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 .
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-5, 7-8 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over SHIN (US 20210249018 A1, hereinafter as “SHIN”).
Regarding claim 1, SHIN teaches:
A system (voice assistant service providing apparatus 100 in Fig. 17), comprising:
at least one memory (memory 140 in Fig. 17) storing instructions thereon; and
at least one processor (processor 120 in FIG. 17) configured to execute the instructions to:
determine operation objectives for a target system (FIG. 17 and [0074]: determine user’s intent for a target system comprising first, second and third devices);
determine one or more devices of the target system to be controlled based on the operation objectives ([0074]);
generate, using a machine-learning model trained on data received from the target system ([0091]: AI model is trained based on service provision history and device information received from the target devices), commands for the one or more devices based on the operation objectives ([0091]: generate service provision message/commands for the devices); and
communicate the commands to the target system to control the one or more devices based on the operation objectives (step S291 in FIG. 2 and [0098]: the operation/commands approved by the user are communicated to the devices so one or more devices implement the approved commands to accomplish user’s intent).
SHIN teaches specifically (underlines are added by Examiner for emphasis):
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[0074] The voice assistant service providing apparatus 100 may determine a user's intent through NLU on the first voice command. The voice assistant service providing apparatus 100 may determine the first device 200-1, the second device 200-2, and the third device 200-3 from among a plurality of candidate devices, based on a relevance between the user's intent and each of the candidate devices.
[0091] The voice assistant service providing apparatus 100 may select at least one of a plurality of service provision messages by using a service recommendation model that is an artificial intelligence (AI) model. The service recommendation model may be an AI algorithm trained based on the first voice command, service provision histories of a plurality of devices with respect to the first voice command, and pieces of device information regarding the devices.
[0098] The voice assistant service providing apparatus 100 may transmit information related to the selected service to both the first and third devices 200-1 and 200-3 determined as the first group (operation S291). The first and third devices 200-1 and 200-3 may each learn the information related to the selected service (operations S293 and S295). The first and third devices 200-1 and 200-3 may update, based on the information related to the service selected by the user, ‘the number of times that the service was selected by the user’ or ‘the number of times that the service was not selected by the user’. Devices may suggest an optimal service preferred by the user by learning a service selected by the user.
Regarding claim 2, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
the machine-learning model is trained to process natural language input, determine the operation objectives from the natural language input, and determine the one or more devices of the target system to be controlled based on the operation objectives([0091]: “The voice assistant service providing apparatus 100 may select at least one of a plurality of service provision messages by using a service recommendation model that is an artificial intelligence (AI) model. The service recommendation model may be an AI algorithm trained based on the first voice command, service provision histories of a plurality of devices with respect to the first voice command, and pieces of device information regarding the devices”).
Regarding claim 3, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
determine one or more external devices outside of the target system based on historical operation data of the target system describing interactions between the one or more external devices and the one or more devices of the target system (FIG. 3 and [0100]: “FIG. 3 illustrates an example in which a fourth device 200-4 is added to the system of FIG. 2”; And [0106]: “because the fourth device 200-4 is newly added, the fourth device 200-4 has not learned group information related to the first voice command. Thus, the service provision message transmitted by the fourth device 200-4 in operation S344 may include information indicating that the fourth device 200-4 is not included in the first group related to the first voice command”; And [0111]: “the voice assistant service providing apparatus 100 may regroup the devices by analyzing service provision messages, service provision histories, and pieces of device information received from the devices (operation S360)”; And [0112]: “Referring to FIG. 3, the voice assistant service providing apparatus 100 may update group information such that the fourth device 200-4 is further added to the first group including the first and third devices 200-1 and 200-3”. All these teach an external device 200-4 is added to the target system comprising 200-1, 200-2 and 200-3. The voice assistant service providing apparatus 100 determines new device 200-4 is added based on historical operation data); and
generate, using the machine-learning model, the commands for the one or more devices further based on the interactions described by the historical operation data ([0091]: “The voice assistant service providing apparatus 100 may select at least one of a plurality of service provision messages by using a service recommendation model that is an artificial intelligence (AI) model. The service recommendation model may be an AI algorithm trained based on the first voice command, service provision histories of a plurality of devices with respect to the first voice command, and pieces of device information regarding the devices”. Newly added device 200-4 interacts with devices 200-1, 200-2 and 200-3 as a whole group to fulfil the user’s intent).
Regarding claim 4, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
the at least one processor is configured to determine constraints of the one or more devices from the data and generate the commands further based on the constraints ([0064]: “According to another embodiment of the disclosure, the voice assistant service providing apparatus 100 may receive, directly from each of a plurality of candidate devices, device information including at least one of ID information (e.g., device ID information), a device type, a functional capability, location information, or status information of each of the candidate devices”; and [0091]: “The voice assistant service providing apparatus 100 may select at least one of a plurality of service provision messages by using a service recommendation model that is an artificial intelligence (AI) model. The service recommendation model may be an AI algorithm trained based on the first voice command, service provision histories of a plurality of devices with respect to the first voice command, and pieces of device information regarding the devices”).
Regarding claim 5, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
the data includes sensor data of the one or more devices the data includes sensor data of the one or more devices (903 in FIG. 9 and [0226]: “the air conditioner may provide information related to indoor temperature, humidity, and dust”).
Regarding claim 7, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
the operation objectives specify at least one target condition for the target system (FIG. 14: the target condition is “play song”), and the commands include instructions executable by the one or more devices to cause the one or more devices to adjust a condition of the target system to satisfy the at least one target condition (FIG. 14: after the user confirmation, commands are sent to devices to play song).
Regarding claim 8, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
each device of the one or more devices includes a respective application ([0352]: “Each device may identify services providable by the corresponding device based on at least one of information about sub-devices (e.g., a screen, an input interface, etc.) constituting the device, information about software (e.g., an application, etc.) running on the device”), and the at least one processor is configured to communicate the commands to the target system to control the one or more devices based on the operation objectives by communicating the commands to each respective application (step S291 in FIG. 2 and [0098]: the operation/commands approved by the user are communicated to the respective application of the devices so one or more devices implement the approved commands to accomplish user’s intent).
Regarding claim 10, SHIN teach(es) all the limitations of the base claim it depends on.
SHIN further teaches:
the data describes one or more applications associated with the one or more devices, the one or more applications configured to execute instructions to cause the one or more devices to perform operations specified by the instructions ([0082]: “the service provision message may include at least one of information about whether the device is able to provide a service in response to a voice command, information about whether the device is included in a group related to the voice command, ID information of the device, ID information of a service provided by the device in response to the voice command, a type of the service, ID information of an application used to provide the service, or a resulting text related to a service suggested by the device”).
Claim 15 recites a non-transitory computer-readable storage medium including instructions to cause one or more processors to implement operational steps as the system of claim 1 with patentably the same limitations. Therefore, claim 15 is rejected for the same reason as the recited in the rejection of claim 1.
Claim 16 recites a non-transitory computer-readable storage medium including instructions to cause one or more processors to implement operational steps as the system of claims 4 and 10 with patentably the same limitations. Therefore, claim 16 is rejected for the same reasons as the recited in the rejection of claims 4 and 10.
Claim 17 recites a non-transitory computer-readable storage medium including instructions to cause one or more processors to implement operational steps as the system of claim 2 with patentably the same limitations. Therefore, claim 17 is rejected for the same reason as the recited in the rejection of claim 2.
Claim 18 recites a method comprising operational steps conducted by the system of claim 1 with patentably the same limitations. Therefore, claim 18 is rejected for the same reason as the recited in the rejection of claim 1.
Claims 19 and 20 recite a method comprising operational steps conducted by the system of claims 7 and 2 respectively with patentably the same limitations. Therefore, claims 19 and 20 are rejected for the same reason as the recited in the rejection of claims 7 and 2, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over SHIN in view of Kuffner (US 20160023351 A1, hereinafter as “Kuffner”).
Regarding claim 6, SHIN teach(es) all the limitations of the base claim it depends on, but does not teach to receive operational data from the target system following communication of the commands to the target system; determine whether the operation objectives are satisfied based on the operational data; and responsive to the operation objectives not being satisfied: generate, using the machine-learning model, updated commands based on the operational data; and communicate the updated commands to the target system.
However, Kuffner teach in analogous art:
receive operational data ([Claim 14]: “receiving the report from the first robotic device”);
determine whether the operation objectives are satisfied based on the operational data ([Claim 17]: “determining that the report indicates unsuccessful performance of the task”); and
responsive to the operation objectives not being satisfied ([Claim 17]: “in response to determining that the report indicates unsuccessful performance of the task, generating second updated instructions to carry out the task based on the report”):
generate updated commands based on the operational data ([Claim 17]: “in response to determining that the report indicates unsuccessful performance of the task, generating second updated instructions to carry out the task based on the report”); and
communicate the updated commands (see [Claim 17] above: updated instructions are generated and communicated to the robot device to carry out the task).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SHIN based on the teaching of Kuffner, to make the system wherein the at least one processor is configured to receive operational data from the target system following communication of the commands to the target system; determine whether the operation objectives are satisfied based on the operational data; and responsive to the operation objectives not being satisfied: generate, using the machine-learning model, updated commands based on the operational data; and communicate the updated commands to the target system. One of ordinary skill in the art would have been motivated to do this modification in order to help successfully “carry out the task”, as Kuffner suggests in [Claim 17].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over SHIN in view of Barone (US 20030066081 A1, hereinafter as “Barone”).
Regarding claim 9, SHIN teach(es) all the limitations of the base claim it depends on, but does not teach each application is encoded with a respective application programming interface (API), and the machine-learning model determines the commands based on each API.
However, Barone teach in analogous art:
each application is encoded with a respective application programming interface (API), and determines commands based on each API ([0020]: “The command generator 104 receives the playlist of events from the ITV device 100 over a serial or a private or public network link 102, and generates commands according to the API command protocol ….”. This teach each API has its own encoding format and commands are generated based on the API).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SHIN based on the teaching of Barone, to make the system wherein each application is encoded with a respective application programming interface (API), and the machine-learning model determines the commands based on each API. One of ordinary skill in the art would have been motivated to do this modification in order to achieve “seamless communication”, as Barone suggests in [Abstract].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over SHIN in view of Reiss (US 20240394433 A1, hereinafter as “Reiss”).
Regarding claim 11, SHIN teach(es) all the limitations of the base claim it depends on, but does not teach the at least one processor is configured to generate one or more simulated applications, with each simulated application corresponding to a respective application of the one or more applications associated with the one or more devices.
However, Reiss teach in analogous art:
generate one or more simulated applications, with each simulated application corresponding to a respective application ([Abstract]: “Techniques for generating a simulated application programming interface (API) may include generating simulations of APIs that provide stateful interactions. The simulations may be generated based on API specifications and/or other API parameters (e.g. from a user)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified SHIN based on the teaching of Reiss, to make the system wherein the at least one processor is configured to generate one or more simulated applications, with each simulated application corresponding to a respective application of the one or more applications associated with the one or more devices. One of ordinary skill in the art would have been motivated to do this modification in order to “allow a real-world evaluation of the simulated API”, as Reiss suggests in [Abstract].
Allowable Subject Matter
Claims 12-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure.
Reier (US 20180092189 A1): teaches a method to choose and control lighting devices and other devices based on user voice input.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES CAI whose telephone number is (571)272-7192. The examiner can normally be reached on M-F 8-5 EST.
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, Kamini Shah can be reached on 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES CAI/Primary Patent Examiner, Art Unit 2115
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