Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 28-47 are pending.
This action is response to the application filed on June 24, 2024.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of priority under 35 U.S.C. 120 as a continuation of U.S. Patent Application Serial No. 17/302,876, filed May 14, 2021, the contents of such application being hereby incorporated by reference in its entirety and for all purposes as if completely and fully set forth herein.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 28-47 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by MAERGNER et al (US 20170287474 A1).
With respect to claims 28, 41 and 47, MAERGNER al teaches
receive a context-independent response generated according to a context-independent conversational flow for a human-resource operation (FIG. 6, [0033] NLU engine 205 dialog manager that generates output prompts and responds to semantic interpretations to manage a dialog process with the human user. the dialog manager may continuously be monitoring for any speech input from the user client 202. [0037] the P2P converter 214 context-independent);
determine an application context and a device context associated with a target application executing on a client device ([0016] FIG. 5 G2P conversion based on mapping foreign pronunciations to native pronunciations for recognizing multilingual name);
select, based on the application context and the device context, a service map from a plurality of service maps respectively associated with collaboration platforms and device types ([0029] Servers and applications may be combined on the same physical machines, and retain separate virtual or logical addresses, or may reside on separate physical machines. FIG. 1 specific network architecture and data processing devices.);
transform, using one or more parameters in the selected service map, the context-independent response into a context-specific response that corresponds to a conversational user interface of the target application as displayed on the client device (0037] The P2P converter 214 may employ different types of mapping to link phonemes from one language to phonemes from another language. For example, the P2P converter 214 may employ a context-independent mapping or a context-dependent mapping. The different types of mapping employed by the P2P converter 214 may be based on one or more algorithms and/or models, such as acoustic models, Hidden Markov models, and other language models (e.g., n-gram model). In some embodiments, the P2P converter 214 may employ decision trees or neural networks for the phoneme mapping); and
forward the context-specific response to the client device for display within the conversational user interface ([0057] FIG. 7 is an illustrative diagram of an example of a graphical user interface 700 after performing speech recognition in accordance with one or more features described herein. Specifically, user interface 700 illustrates results from a voice-based search using a speech-based personal assistant application. In an embodiment, user interface 700 may be displayed by a user client 202, such as on a mobile device. In this example, the user associated with user client 202 may make a statement in a native language (e.g., German) such as “Warm läuft How I Met Your Mother im Fernsehen”, which may be translated to “When is How I Met Your Mother on TV” in English. The user client 202 may transmit the statement as a speech input to the ASR engine (e.g., ASR engine 204)).
With respect to claims 29 and 42, MAERGNER et al teaches execute a local mapping service to intercept the context-independent response from the context-independent conversation flow (FIG. 6, [0033] NLU engine 205 dialog manager that generates output prompts and responds to semantic interpretations to manage a dialog process with the human user. [0037] the P2P converter 214 context-independent).
With respect to claims 30 and 43, MAERGNER et al teaches transform, using the one or more parameters specified in the selected service map, the context-independent response into the context-specific response without utilization of platform-specific flow objects (FIG. 6, [0033] NLU engine 205 dialog manager that generates output prompts and responds to semantic interpretations to manage a dialog process with the human user. [0037] the P2P converter 214 context-independent).
With respect to claim 31, MAERGNER et al teaches mapping service executes as a middleware layer configured to intercept the context-independent response prior to the display within the conversational user interface via the client device (FIG. 6, [0033] NLU engine 205 dialog manager that generates output prompts and responds to semantic interpretations to manage a dialog process with the human user. [0037] the P2P converter 214 context-independent).
With respect to claims 32 and 44, MAERGNER et al teaches select the service map based on a collaboration platform identifier and a device capability class associated with the client device ([0061] In FIG. 9, an example of an implementation of a computing environment 900. Client computing devices 902 and server computing devices 904 provide processing, storage, and input/output devices executing application programs).
With respect to claims 33 and 45, MAERGNER et al teaches transform the context-independent response into a plurality of context-specific responses that each correspond to a different conversational user interface for a respective collaboration platform ([0061] In FIG. 9, computing environment 900. Client computing devices 902 and server computing devices 904 provide processing, storage, and input/output devices executing application programs and the like. Client computing devices 902 may include, e.g., desktop computers, laptop computers, tablet computers, palmtop computers, smartphones, smart televisions, and the like).
With respect to claims 34 and 46, MAERGNER et al teaches use a first parameter of the one or more parameters to generate the context-specific response for the client device; and use a second parameter of the one or more parameters to generate an additional context- specific response for an additional user interface [0037] the P2P converter 214 context-independent).
With respect to claim 35, MAERGNER et al teaches use one or more rules in the service map to generate the context-specific response [0037] the P2P converter 214 context-independent).
With respect to claim 36, MAERGNER et al teaches formatting, data structure or interaction patterns for the context-specific response based on the target application and the client device [0037] the P2P converter 214 context-independent).
With respect to claim 37, MAERGNER et al teaches conditional logic that modifies the context-specific response based on at least one of user preferences or historical interaction data associated with the client device [0037] the P2P converter 214 context-independent).
With respect to claim 38, MAERGNER et al teaches use the validation rule to determine the context-specific response conforms to predefined constraints of the target application before forwarding the context-specific response to the client device ([0061] In FIG. 9, computing environment 900. Client computing devices 902 and server computing devices 904 provide processing, storage, and input/output devices executing application programs and the like. Client computing devices 902 may include, e.g., desktop computers, laptop computers, tablet computers, palmtop computers, smartphones, smart televisions).
With respect to claim 39, MAERGNER et al teaches rules are related to adaptation of graphical user interface elements of the target application that include at least one of layout, color scheme, or input controls ([0061] In FIG. 9, computing environment 900. Client computing devices 902 and server computing devices 904 provide processing, storage, and input/output devices executing application programs and the like).
With respect to claim 40, MAERGNER et al teaches apply a security protocol to the context-independent response prior to transformation, the security protocol comprising at least one of encryption or authentication of data of the context- specific response ([0061] In FIG. 9, computing environment 900. Client computing devices 902 and server computing devices 904 provide processing, storage, and input/output devices executing application programs and the like).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Perronnin (US 20030046068 A1) Eigenvoice Re-estimation Technique Of Acoustic Models For Speech Recognition, Speaker Identification And Speaker Verification.
Considered for teachings related generally to human-resources tasks through a chat-like interface without tying the conversation logic to a specific app or device. A user sends a request from an application on a phone, tablet, web browser, or other client device.
Does not disclose or suggest responsive to interprets objects to produce a response based on the rules. It then converts that response into a generic, context-independent message. After that, the system adapts the message to particular conversational UI. The adapted response is sent back for display to the user. In some examples, machine learning helps infer the intended HR operation from the user’s message. The system can also rank likely operations and let the user pick one.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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