,
Madwed discloses the step wherein when executing the function according to the intent analysis result information in the response information, the processor is configured to determine whether the function is capable of being executed, and when the determination result indicates that the function is not capable of being executed, provide recommended function information corresponding to the current time section and execute the recommended function instead, See Paragraph [0043], (Disclosing a system for responding to a user by detecting user frustration and presenting alternative action. Alternate input component 282 may determine an alternative representation of a user input based on a determination that a user input may result in error or undesired response based on past interactions with different users, i.e. wherein when executing the function according to the intent analysis result information in the response information, the processor is configured to determine whether the function is capable of being executed (e.g. an error would represent an inability to successfully execute a transaction or process), and when the determination result indicates that the function is not capable of being executed, provide recommended function information (e.g. the system presents the alternative action) and execute the recommended function instead (e.g. a system may present the alternative action and request confirmation that the system should proceed with the alternative action).) Note [0156] wherein the natural language understanding (NLU) component 260 comprises a reranker 490 may consider other data 491 including date, time, location, etc. for selecting candidate skills to execute a user input.)
Lewis Meza, GRUBER, Heere and Madwed are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER-Heere to include the method of providing alternative actions in response to a user input that may result in an error as disclosed by Madwed. Paragraph [0230] of Madwed discloses that the system may use a neural network to improve speech recognition using techniques such as back propagation which update weights of the neural network to reduce errors when processing training data.
Lewis Meza-GRUBER-Heere-Madwed does not disclose the step wherein the recommended function includes automatic control of a device setting function.
KUMAR disclose the step wherein the recommended function includes automatic control of a device setting function. See Paragraphs [0090]-[0092], (Disclosing a system for adaptively predicting non-default actions against unstructured utterances by an automated assistant operating in a computing system. The system comprises action review module 228 configured for updating control settings of a target device based on detected utterances, i.e. wherein the recommended function includes automatic control of a device setting function (e.g. by updating device settings).)
Lewis Meza, GRUBER, Heere, Madwed and KUMAR are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER-Heere-Madwed to include the method of modifying device settings as disclosed by KUMAR. Paragraph [0197] of KUMAR discloses that the client system may provide an enhanced user experience and hassle-free interaction by updating a global model including action-utterance related data.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 5 directed to a method or process and is rejected under similar rationale.
Claim(s) 6 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER as applied to claim 1 above, and further in view of Shang et al. (US PGPU No. 2022/0261833; Pub. Date: Aug. 18, 2022) and KUMAR et al. (US PGPUB No. 2022/0148580; Pub. Date: May 12, 2022).
Regarding dependent claim 6,
As discussed above with claim 1, Lewis Meza-GRUBER discloses all of the limitations.
Lewis Meza-GRUBER does not disclose the step wherein when an event occurrence is detected, the processor is configured to extract a previous user input, the intent analysis result information, and function execution operation information, and output information on at least one of information on the recommended reward function corresponding to the current time section, and execute the recommended reward function,
Shang discloses the step wherein when an event occurrence is detected, the processor is configured to extract a previous user input, the intent analysis result information, and function execution operation information, See Paragraphs [0003] & [0038], (Disclosing a system for determine a policy to prevent fading drivers having declining participation such as a decrease in driving frequency or length of time available for driving when compared to previous peaks in driving frequency or length of time. Policy engine 202 may optimize an incentive policy using a reinforcement learning system 204 through the generation of virtual trajectories of rewards, driver incentives and driver actions based on said incentives.
Lewis Meza, GRUBER and Shang are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER to include the method of providing users with incentives based on machine learning techniques as disclosed by Shang. Paragraph [0034] of Shang discloses that the use of incentives allows for the retention of drivers for a ridesharing service. One of ordinary skill in the art would recognize that incentives may be provided to users with incentives relating to other environments to increase the user satisfaction with a system and improving the likelihood that the user will continue to interact with the digital assistant system.
GRUBER further disclose the step wherein the system may output information on at least one of information on the recommended reward function corresponding to the current time section, See Paragraph [0548], (The process of parsing user speech may include using context information such as a current date to determine a date referenced in the user input as part of a disambiguation process. Once the speech input is fully disambiguated, the digital assistant performs a search to retrieve requested information, i.e. output information on at least one of information on the recommended reward function corresponding to the current time section (e.g. the system may perform disambiguation of a speech input based on context information including a current date in order to select an optimal interpretation of speech input to retrieve requested information).)
Lewis Meza-GRUBER-Shang does not disclose the step and execute the recommended reward function, wherein execution of the recommended reward function includes controlling an operation setting of the artificial intelligence device.
KUMAR discloses the step and execute the recommended reward function, wherein execution of the recommended reward function includes controlling an operation setting of the artificial intelligence device. See Paragraphs [0090]-[0092], (Disclosing a system for adaptively predicting non-default actions against unstructured utterances by an automated assistant operating in a computing system. The system comprises action review module 228 configured for updating control settings of a target device based on detected utterances. Action review module 228 may approve or reject recommended actions based on received feedback which may be positive or negative, i.e. wherein execution of the recommended reward function includes controlling an operation setting of the artificial intelligence device (e.g. the updates to device settings are selected based on a feedback metric, i.e. a reward function).)
Lewis Meza, GRUBER, Shang and KUMAR are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER-Shang to include the method of modifying device settings as disclosed by KUMAR. Paragraph [0197] of KUMAR discloses that the client system may provide an enhanced user experience and hassle-free interaction by updating a global model including action-utterance related data.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 6 directed to a method or process and is rejected under similar rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER as applied to claim 1 above, and further in view of KUMAR et al. (US PGPUB No. 2022/0148580; Pub. Date: May 12, 2022).
Regarding dependent claim 7,
As discussed above with claim 1, Lewis Meza discloses all of the limitations.
Lewis Meza further discloses the step wherein the processor is configured to configure and store routine information on function execution in a memory,
and, when the received intent analysis result information is related to at least one of the routines defined in the stored routine information, automatically execute the remaining routines included in the routine information. See Col. 3, lines 64-66, (Computer system 100 may comprise one or more processors and associated memories that execute the process of generating responses via an AI platform 104 as in FIG. 1.) See Col. 9, lines 64-67, (FIG. 6A illustrates a rule set used to perform a reduction function to determine the meaning of an input phrase, i.e. wherein the processor is configured to configure and store routine information on function execution in a memory (e.g. the process of reduction comprises executing typed functions that convert lists of named entities into an aggregated named entity. The method is executed by computer system 100 by the one or more processors).)
Additionally, GRUBER further discloses the step when the received intent analysis result information is related to at least one of the routines defined in the stored routine information according to the current time section associated with the user input, See Paragraph [0548], (The process of parsing user speech may include using context information such as a current date to determine a date referenced in the user input as part of a disambiguation process. Once the speech input is fully disambiguated, the digital assistant performs a search to retrieve requested information, i.e. when the received intent analysis result information is related to at least one of the routines defined in the stored routine information (e.g. the speech input is associated with a domain indicating a field of search, the field of search being associated with a vocabulary) according to the current time section associated with the user input (e.g. context information includes current date information).)
Lewis Meza-GRUBER does not disclose the step wherein automatically executing the remaining routines includes automatically controlling at least one device operation setting.
KUMAR discloses the step wherein automatically executing the remaining routines includes automatically controlling at least one device operation setting. See Paragraphs [0090]-[0092], (Disclosing a system for adaptively predicting non-default actions against unstructured utterances by an automated assistant operating in a computing system. The system comprises action review module 228 configured for updating control settings of a target device based on detected utterances, i.e. wherein the recommended function includes automatic control of a device setting function (e.g. by updating device settings).)
Lewis Meza and KUMAR are analogous art because they are in the same field of endeavor, digital assistant systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza to include the method of modifying device settings as disclosed by KUMAR. Paragraph [0197] of KUMAR discloses that the client system may provide an enhanced user experience and hassle-free interaction by updating a global model including action-utterance related data.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 7 directed to a method or process and is rejected under similar rationale.
Claim(s) 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis Meza in view of GRUBER as applied to claim 1 above, and further in view of Greystoke et al. (US PGPUB No. 2015/0310131; Pub. Date: Oct. 29, 2015).
Regarding dependent claim 8,
As discussed above with claim 1, Lewis Meza-GRUBER discloses all of the limitations.
Lewis Meza-GRUBER does not disclose the step wherein the processor is configured to provide an utterance agent including at least one recommended query, and wherein the at least one recommended query is written based on a recommended keyword configured based on at least one of intent analysis factors including the current time section associated with the user input,
Greystoke discloses the step wherein the processor is configured to provide an utterance agent including at least one recommended query, and wherein the at least one recommended query is written based on a recommended keyword configured based on at least one of intent analysis factors See Paragraph [0096], (Disclosing a collective intelligence experience system comprising a client interface that allows users to enter an initial search. The collective intelligence experience system may provide data including recommendations of better search terms, better searches or both to the client interface.) See FIG. 8 * Paragraph [0116], (FIG. 8 illustrates method 800 comprising step 804 wherein the system may determine a plurality of potential outcomes related to the request followed by step 806 of determining further potential outcomes based on collective information related to the request. The system may then present information including recommendations to a destination device via a GUI, i.e. wherein the processor is configured to provide an utterance agent including at least one recommended query, and wherein the at least one recommended query is written based on a recommended keyword configured based on at least one of intent analysis factors (e.g. recommended queries are displayed based on information relating to an initial request).)
The examiner notes that Greystoke does not disclose the step including the current time section associated with the user input,
However, GRUBER further discloses performing an intent analysis including the current time section associated with the user input, See Paragraph [0548], (The process of parsing user speech may include using context information such as a current date to determine a date referenced in the user input as part of a disambiguation process. Once the speech input is fully disambiguated, the digital assistant performs a search to retrieve requested information, i.e. performing an intent analysis including the current time section associated with the user input (e.g. context information includes current date information).)
Lewis Meza, GRUBER and Greystoke are analogous art because they are in the same field of endeavor, search assistance systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Lewis Meza-GRUBER to include the method of providing a user with recommended search queries as disclosed by Greystoke. Paragraph [0051] of Greystoke discloses that the system may provide a user with additional queries that may lead a user to beneficial outcomes that the user may not have considered to achieve a subjective "best" result based on collective information.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 4-9, 11-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments modify the scope of the claimed invention and therefore necessitated the new grounds of rejection presented in this Office Action.
Applicant’s remarks and amendments with respect to the rejection of claims 1-15 under 35 USC 101 have been fully considered and are persuasive for at least the following reasons:
Applicant’s amendments integrate the previously identified abstract idea of a mental process into a practical application by specifying that the process of performing an intent analysis is directed to retrieving weather information by reinterpreting and disambiguating a speech input. This represents an improvement in the field of artificial intelligence techniques for retrieving data by selecting an optimal interpretation of a speech input that accurately represents a user’s intent.
Therefore, the corresponding rejection is withdrawn.
Applicant’s remarks and amendments with regard to claim 15 remedy the minor typographical error objected to in the previous Non-Final Rejection. The corresponding objection has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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