DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-11 are presented for examination based on the application filed on September 21, 2022.
Claims 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea.
Claims 1 and 5-11 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Liang, Wei, Jingjing Liu, Yining Lang, Bing Ning, and Lap-Fai Yu. “Functional workspace optimization via learning personal preferences from virtual experiences.” IEEE transactions on visualization and computer graphics 25, no. 5 (2019): 1836-1845. [herein “Liang”].
Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Liang as applied to claim 1 above, and in view of Lee, Jia-You, Hung-Yu Shen, and Tsung-Wen Chang. “Contactless inductive charging system with hysteresis loop control for small-sized household electrical appliances.” In 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2172-2178. IEEE, 2012 [herein “Lee”].
Claims 3-4 are rejected under 35 U.S.C. § 103 as being unpatentable over Liang and Lee as applied to claim 2 above, and further in view of Kim, Kyung-Bum, Jae Yong Cho, Hamid Jabbar, Jung Hwan Ahn, Seong Do Hong, Sang Bum Woo, and Tae Hyun Sung. “Optimized composite piezoelectric energy harvesting floor tile for smart home energy management.” Energy Conversion and Management 171 (2018): 31-37 [herein “Kim”].
This action is made non-Final.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on September 21, 2022; April 12, 2023; and June 3, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to because the unlabeled rectangular box filled with grid pattern in the room with the “Bathtub” shown in FIG. 6 and FIG. 7 should be provided with a descriptive text label. Para. 0022 states “In the example of FIG. 6, the space is an apartment in a building, and six electrical appliances (n) (n = 1, ···, 6) are installed in the space. Specifically, as the six electrical appliances (n), one audio device, one electric fan, one refrigerator, one microwave oven, one heater, and one television are installed in the space”, FIG. 6 illustrates the appliances as a rectangular box filled with grid pattern. However, FIG. 6 also shows a seventh appliance in the room with the “Bathtub”, also shown in FIG. 7. This seventh appliance should be provided with a descriptive text label. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Para. 0039, which cites “5.1 ch speaker” should be “5.1 channel speaker”.
Para. 0039, which cites “first threshold < the second threshold” should be “first threshold is less than the second threshold”
Appropriate correction is required.
Claim Objections
Claims 1-15 are objected to because of the following informalities:
Claim 4, which cites “wherein as the usage condition data, operation rate data indicating the operation rate is used” in Ln. 3-5, should be “wherein [[ the usage condition data is given as operation rate data indicating the operation rate is used” or similar as support by the specification, see Para. 0015.
Claim 8, which cites “the user who has a billing contract lives” in Ln. 3, is improper because there has been no previous recitation of “the user who has a billing contract lives”. For the purpose of examination, “the user who has a billing contract lives” will be interpreted as “[[a user who has a billing contract lives”.
Claim 8, which cites “the user lives” in Ln. 5, is improper because there has been no previous recitation of “the user”. For the purpose of examination, “the user lives” will be interpreted as “the user who has the billing contract lives”.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-8 are directed to a device and fall within the statutory category of a system; claim 9 is directed to a method and falls within the statutory category of a process; claim 10 is directed to a device and falls within the statutory category of a system; and claim 11 is directed to a method and falls within the statutory category of a process. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 9, 10 and 11: The limitations of “to create layout data indicating a layout of the electrical appliance for the space” and “outputs layout data indicating a layout for a certain space of all electrical appliances installed in the certain space or a part of electrical appliances installed in the certain space when position data indicating an installation position of the electrical appliance installed in the certain space and usage condition data indicating a usage condition of the electrical appliance installed in the certain space are given” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout for an electrical appliance based on where the appliance is currently located and how often the appliance is currently used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Therefore, yes, claims 1, 9, 10 and 11 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1, 9, 10 and 11: The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
1) “A layout creation device comprising processing circuitry” and “A learning model generation device comprising processing circuitry” which are merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate a judicial exception into elements. Further, the following additional element
2) “acquire position data indicating an installation position of an electrical appliance installed in a space” and “to acquire position data indicating an installation position of an electrical appliance installed in a space, usage condition data indicating a usage condition of the electrical appliance, and training data indicating a layout of the electrical appliance for the space” which are merely a recitation of insignificant extra-solution data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC). Further, the following additional element
3) “by giving the position data and usage condition data indicating a usage condition of the electrical appliance to a learning model” which is merely a recitation of insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) and/or is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) both of which do not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC). Further, the following additional element
4) “to learn a layout of one or more electrical appliances suitable for installation in the space using the position data, the usage condition data, and the training data, and generate a learning model” which are merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate a judicial exception into elements.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 9, 10 and 11 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Claims 1, 9, 10 and 11: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”).
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded the analysis within the provided framework, claims 1, 9, 10 and 11 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 2, it recites an additional limitation of “wherein the electrical appliance is supplied with power from a non-contact power supply coil installed in the space”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout for a home whose floor is installed with non-contact power supply coils to power electrical appliances wirelessly based on where the appliances are currently located and how often the appliance is currently used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 3, it recites an additional limitation of “wherein the usage condition data is calculated on a basis of a power supply condition of the non-contact power supply coil, and the position data indicates an installation position of the non-contact power supply coil that supplies power to the electrical appliance”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout for a home whose floor is installed with non-contact power supply coils to power electrical appliances wirelessly based on where the non-contact power supply coils are currently located and how often the appliance draws power its respective power supply.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 4, it recites an additional limitation of “calculates an operation rate of the electrical appliance on a basis of an energization time of the electrical appliance, wherein as the usage condition data, operation rate data indicating the operation rate is used” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation of mathematical evaluations. For example, calculating how often the appliance is used can be conducted based a ratio of when the non-contact power supply coil is actively supplying power to an appliance to the entire time the appliance has been installed in the home (See Para. 0024 and Equ. 1 for this example).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of mathematic evaluations but for the recitation of generic computer components, then it falls within the “Mathematical Operation” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 4, it recites an additional limitation of “calculates an operation rate of the electrical appliance on a basis of an energization time of the electrical appliance, wherein as the usage condition data, operation rate data indicating the operation rate is used”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally determine or draw with pen and paper how often the appliance is used by taking the time of when a non-contact power supply coil is actively supplying power to an appliance and dividing it by the entire time the appliance has been installed in the home.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 4, it recites an additional element recitation of “wherein the processing circuitry” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)). Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 5, it recites an additional limitation of “wherein the layout data includes an installation recommended electrical appliance and an installation position of the installation recommended electrical appliance”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout to include the recommend installment position for an electrical appliance based on where the appliance is currently located and how often the appliance is currently used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Regarding claim 6, it recites an additional limitation of “creates the layout data”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout for an electrical appliance based on where the power supply is currently located, how often the appliance is currently used, and the path a user travels to arrive at the appliance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 6, it recites additional element recitations of “wherein the processing circuitry outputs path data indicating a path taken by a user living in the space through the space” and “by giving the path data to the learning model in addition to giving the position data and the usage condition data to the learning model” is merely an insignificant extra-solution data outputting activities (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the following additional element of “the processing circuitry” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)). Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 6 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 7, it recites an additional limitation of “creates the layout data”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout for an electrical appliance based on where appliance is currently located, how often the appliance is currently used, and the path that a family of individuals and animals take to arrive at the appliance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 7, it recites an additional element recitation of “by giving one or more pieces of data among family data indicating a family structure of a user living in the space, animal presence data indicating presence or absence of an animal in the space, and hobby data indicating a hobby of the user to the learning model in addition to giving the position data and the usage condition data to the learning model” is merely an insignificant extra-solution data outputting activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the following additional element of “the processing circuitry” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)). Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 8, it recites an additional limitation of “circuitry creates the layout data of the electrical appliance for the space where the user lives”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper. For example, a person can mentally create or draw with pen and paper an optimum layout, in a home where someone owns and lives, for an electrical appliance based on where the appliance is currently located and how often the appliance is currently used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Furthermore, regarding claim 8, it recites an additional element recitation of “wherein the processing circuitry acquires the position data of the electrical appliance installed in the space where the user who has a billing contract lives” is merely an insignificant extra-solution data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Further, the following additional element of “the processing circuitry” is merely a recitation of generic computing components and functions being used as a tool to implement the judicial exception (see MPEP § 2106.05(f)). Further, the insignificant extra-solution data gathering, record update, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(II), “The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory”). Further, this claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional element amounts to significantly more, this claim also fails both Step 2A prong 2, thus this claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Therefore, having concluded the analysis within the provided framework, claims 1-11 do not recite patent eligible subject matter and are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception, an abstract idea, that has not been integrated into a practical application. The claims further do not recite significantly more than the judicial exception. Claims 2-8 are also rejected for incorporating the deficiency of their independent claim 1.
Claim Rejections - 35 U.S.C. § 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 (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.
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 1 and 5-11 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Liang, Wei, Jingjing Liu, Yining Lang, Bing Ning, and Lap-Fai Yu. “Functional workspace optimization via learning personal preferences from virtual experiences.” IEEE transactions on visualization and computer graphics 25, no. 5 (2019): 1836-1845. [herein “Liang”].
As per claim 1, Liang teaches “A layout creation device comprising processing circuitry”. (Pg. 1837 Sect. 1, “An optimization algorithm is applied to search for the optimal solution, resulting in the desired layout” [a layout creation]. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [layout creation device comprising processing circuitry]. Further see Sect. 1 and 7. The examiner has interpreted that finding the optimal solution for a desired layout using a personal computer with a processor as a layout creation device comprising processing circuitry.)
Liang teaches “acquire position data indicating an installation position of an electrical appliance installed in a space”. (Pg. 1837 Sect. 3, “Assume that the workspace consists of N components. For the ith component, we consider two attributes: position (xi,yi,zi) and orientation oi” [position data indicating a position in a space]. Pg. 1839 Sect. 5, “a person may move among the components, such as the refrigerator, countertop, and oven while carrying different virtual objects” [electrical appliance]. Fig. 2 shows that a given initial workspace layout including appliances such as range, stovetop, and refrigerator, e.g., acquire position data indicating an installation position of an electrical appliance installed in a space. Further see Sect. 3 and 5. The examiner has interpreted that given an initial workspace layout that consists of a refrigerator, oven, and a stovetop having position attributes as acquire position data indicating an installation position of an electrical appliance installed in a space.)
Liang teaches “create layout data indicating a layout of the electrical appliance for the space by giving the position data and usage condition data indicating a usage condition of the electrical appliance to a learning model.” (Pg. 1843 Sect. 7, “Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink” [create layout data indicating a layout of the electrical appliance for the space]. Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [e.g., by giving data indicating a usage condition to a learning model]. Pg. 1839 Sect. 4, “As shown in Fig. 3, each bar with different colors depicts the component with which the user interacts over time” [usage condition data indicating a usage condition of the electrical appliance]. Fig. 3 also shows the user interactions of the components in the layout, e.g. position data and usage condition data. Further see Sect. 4 and 7. The examiner has interpreted that optimizing the position of the refrigerator so it is close to the sink by modeling user personal preferences learned by monitoring the captured activities of the user over time in a workspace layout as create layout data indicating a layout of the electrical appliance for the space by giving the position data and usage condition data indicating a usage condition of the electrical appliance to a learning model.)
As per claim 5, Liang teaches “wherein the layout data includes an installation recommended electrical appliance and an installation position of the installation recommended electrical appliance.” (Pg. 1843 Sect. 7, “Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink”. Further see Sect. 4 and 7. The examiner has interpreted that optimizing the position of the refrigerator so it is close to the sink as wherein the layout data includes an installation recommended electrical appliance and an installation position of the installation recommended electrical appliance.)
As per claim 6, Liang teaches “wherein the processing circuitry outputs path data indicating a path taken by a user living in the space through the space, and the processing circuitry creates the layout data by giving the path data to the learning model in addition to giving the position data and the usage condition data to the learning model.” (Pg. 1843 Sect. 7, “In Fig. 10, we show examples of two users (User1 and User2). From the first to the fourth column are the four scenes (the same as Fig. 8), respectively. (a) and (c) are activities captured from two users plotted with a heatmap, where the color of each pixel depicts the weighted trajectories (the summation of the objects’ mass the user carried at each point) of the user” [e.g., outputs path data indicating a path taken by a user living in the space through the space]. “(b) and (d) are the optimized results according to their personal preferences. It is interesting to see that there are some similarities in one participant’s activities in some details across the four scenes in Fig. 10. For example, User1 preferred to store the food such as vegetables and fruits in the refrigerator (highlighted by green color). When she took out food from the refrigerator, she always went to the sink (highlighted by yellow color) to wash them directly. Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink” [creates the layout data by giving the path data to the learning model]. Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [e.g., by giving data indicating a usage condition to a learning model]. Pg. 1839 Sect. 4, “As shown in Fig. 3, each bar with different colors depicts the component with which the user interacts over time” [usage condition data indicating a usage condition of the electrical appliance]. Fig. 3 also shows the user interactions of the components in the layout, e.g. position data and usage condition data. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [e.g., by the processing circuitry]. Further see Sect. 4 and 7. The examiner has interpreted that optimizing the position of the refrigerator so it is close to the sink by modeling user personal preferences learned by monitoring the captured activities of the user over time such as generating a heat map of the trajectories taken by the user between components in a workspace layout implemented using a personal computer with a processor as wherein the processing circuitry outputs path data indicating a path taken by a user living in the space through the space, and the processing circuitry creates the layout data by giving the path data to the learning model in addition to giving the position data and the usage condition data to the learning model.)
As per claim 7, Liang teaches “wherein the processing circuitry creates the layout data by giving one or more pieces of data among family data indicating a family structure of a user living in the space, animal presence data indicating presence or absence of an animal in the space, and hobby data indicating a hobby of the user to the learning model in addition to giving the position data and the usage condition data to the learning model.” (Pg. 1843 Sect. 7, “It is interesting to see that there are some similarities in one participant’s activities in some details across the four scenes in Fig. 10. For example, User1 preferred to store the food such as vegetables and fruits in the refrigerator (highlighted by green color). When she took out food from the refrigerator, she always went to the sink (highlighted by yellow color) to wash them directly” [cooking, e.g., hobby data indicating a hobby of the user]. “Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink” [creates the layout data by giving data to the learning model]. Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [e.g., by giving data indicating a usage condition to a learning model]. Pg. 1839 Sect. 4, “As shown in Fig. 3, each bar with different colors depicts the component with which the user interacts over time” [usage condition data indicating a usage condition of the electrical appliance]. Fig. 3 also shows the user interactions of the components in the layout, e.g. position data and usage condition data. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [e.g., by the processing circuitry]. Further see Sect. 4 and 7. The examiner has interpreted that optimizing the position of the refrigerator so it is close to the sink by modeling user personal preferences learned by monitoring the captured activities of the user over time for taking out food from the refrigerator and preparing the food in a workspace layout implemented using a personal computer with a processor as wherein the processing circuitry creates the layout data by giving hobby data indicating a hobby of the user to the learning model in addition to giving the position data and the usage condition data to the learning model.)
As per claim 8, Liang teaches “wherein the processing circuitry acquires the position data of the electrical appliance installed in the space where the user who has a billing contract lives, and the processing circuitry creates the layout data of the electrical appliance for the space where the user lives.” (Pg. 1837 Sect. 1, “For example, one person might want to place a tea table on the left side of a sofa in a living room instead of on the right side” [e.g., where the user who has a billing contract lives]. Pg. 1837, “we take a kitchen as an example workspace to demonstrate our approach” [e.g., where the user who has a billing contract lives]. Pg. 1837 Sect. 3, “Assume that the workspace consists of N components. For the ith component, we consider two attributes: position (xi,yi,zi) and orientation oi” [position data indicating a position in a space]. Pg. 1839 Sect. 5, “a person may move among the components, such as the refrigerator, countertop, and oven while carrying different virtual objects” [electrical appliance]. Fig. 2 shows that a given initial workspace layout including appliances such as range, stovetop, and refrigerator, e.g., acquires the position data of the electrical appliance installed in a space where the user who has a billing contract lives. (Pg. 1843 Sect. 7, “Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink” [e.g., creates the layout data indicating a layout of the electrical appliance for the space where the user lives]. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [e.g., by the processing circuitry]. Further see Sect. 3-5 and 7. The examiner has interpreted that optimizing the position of the refrigerator for a given initial workspace layout that has position attributes for a kitchen as an example workspace for a person with a living room through the use of a personal computer with a processor as wherein the processing circuitry acquires the position data of the electrical appliance installed in the space where the user who has a billing contract lives, and the processing circuitry creates the layout data of the electrical appliance for the space where the user lives.)
Re Claim 9, it is a method claim, having similar limitations of claim 1. Thus, claim 9 is also rejected under the similar rationale as cited in the rejection of claim 1.
Re Claim 10, it is a method claim, having similar limitations of claim 1. Thus, claim 10 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 10, Liang teaches “A learning model generation device comprising processing circuitry”. (Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [learning model generation]. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [e.g., learning model generation device comprising processing circuitry]. Further see Sect. 4 and 7. The examiner has interpreted that user personal preferences learned by monitoring the captured activities of the user over time in a workspace layout using a personal computer with a processor as a learning model generation device comprising processing circuitry.)
Liang teaches “acquire position data indicating an installation position of an electrical appliance installed in a space, usage condition data indicating a usage condition of the electrical appliance, and training data indicating a layout of the electrical appliance for the space”. (Pg. 1837 Sect. 3, “Assume that the workspace consists of N components. For the ith component, we consider two attributes: position (xi,yi,zi) and orientation oi” [position data indicating a position in a space]. Pg. 1839 Sect. 5, “a person may move among the components, such as the refrigerator, countertop, and oven while carrying different virtual objects” [electrical appliance]. Fig. 2 shows that a given initial workspace layout including appliances such as range, stovetop, and refrigerator, e.g., acquire position data indicating an installation position of an electrical appliance installed in a space and training data indicating a layout of the electrical appliance for the space. Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [e.g., acquire usage condition data]. Pg. 1839 Sect. 4, “As shown in Fig. 3, each bar with different colors depicts the component with which the user interacts over time” [usage condition data indicating a usage condition of the electrical appliance]. Fig. 3 also shows the user interactions of the components in the layout, e.g. position data and usage condition data. Further see Sect. 3-5. The examiner has interpreted that given an initial workspace layout that consists of a refrigerator, oven, and a stovetop having position attributes and modeling user personal preferences learned by monitoring the captured activities of the user over time in a workspace layout as acquire position data indicating an installation position of an electrical appliance installed in a space, usage condition data indicating a usage condition of the electrical appliance, and training data indicating a layout of the electrical appliance for the space.)
Liang teaches “to learn a layout of one or more electrical appliances suitable for installation in the space using the position data, the usage condition data, and the training data, and generate a learning model that outputs layout data indicating a layout for a certain space of all electrical appliances installed in the certain space or a part of electrical appliances installed in the certain space”. (Pg. 1843 Sect. 7, “Based on his preferences of walking between the refrigerator and the sink frequently, our approach optimized the position of the refrigerator next to or close to the sink” [layout of an electrical appliance suitable for installation in the space, and outputs layout data indicating a layout for a certain space of all electrical appliances installed in the certain space]. Pg. 1838 Sect. 4, “The level of functionality of a workspace usually depends on a user’s personal preferences of using it. The preferences can be reflected by the user’s activities. In this section, we discuss how to learn such personal preferences for an individual user through the observation of his activities. Our approach defines a set of tasks in the virtual workspace that are typical for the kind of workspace. A user enters the initialized virtual workspace by wearing the HTC VIVE and HTC trackers. He is given the task instructions and is required to perform the tasks one at a time. During this process, all parameters from the HTC VIVE trackers are captured and are used to model the personal preferences of the user” [e.g., learning to layout using the position data, the usage condition data, and the training data; and generate a learning model]. Fig. 3 also shows the user interactions of the components in the layout, e.g. position data and usage condition data. Further see Sect. 4 and 7. The examiner has interpreted that optimizing the position of the refrigerator so it is close to the sink by modeling user personal preferences learned by monitoring the captured activities of the user over time in a workspace layout as to learn a layout of one or more electrical appliances suitable for installation in the space using the position data, the usage condition data, and the training data, and generate a learning model that outputs layout data indicating a layout for a certain space of all electrical appliances installed in the certain space.)
Re Claim 11, it is a system claim, having similar limitations of claim 10. Thus, claim 11 is also rejected under the similar rationale as cited in the rejection of claim 10.
Claim Rejections - 35 U.S.C. § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Liang as applied to claim 1 above, and in view of Lee, Jia-You, Hung-Yu Shen, and Tsung-Wen Chang. “Contactless inductive charging system with hysteresis loop control for small-sized household electrical appliances.” In 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2172-2178. IEEE, 2012 [herein “Lee”].
As per claim 2, Liang does not specifically teach “wherein the electrical appliance is supplied with power from a non-contact power supply coil installed in the space.”
However, in the same field of endeavor namely providing a more efficient experience for the user, Lee teaches “wherein the electrical appliance is supplied with power from a non-contact power supply coil installed in the space.” (Pg. 2172 Sect. 1, “Fig. 1 is the black diagram of contactless inductive charging system with hysteresis loop control for small-sized household electrical appliances”. Further see Sect. 1. The examiner has interpreted that providing a contactless inductive charging system for household electrical appliances as wherein the electrical appliance is supplied with power from a non-contact power supply coil installed in the space.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the electrical appliance is supplied with power from a non-contact power supply coil installed in the space” as conceptually seen from the teaching of Lee, into that of Liang because this modification of powering the appliance through a non-contact coil for the advantageous purpose of creating a unified power supply for a plethora of household electrical appliances (Lee, Pg. 2172 Sect. 1). Further motivation to combine be that Liang and Lee are analogous art to the current claim are directed to providing a more efficient experience for the user.
Claims 3-4 are rejected under 35 U.S.C. § 103 as being unpatentable over Liang and Lee as applied to claim 2 above, and further in view of Kim, Kyung-Bum, Jae Yong Cho, Hamid Jabbar, Jung Hwan Ahn, Seong Do Hong, Sang Bum Woo, and Tae Hyun Sung. “Optimized composite piezoelectric energy harvesting floor tile for smart home energy management.” Energy Conversion and Management 171 (2018): 31-37 [herein “Kim”].
As per claim 3, Liang does not teach “wherein the usage condition data is calculated on a basis of a power supply condition of the non-contact power supply coil”.
However, Lee teaches “wherein the usage condition data is calculated on a basis of a power supply condition of the non-contact power supply coil”. (Pg. 2175 Sect. 3, “Fig. 14 illustrates the condition of contactless inductive charging system under normal operating and standby proposed by this paper. Fig. 15 represents the hysteresis loop including normal operation and standby modes by tracking and offsetting the operating frequency of system from resonant frequency. When the secondary is removed from primary, the driver is set to the standby mode. Otherwise, when the secondary is on top of the primary, the driver is set to normal operation mode”. Further see Sect. 3 and Fig. 14-15. The examiner has interpreted that determining the condition of the operating and standby modes of the contactless inductive charging system as wherein the usage condition data is calculated on a basis of a power supply condition of the non-contact power supply coil.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the usage condition data is calculated on a basis of a power supply condition of the non-contact power supply coil” as conceptually seen from the teaching of Lee, into that of Liang because this modification of determining appliance usage based on power supplied for the advantageous purpose of tracking the operation of the system (Lee, Pg. 2177 Sect. 5). Further motivation to combine be that Liang and Lee are analogous art to the current claim are directed to providing a more efficient experience for the user.
Liang nor Lee teaches “the position data indicates an installation position of the non-contact power supply coil that supplies power to the electrical appliance.”
However, in the same field of endeavor namely providing a more efficient experience for the user, Kim teaches “the position data indicates an installation position of the non-contact power supply coil that supplies power to the electrical appliance.” (Pg. 31 Abstract, “One factor in the energy management of smart home is based upon the location of the home occupants. This location information then can be used to control various electrical and electronic devices and appliances. A floor tile is presented which can be used at different locations in the home and can generate enough energy to wirelessly transmit the information to electrical appliance when a person steps on it” [the position data indicates an installation position of the non-contact power supply that supplies power to the electrical appliance]. Further see Pg. 31 Abstract and Sect. 1. The examiner has interpreted that using location information to generate enough energy to control electronic appliances using a floor tile as the position data indicates an installation position of the non-contact power supply that supplies power to the electrical appliance.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “the position data indicates an installation position of the non-contact power supply coil that supplies power to the electrical appliance” as conceptually seen from the teaching of Kim, into that of Liang and Lee because this modification of determining the position of the power supply for the advantageous purpose of providing power to the appliance (Kim, Pg. 31 Abstract). Further motivation to combine be that Liang, Lee, and Kim are analogous art to the current claim are directed to providing a more efficient experience for the user.
As per claim 4, Liang teaches “wherein the processing circuitry calculates an operation rate of the electrical appliance on a basis of an [energization] time of the electrical appliance, wherein as the usage condition data, operation rate data indicating the operation rate is used.” (Pg. 1839 Sect. 5, “From the user’s original locations H, we estimate a distribution of the frequency of the visited times at each point when the user interacts with the pk-th component. Fig. 4 shows an example. The redder region represents that the points are visited more frequently” [calculates an operation rate of the electrical appliance on a basis of a time of the electrical appliance]. Pg. 1839 Sect. 4, “The user preferences on the object storage are captured in the setting up process, described in Sec. 4.1. After the setting up, when the user retrieves the corresponding objects to achieve the task, the preferences are reflected in the workflow. For example, in Fig. 3, if the user chose to place the plates into the orange cabinet, he may frequently transfer between that cabinet and the sink. During the optimization, the optimizer will tend to place the shelf and the sink as close together as possible. The shorter distance save the user energy when transferring between them.” [wherein as the usage condition data, operation rate data indicating the operation rate is use e.g., wherein the usage condition data is given as operation rate data indicating the operation rate is used]. Pg. 1841 Sect. 7, “We implemented our approach using C# and Unity 5.6 and ran the optimization approach on a PC equipped with 32GB of RAM, a Nvidia Titan X graphics card with 12GB of memory, and a 2.60GHz Intel i7- 5820K processor” [the processing circuitry calculates]. Further see Sect. 4-5 and 7. The examiner has interpreted that generating a distribution of frequency of the visited times at each point when the user interacts with the component that reflect the user preferences as wherein the processing circuitry calculates an operation rate of the electrical appliance on a basis of a time of the electrical appliance, wherein as the usage condition data, operation rate data indicating the operation rate is used.)
Liang nor Lee does not specifically teach “on a basis of an energization time”.
However, Kim teaches “an operation rate of the electrical appliance on a basis of an energization time of the electrical appliance”. Pg. 33 Sect. 3, “We developed a system that uses a wireless foot switch as a human mechanical piezoelectric energy harvester. Fig. 1(a) shows an application of the floor tile on an optimized PZN0.5C thick film cantilever type harvester supplying energy to wireless sensor node which controls the appliance switching in a real-time. The actual experimental photograph in Fig. 1(b) shows how the lights are turned on through the floor tile”. Fig. 1(b) shows that appliance is turned on when a person steps on the piezoelectric harvester and the appliance is off when there is no person on the harvester, e.g., an operation rate the electrical appliance. Pg. 33-35 Sect. 3, “The embedded PZN0.5C thick films in the piezoelectric harvester use human footsteps to drive the foot switch and thus to generate open-circuit output voltage of 42 V (human weight : 80 kg), even when the measurement probe polarity is changed, as shown in Fig. 4(c)”. Fig. 4(c) shows that voltage output of the piezoelectric harvester to control and power the appliance generated for a provided time sequence, e.g. on a basis of an energization time. Pg. 36 Sect. 4, “To validate its applicability to a real-time floor tile operated by the PZN0.5C thick films, it is essential to estimate the power consumption of the circuit which is composed of a transmitter sensor node” [energization time of the harvester to power the electrical appliance, e.g. energization time of the electrical appliance]. Further see Sect. 3-4. The examiner has interpreted that using a harvest to supply energy for the control of an appliance in real-time and generating a voltage to estimate the power consumed as an operation rate of the electrical appliance on a basis of an energization time of the electrical appliance.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “on a basis of an energization time” as conceptually seen from the teaching of Kim, into that of Liang and Lee because this modification of determining the operation of the power supply for the advantageous purpose of determining and controlling the power supplied to the appliance (Kim, Pg. 30 Abstract and Pg. 33 Sect. 3). Further motivation to combine be that Liang, Lee, and Kim are analogous art to the current claim are directed to providing a more efficient experience for the user.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2021/0073449 A1 Segev, Tom et al. teaches a method for performing floor plan analysis on a floor plan to ascertain room features associated with the functional requirements and technical specifications. The operations of the method include generatively analyzing the room features to determine a customized equipment configuration for at least some of the rooms, and generating a manufacturer dataset including a room identifier, an equipment identifier, and the customized equipment configuration.
Kán, Peter, and Hannes Kaufmann teach in “Automatic furniture arrangement using greedy cost minimization.” In 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 491-498. IEEE, 2018 a method of fast generation of furniture arrangements in interior scenes by optimizing the selection and arrangement of furniture objects in a room with respect to aesthetic and functional rules. The infinite trans-dimensional space of furniture layouts is rapidly explored by greedy cost minimization.
Cheng, Shih-Hsien, and Sheng-Fuu Lin in “Current/Voltage Measurement Scheme Using a Flexible Coil/Electrode Power Sensor to Monitor the Power of Two-Wired Household Appliances.” POWER 3, no. 7 (2015) teaches a method of electricity monitoring of two-wire household appliances.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing response, to fully consider the references in in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Simeon P Drapeau whose telephone number is (571)-272-1173. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET.
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/SIMEON P DRAPEAU/ Examiner, Art Unit 2188
/RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188