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 .
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.
DETAILED ACTION
Claims status
Claims 1-12 are pending as the applicant filed Preliminary Amendment on 06/20/2024.
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-12 are provisionally rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-7 of a related Application 17/491,037 now US Patent 12,100,952. Although the conflicting claims are not identical, they are not patentably distinct from each other because the limitations of the claims in the current application are encompassed in the previous application. The latter pending application encompasses the same process as the pending application and is a different version of the previous application because of rearrangement of the claim language.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1-12, the term “current” is vague and has multiple meaning in view of the claim invention that renders the claim indefinite. The term “current” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “current” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “current” occurs.
Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood.
Claim Rejections - 35 USC § 102
4. 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.
(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) 1-12 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Hieda et al. US Patent Application Publication 2015/0253364 A1, Date Published: 2015-09-10.
Regarding claim 1:
Hieda described a system for monitoring usage of an electrical appliance, comprising: an electrical socket device (abstract, connected electric equipment) including
a socket module adapted to be electrically connected to the electrical appliance, and operable to switch between a first state for permitting supply of electric power to the electrical appliance and a second state for terminating supply of electric power to the electrical appliance (abstartc, connected electric equipment based on the voltage waveform and/or the current waveform received from the smart tap, 0036, smart tap, and comparing any one or more of the newly measured and received voltage waveform, current waveform, characteristic amount of the voltage waveform, characteristic amount of the current waveform, and, optionally, a waveform of a power amount and a characteristic amount of the waveform of the power amount with the stored data to judge an operation state of each connected piece of electric equipment and transmit the operation state of the electric equipment),
a storage module storing current feature data that is related to multiple reference values of an operating current of the electrical appliance, the reference values corresponding respectively to different operating modes of the electrical appliance (0036, stored data to judge an operation state of each connected piece of electric equipment and transmit the operation state of the electric equipment, 0041, electric household appliance with the power waveforms and the characteristic amounts upon the previous usage accumulated and stored in a database),
a current detecting module electrically connected to said socket module, and configured to continuously detect a current flowing through the electrical appliance so as to generate a current detection result (0041, comparing the normal power waveform pattern and the characteristic amount of the electric household appliance with the power waveforms and the characteristic amounts upon the previous usage accumulated and stored in a database, if the waveform does not match the previous waveforms, it is possible to detect abnormality of wiring or an outlet or failure or trouble of the electric household appliance a),
an audio receiving module for receiving sound at a site where the electrical appliance is disposed and generating an audio result based on the sound thus received (0142, configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance), and
a processing module electrically connected to said socket module, said storage module, said current detecting module and said audio receiving module, and configured to generate audio feature data based on the audio result, and to generate event data that is related to the usage of the electrical appliance based on the current feature data, the current detection result and the audio feature data (0142, configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.); and
a management server including
a database storing user data that is related to a user at the site, appliance data that is related to the electrical appliance, and service record data that is related to historical usage of the electrical appliance (0078, makes it possible to identify the electric household appliance connected to the outlet and the tap and detect an operation state of each electric household appliance and occurrence of abnormality such as degradation, failure and electricity leakage by compiling a database of patterns of these voltage and current waveforms in a server),
a storage unit storing a behavioral feature recognition model that is configured to recognize multiple behavioral features related to behaviors of the user using the electrical appliance, wherein the behavioral features and the behavioral feature recognition model are derived and established by using a first machine learning algorithm to analyze the user data and the service record data based on multiple feature parameters that are each related to the user, the electrical appliance, and an environment where the electrical appliance is disposed (0079, makes it possible to identify the electric household appliance connected to the outlet and the tap and detect an operation state of each electric household appliance and occurrence of abnormality such as degradation, failure and electricity leakage by compiling a database of patterns of these voltage and current waveforms in a server,), and
a processing unit communicatively connected to said processing module, and electrically connected to said database and said storage unit,
wherein said processing module transmits the event data to said processing unit, and said processing unit uses the behavioral feature recognition model to determine whether the event data received from said processing module matches one of the behavioral features, and sends a warning message to a user end device that is related to the user upon determining that the event data does not match any of the behavioral features (0078, makes it possible to identify the electric household appliance connected to the outlet and the tap and detect an operation state of each electric household appliance and occurrence of abnormality such as degradation, failure and electricity leakage by compiling a database of patterns of these voltage and current waveforms in a server, 0142, configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance).
Regarding claim 5:
Hieda described a method for monitoring usage of an electrical appliance, said method being implemented by a system that includes an electrical socket device and a management server (abstract, a server is provided. The smart tap includes voltage waveform measuring means and/or current waveform measuring means and communication means. The server has a function of judging an operation state of each piece of connected electric equipment based on the voltage waveform and/or the current waveform received from the smart tap and, when detecting abnormality of the electric equipment, transmitting the detection to an external network.),
the electrical socket device storing current feature data that is related to multiple reference values of an operating current of the electrical appliance, the reference values corresponding respectively to different operating modes of the electrical appliance (abstartc, connected electric equipment based on the voltage waveform and/or the current waveform received from the smart tap, 0036, smart tap, and comparing any one or more of the newly measured and received voltage waveform, current waveform, characteristic amount of the voltage waveform, characteristic amount of the current waveform, and, optionally, a waveform of a power amount and a characteristic amount of the waveform of the power amount with the stored data to judge an operation state of each connected piece of electric equipment and transmit the operation state of the electric equipment),
the management server storing user data that is related to a user at a site where the electrical appliance is disposed, appliance data that is related to the electrical appliance, service record data that is related to historical usage of the electrical appliance, and a behavioral feature recognition model that is configured to recognize multiple behavioral features related to behaviors of the user using the electrical appliance, said method comprising: by the electrical socket device, continuously detecting a current flowing through the electrical appliance to generate a current detection result (0188, a power assignment message including the allowed power amount and the allowed time period is transmitted to each electric household appliance or a refusal message is transmitted to equipment to which power cannot be supplied, through bidirectional packet communication. The electric household appliance which is allowed to use power starts or continues operation, or performs operation while reducing or increasing power consumption.), receiving sound at the site, and generating an audio result based on the sound thus received; by the electrical socket device, generating audio feature data based on the audio result, generating event data related to the usage of the electrical appliance based on the current feature data, the current detection result and the audio feature data, and transmitting the event data to the management server (0142, comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance); by the management server, using the behavioral feature recognition model to determine whether the event data received from the electrical socket device matches one of the behavioral features (0278, result is transmitted to the server, and the server extracts the past voltage waveform and the past current waveform of the electric household appliance stored in a voltage and current waveform DB, and in S2, compares the measured voltage waveform, current waveform with the extracted voltage waveform and current waveform and acquires points where the waveforms match and points where the waveforms do not match.); and by the management server, sending a warning message to a user end device that is related to the user in response to determining that the event data does not match any of the behavioral features (0142, comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance, 0278, measurement result is transmitted to the server).
Regarding claim 10:
Hieda described an electrical socket device to be used in a system for monitoring usage of an electrical appliance, the system including a management server, said electrical socket device comprising (0036, smart tap, and comparing any one or more of the newly measured and received voltage waveform, current waveform, characteristic amount of the voltage waveform, characteristic amount of the current waveform, and, optionally, a waveform of a power amount and a characteristic amount of the waveform of the power amount with the stored data to judge an operation state of each connected piece of electric equipment and transmit the operation state of the electric equipment): a socket module adapted to be electrically connected to the electrical appliance, and operable to switch between a first state for permitting supply of electric power to the electrical appliance (abstract, a server is provided. The smart tap includes voltage waveform measuring means and/or current waveform measuring means and communication means. The server has a function of judging an operation state of each piece of connected electric equipment based on the voltage waveform and/or the current waveform received from the smart tap and, when detecting abnormality of the electric equipment, transmitting the detection to an external network.) and a second state for terminating supply of electric power to the electrical appliance; a storage module storing current feature data that is related to multiple reference values of an operating current of the electrical appliance, the reference values corresponding respectively to different operating modes of the electrical appliance; a current detecting module electrically connected to said socket module (0193, the information such as the priorities of the electric household appliance which is operating is sequentially updated, and, when the electric equipment is newly powered on, the priorities of the electric household appliance is updated to include this new electric equipment, and it is judged whether or not power is supplied to the electric household appliance which is newly powered on based on the power amount which can be supplied and the priorities of the electric household equipment.), and configured to continuously detect a current flowing through the electrical appliance so as to generate a current detection result (0188, the electric household appliance which is allowed to use power starts or continues operation, or performs operation while reducing or increasing power consumption.); an audio receiving module for receiving sound at a site where the electrical appliance is disposed and generating an audio result based on the sound thus received (0142, computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance); and a processing module electrically connected to said socket module, said storage module, said current detecting module and said audio receiving module, and configured to generate audio feature data based on the audio result, generate event data that is related to the usage of the electrical appliance based on the current feature data, the current detection result and the audio feature data, and transmit the event data to the management server (0142, computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance, 0162, data with the past data of the electric household appliance at the server using any one or more of the voltage waveform, the current waveform, the characteristic amount of the voltage waveform, the characteristic amount of the current waveform).
Regarding claim 2, Hieda further described stores the event data in said database as a part of the service record data upon receipt of the event data, determines, based on the event data and the appliance data, whether operating current information that is related to the operating current of the electrical appliance needs to be adjusted, generates, upon determining that the operating current information needs to be adjusted, current feature adjustment data that indicates adjustment of the operating current information, and transmits the current feature adjustment data to said storage module of said electrical socket device in order to update the current feature data stored in said storage module (0078, the voltage and the current waveforms are often different from each other, which makes it possible to identify the electric household appliance connected to the outlet and the tap and detect an operation state of each electric household appliance and occurrence of abnormality such as degradation, failure and electricity leakage by compiling a database of patterns of these voltage and current waveforms in a server, or the like.).
Regarding claim 3, Hieda further described further stores an audio feature recognition model that is configured to recognize multiple audio features; wherein the audio features and the audio feature recognition model are derived and established by using a second machine learning algorithm to analyze the audio result based on multiple audio parameters that are each related to the environment where the electrical socket device is disposed; and wherein said processing module uses the audio feature recognition model to determine which of the audio features that the audio result generated by said audio receiving module matches, and generates the audio feature data based on the audio result that matches any of the audio features (0142, detecting that the voltage waveform and/or the current waveform, or the like, or the characteristic amounts of the voltage and/or current waveforms computed at the arithmetic device indicate some abnormality by comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.).
Regarding claim 4, Hieda further described an output module that is electrically connected to said processing module; and wherein said processing module of said electrical socket device determines whether the electrical appliance is operating in one of a normal condition and an abnormal condition based on the current feature data, the current detection result and the audio feature data, and upon determining that the electrical appliance is operating in the abnormal condition, makes said output module to execute one of a first response of generating a warning output that indicates the abnormal condition, and a second response of controlling said socket module to terminate supply of electric power to the electrical appliance (0142, means for detecting that the voltage waveform and/or the current waveform, or the like, or the characteristic amounts of the voltage and/or current waveforms computed at the arithmetic device indicate some abnormality by comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.).
Regarding claim 6, Hieda further described by the management server, storing the event data as a part of the service record data upon receipt of the event data from the electrical socket device; by the management server, determining, based on the event data and the appliance data, whether operating current information that is related to the operating current of the electrical appliance needs to be adjusted; by the management server, in response to determining that the operating current information needs to be adjusted, generating current feature adjustment data that indicates adjustment of the operating current information, and transmitting the current feature adjustment data to the electrical socket device; and by the electrical socket device, upon receipt of the current feature adjustment data from the management server, using the current feature adjustment data to update the current feature data (0027, measuring power flow itself of power consumption and distribution of one or a plurality of power sources and its power network, and electric household appliance on the power network, a power supply pattern such as a voltage waveform and a current waveform of the connected electric household appliance is compared with an original power waveform of each electric household appliance which is measured in advance and stored in a server, so that it is detected whether the electric household appliance used in the house normally operates or degrades, or some abnormality occurs, 0134, adjust power of each electric household appliance so that the power value is equal to or lower than an upper limit power value requested from the server and a peak cut power value by using the semiconductor device. Further, by providing an infrared remote control function, the smart tap can switch ON and OFF or reduce power.).
Regarding claim 7, Hieda further described storing an audio feature recognition model that is configured to recognize multiple audio features, the method further comprising: by the electrical socket device, storing an audio feature recognition model that is configured to recognize multiple audio features (0194, the power usage model is created within the server and the power usage plan is created by learning the behavior pattern of the user), using a second machine learning algorithm to analyze the audio detection result based on multiple audio parameters that are each related to the environment where the electrical appliance is disposed, so as to derive the audio features and establish the audio feature recognition model, by the electrical socket device (0036, smart tap, and comparing any one or more of the newly measured and received voltage waveform, current waveform, characteristic amount of the voltage waveform, characteristic amount of the current waveform, and, optionally, a waveform of a power amount and a characteristic amount of the waveform of the power amount with the stored data to judge an operation state of each connected piece of electric equipment and transmit the operation state of the electric equipment.), using the audio feature recognition model to determine which among the audio features that the audio detection result matches with, and generating the audio feature data based on the audio detection result that matches with any of the audio features (0142, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.).
Regarding claim 8, Hieda further described by the electrical socket device, determining whether the electrical appliance operates in one of a normal condition and an abnormal condition based on the current feature data, the current detection result and the audio feature data; and by the electrical socket device, in response to determining that the electrical appliance operates in the abnormal condition, executing one of a first response of generating a warning output that indicates the abnormal condition, and a second response of terminating supply of electric power to the electrical appliance (0024, it is required to connect the electric household appliance, measure detailed voltage and current waveforms, or the like, and measure, collect, calculate, control and communicate a power amount for each outlet, that is, for each connected electric household appliance, and, further, combine these to estimate and control power flow for each house or for each group of a plurality of houses., 0142, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.).
Regarding claim 9, Hieda further described by the management server, using a machine learning algorithm to analyze the user data and the service record data based on multiple feature parameters that are each related to the user, the electrical appliance, and an environment where the electrical appliance is disposed, so as to derive the behavioral features in relation to behaviors of the user using the electrical appliance at the site and to establish the behavioral feature recognition model (0075, set priorities of the electric household appliance, or can set the priorities through a preset program according to seasons, climates, temperature, humidity, time, or the like. Further, this EoD system can automatically determine electric household appliance to which power should be preferentially supplied as appropriate based on a plan or an upper limit of power to be supplied and supply power to each electric household appliance based on the priorities of the electric household appliance from the result. Further, in addition to the plan and the upper limit of the power to be supplied, by learning a behavior pattern of the user, it is possible to build a power usage model and determine the priorities of the electric household appliance according to this model.).
Regarding claim 11, Hieda further described further stores an audio feature recognition model that is configured to recognize multiple audio features; wherein the audio features and the audio feature recognition model are derived and established by using a second machine learning algorithm to analyze the audio detection result based on multiple audio parameters that are each related to the environment where the electrical appliance is disposed; and wherein said processing module uses the audio feature recognition model to determine which of the audio features that the audio result generated by said audio receiving module matches, and generates the audio feature data based on the audio result that matches any of the audio features (0142, computing means for recognizing the state of the electric household appliance and detecting abnormality (such as failure and electricity leakage) is provided inside the server. In this case, the computing means may be means for detecting that the voltage waveform and/or the current waveform, or the like, or the characteristic amounts of the voltage and/or current waveforms computed at the arithmetic device indicate some abnormality by comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance.).
Regarding claim 12, Hieda further described an output module that is electrically connected to said processing module, wherein said processing module determines whether said electrical appliance is operating in one of a normal condition and an abnormal condition based on the current feature data, the current detection result and the audio feature data, and upon determining that the electrical appliance is operating in the abnormal condition, makes said output module to execute one of a first response of generating a warning output that indicates the abnormal condition, and a second response of controlling said socket module to terminate supply of electric power to the electrical appliance (0142, computing means for recognizing the state of the electric household appliance and detecting abnormality (such as failure and electricity leakage) is provided inside the server. In this case, the computing means may be means for detecting that the voltage waveform and/or the current waveform, or the like, or the characteristic amounts of the voltage and/or current waveforms computed at the arithmetic device indicate some abnormality by comparing the waveforms and the characteristic amounts with the normal state of the electric household appliance, and, as a result, the computing means may be configured to be able to display whether or not there is abnormality using sound or light as necessary when abnormality can be found in the state of the electric household appliance).
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
September 6, 2026
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