Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status
This instant application No. 18/054156 has Claims 1-20 pending.
Priority /Filing Date
Applicant claimed Foreign Priority from Chinese Application No. CN202211269602.8. The priority filing date of this application is October 18, 2022.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the Applicant’s submissions of the Information Disclosure Statements dated January 15, 2026 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609 C(2), a copy of each of the PTOL-1449s initialed and dated by the Examiner is attached to the instant Office action.
Claim Rejections - 35 USC § 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A Prong One:
Independent claims 1, 13, and 20 recite:
“determining at least one candidate construction site based on the region feature; wherein the region feature at least includes distribution of people flow in the region to be expanded and distribution of existing charging piles in the region to be expanded; and
determining at least one target construction site based on the at least one candidate construction site,” are process steps that cover both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships because distribution of people flow or existing charging piles are mathematical relationships that could be deduced mentally or using simple pen and/or paper. Said limitations in claims 1, 13 and 20 are a process that under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. Other than reciting “a management platform of an Internet of Things system”, “a user platform”, “a service platform”, “a management platform”, “a sensor network platform”, “an object platform” and “a non-transitory computer-readable storage medium storing computer instructions, when the computer instructions are executed by a processor” in the claims nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” and “mathematical concept” groupings of abstract ideas. As such claims 1, 13 and 20 recite an abstract idea.
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims recite the additional elements of “a management platform of an Internet of Things system” ,“a user platform”, “a service platform”, “a management platform”, “a sensor network platform”, “an object platform” and “a non-transitory computer-readable storage medium storing computer instructions, when the computer instructions are executed by a processor” to perform the claimed steps at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component/hardware. These additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of obtaining a region feature of a region to be expanded is data gathering step and is an insignificant pre-solution activity. Similarly, transmitting target construction site to the service platform through the general database and uploading the target construction site to the user platform are data output/data display steps and are insignificant post-solution activity. As such these additional elements also do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
Finally, the pre-processing step of receiving measured values and transmitting/outputting post simulation value is categorized as insignificant extra solution activity under 2106.05(g). Claims 1 ,13 and 20 only recite “a management platform of an Internet of Things system” ,“a user platform”, “a service platform”, “a management platform”, “a sensor network platform”, “an object platform” and “a non-transitory computer-readable storage medium storing computer instructions, when the computer instructions are executed by a processor” to perform the claimed steps and therefore only recite a general purpose computer/hardware rather than a specific machine under MPEP 2106.05(b), and are directed to mere instructions to apply the exception under MPEP 2106.05(f), and do not result in anything significantly more than the judicial exception. The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. The inclusion of the computer or memory and controller to perform the obtaining and determining steps amount to nor more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 13 and 20 are not patent eligible.
The dependent claims include the same abstract ideas and mathematical techniques recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 3 disclose wherein the region feature further includes distribution of economic level and traffic convenience in the region to be expanded using the one or more analysis techniques. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claims 4 and 14 disclose predicting an expected benefit of the candidate construction site through processing a site feature of each candidate construction site in the at least one candidate construction site based on a first prediction model; and determining the target construction site based on the expected benefit of the candidate construction site using the one or more analysis techniques. This is a process that, under its broadest reasonable interpretation, is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. Thus, the claims are directed to the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper. “machine learning model” as mentioned in the claim language could be a generic mathematical model that could be performed in the human mind or with the aid of pencil and paper.
Dependent claims 5, 6 and 15 disclose constructing a first candidate feature map based on the at least one candidate construction site, wherein the first candidate feature map includes nodes and edges, the nodes correspond to preset facilities in the region to be expanded, and the edges represent roads between the preset facilities corresponding to the nodes; and determining the at least one target construction site based on the first candidate feature map. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claim 7 and 16 disclose determining at least one second candidate feature map based on the first candidate feature map; and determining the at least one target construction site based on the at least one second candidate feature map. This is a process that, under its broadest reasonable interpretation, is a process step that covers mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. Thus, the claims are directed to the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claim 8 and 17 disclose determining a plurality of first candidate expansion maps based on the at least one second candidate feature map; determining a target expansion map through performing a plurality of rounds of iteration updates on the plurality of first candidate expansion maps until a preset iterative condition is satisfied; and determining the at least one target construction site based on the target expansion map. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claim 9 and 18 disclose determining an evaluation value of each first candidate expansion map in the plurality of first candidate expansion maps, wherein when a number of round of iteration is 1, the first candidate expansion map is the at least one second candidate feature map; when the number of round of iteration is larger than 1, the first candidate expansion map is a third candidate expansion map obtained from a previous round of iteration; determining a second candidate expansion map from the plurality of first candidate expansion maps based on the evaluation value or evaluation parameter of the first candidate expansion map; determining the third candidate expansion map through performing transformation processing on the second candidate expansion map; and determining the first candidate expansion map of a next round of iteration or the target expansion map based on the third candidate expansion map. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claim 10 and 19 disclose wherein the transformation processing includes a first transformation and a second transformation; the first transformation includes: selecting at least two second candidate expansion maps from a plurality of the second candidate expansion maps, exchanging one or more nodes in the at least two second candidate expansion maps to generate at least two candidate maps, and determining the third candidate expansion map based on the at least two candidate maps; and the second transformation includes: deleting or adding a node in a preliminary map to generate at least one third candidate expansion map, wherein the preliminary map is the second candidate expansion map or the candidate map. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claim 11 disclose wherein the preset iterative condition includes at least one of the number of round of iteration being not less than a preset round value; the evaluation value of the first candidate expansion map being not less than a preset evaluation value; and in at least two consecutive rounds of iteration, a change of the evaluation value of the first candidate expansion map being smaller than a preset change value. This is a process that, under its broadest reasonable interpretation, is a process step that covers both mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper and a mathematical concept including mathematical calculations and /or mathematical relationships. Thus, the claims are directed to the abstract idea of a mental process and mathematical concept.
Dependent claim 12 disclose the details of a user platform, a service platform, a sensor network platform, and an object platform in terms of general database and sensor network and also transmitting target construction site to the service platform through the general database and uploading the target construction site to the user platform – all of which are related to data gathering and or output/data display and are considered insignificant extra-solution activity, because none of these additional elements integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 1-4 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jing et al. hereafter, Jing (Chinese Publication No: CN113642757A).
Regarding Claim 1, Jing disclose a method for construction planning of a charging pile in a smart city implemented based on a management platform of an Internet of Things system for construction planning of the charging pile in the smart city (Jing: abstract), the method comprising:
obtaining a region feature of a region to be expanded (Jing: page 13 (1) - Data acquisition: Note the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data));
determining at least one candidate construction site based on the region feature (Jing pages 13-14 (2) - Data pre-processing; (3) Rating of utilization; (4) Model training: Note how the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data) are utilized in model training for the selection of obtaining the optimal construction planning scheme of site selection (i.e. candidate construction site) and volume fixing of the charging pile of the Internet of things);
wherein the region feature at least includes distribution of people flow in the region to be expanded (Jing: page 13 (1) - Data acquisition: the residential area data comprises position data, permanent population data, occupied area and economic income of the residential area (note that the site information published by the operator is continuously collected within a period of time-which is construed as distribution of people flow in the region to be expanded)) and distribution of existing charging piles in the region to be expanded (Jing: page 13 (1) - Data acquisition: charging station data of the existing charging pile in an area within 5 km around the charging pile are obtained (note that the site information published by the operator is continuously collected within a period of time-distribution of existing charging piles in the region to be expanded)); and
determining at least one target construction site based on the at least one candidate construction site (Jing: page 14 (4) Model training: obtaining the optimal construction planning scheme of site selection and volume fixing of the charging pile of the Internet of things).
Regarding Claim 20, it is a computer-implemented method claim corresponding to the method as recited claim 1. Therefore, it is rejected for the same reason as claim 1 above. The additional limitation such as “A non-transitory computer-readable storage medium storing computer instructions, when the computer instructions are executed by a processor” is being anticipated by citations of Jing in page 16 third paragraph.
Regarding Claim 2, Jing further disclose the method of claim 1, wherein the region feature further includes distribution of economic level in the region to be expanded (Jing: section (1) - Data acquisition: economic benefit).
Regarding Claim 3, Jing further disclose the method of claim 1, wherein the region feature further includes traffic convenience in the region to be expanded (Jing: page 13 (1) - Data acquisition: automobile services, transportation facilities).
Regarding Claim 4, Jing further disclose the method of claim 1, wherein the determining at least one target construction site based on the at least one candidate construction site comprises:
predicting an expected benefit of the candidate construction site through processing a site feature of each candidate construction site in the at least one candidate construction site based on a first prediction model, wherein the first prediction model is a machine learning model (Jing: pages 13-14 (2) - Data pre-processing; (3) Rating of utilization; (4) Model training: Note how the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data) are utilized in model training for the selection of obtaining the optimal construction planning scheme of site selection (i.e. candidate construction site) and volume fixing of the charging pile of the Internet of things); and
determining the target construction site based on the expected benefit of the candidate construction site (Jing: pages 13-14 (2) - Data pre-processing; (3) Rating of utilization; (4) Model training: Note how the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data) are utilized in model training for the selection of obtaining the optimal construction planning scheme of site selection (i.e. candidate construction site) and volume fixing of the charging pile of the Internet of things).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 of this title, 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.
6. Claims 5-19 are rejected under 35 U.S.C. 103 as being obvious over Jing et al. hereafter, Jing (Chinese Publication No: CN113642757A), in view of Shao et al. hereafter Shao (Pub. No.: US 2023/0124522 A1).
Regarding Claim 5, Jing discloses the method of claim 1.
Jing further disclose determining at least one target construction site based on the at least one candidate construction site (Jing: page 14 (4) Model training: obtaining the optimal construction planning scheme of site selection and volume fixing of the charging pile of the Internet of things).
However, Jing do not explicitly disclose
constructing a first candidate feature map based on the at least one candidate construction site, wherein the first candidate feature map includes nodes and edges, the nodes correspond to preset facilities in the region to be expanded, and the edges represent roads between the preset facilities corresponding to the nodes; and
determining the at least one target
Shao disclose:
constructing a first candidate feature map based on the at least one candidate construction site, wherein the first candidate feature map includes nodes and edges, the nodes correspond to preset facilities in the region to be expanded, and the edges represent roads between the preset facilities corresponding to the nodes (Shao: Figure 4, [0080]-[0084]; Especially see [0080]: GNN model may process map data constructed based on relationships of intersections of various roads to determine one or more target regions 430. In some embodiments, the graph may include a plurality of nodes and edges, the nodes correspond to the intersections of each road, and the edges correspond to a relationship between the road connections. In some embodiments, the edges correspond to a spatial position relationship between roads, and the spatial position relationship may be a relative position relationship, a distance relationship, etc. In some embodiments, the nodes and edges may include their respective features, respectively. In some embodiments, a node feature may include whether there are traffic lights at the intersection of each road and the duration of traffic lights. An edge feature may include a lane type corresponding to the road, a number of lanes, and whether there is an underpass tunnel; and see [0084]: In some embodiments, a node feature of the traffic state prediction model 420 may also include at least one of a first strategy adjustment feature 410-2 or a second strategy adjustment feature 410-3); and
determining the at least one target (Shao: Figure 4, [0080]: GNN model may process map data constructed based on relationships of intersections of various roads to determine one or more target regions 430).
Jing and Shao are analogous art because they are from the same field of endeavor. They both relate to IoT infrastructure.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above IoT charging pile construction planning development application, as taught by Jing, and incorporating the use of Graph Neural Network (GNN) model, as taught by Shao.
One of ordinary skill in the art would have been motivated to do this modification in order to improve efficiency and quality of IoT charging pile construction planning.
Regarding Claim 15, the claim recites the same substantive limitations as claim 5 and is rejected using the same teachings.
Regarding Claim 6, the combinations of Jing and Shao further disclose the method of claim 5, wherein a feature of the edge includes a length and convenience of the road corresponding to the edge (Shao: Figure 4, [0080]: In some embodiments, the edges correspond to a spatial position relationship between roads, and the spatial position relationship may be a relative position relationship, a distance relationship, etc.……..In some embodiments, a node feature may include whether there are traffic lights at the intersection of each road and the duration of traffic lights. An edge feature may include a lane type corresponding to the road, a number of lanes, and whether there is an underpass tunnel).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 7, the combinations of Jing and Shao further disclose the method of claim 5, wherein the determining at least one target construction site based on the first candidate feature map comprises:
determining at least one second candidate feature map based on the first candidate feature map (Shao: Figure 3, [0071]-[0074]; especially see [0072]: In some embodiments, the processing device may switch the first traffic scheduling strategy to the second traffic scheduling strategy in a variety of ways; also see [0073]: the processing device may switch the first traffic scheduling strategy to the second traffic scheduling strategy by determining the traffic data information (e.g., the speed of the vehicle in the target area) of the vehicle in the target area during the current time period and the next time period); and
determining the at least one target (Shao: Figure 3, [0071]-[0074]; especially see [0073]: The processing device may switch the first traffic scheduling strategy to the second traffic scheduling strategy through the database based on the traffic data information in one or more target areas during the current time period and the next time period).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 16, the claim recites the same substantive limitations as claim 7 and is rejected using the same teachings.
Regarding Claim 8, the combinations of Jing and Shao further disclose the method of claim 7, wherein the determining at least one target construction site based on the at least one second candidate feature map comprises:
determining a plurality of first candidate expansion maps based on the at least one second candidate feature map (Shao: Figure 4, [0081]-[0084]: In some embodiments, as shown in FIG. 4, parameters of the traffic state prediction model 420 may be trained by a plurality of labeled first training samples 440. In some embodiments, the processing device may obtain a plurality of groups of first training samples 440, and each group of first training samples 440 may include a plurality of training data and labels corresponding to the training data);
determining a target expansion map through performing a plurality of rounds of iteration updates on the plurality of first candidate expansion maps until a preset iterative condition is satisfied (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples); and
determining the at least one target construction site based on the target expansion map (Shao: Figure 3, [0071]-[0074]; especially see [0073]: The processing device may switch the first traffic scheduling strategy to the second traffic scheduling strategy through the database based on the traffic data information in one or more target areas during the current time period and the next time period).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 17, the claim recites the same substantive limitations as claim 8 and is rejected using the same teachings.
Regarding Claim 9, the combinations of Jing and Shao further disclose the method of claim 8, wherein at least one round of iteration in the plurality of iteration updates comprises:
determining an evaluation value of each first candidate expansion map in the plurality of first candidate expansion maps, wherein when a number of round of iteration is 1, the first candidate expansion map is the at least one second candidate feature map (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples); when the number of round of iteration is larger than 1, the first candidate expansion map is a third candidate expansion map obtained from a previous round of iteration (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples);
determining a second candidate expansion map from the plurality of first candidate expansion maps based on the evaluation value or evaluation parameter of the first candidate expansion map (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples);
determining the third candidate expansion map through performing transformation processing on the second candidate expansion map (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples); and
determining the first candidate expansion map of a next round of iteration or the target expansion map based on the third candidate expansion map (Shao: Figure 3, Figure 4, [0081]-[0084]: In some embodiments, the processing device may iteratively update the parameters of the initial traffic state prediction model 450 to make a loss function of the initial traffic state prediction model meet a preset condition based on the plurality of groups of first training samples).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 18, the claim recites the same substantive limitations as claim 9 and is rejected using the same teachings.
Regarding Claim 10, the combinations of Jing and Shao further disclose the method of claim 9, wherein the transformation processing includes a first transformation and a second transformation (Shao: Figure 3, [0068]-[0073], Figure 4, [0081]-[0084]);
the first transformation includes: selecting at least two second candidate expansion maps from a plurality of the second candidate expansion maps, exchanging one or more nodes in the at least two second candidate expansion maps to generate at least two candidate maps, and determining the third candidate expansion map based on the at least two candidate maps (Shao: Figure 3, [0068]-[0073], Figure 4, [0081]-[0084]); and
the second transformation includes: deleting or adding a node in a preliminary map to generate at least one third candidate expansion map, wherein the preliminary map is the second candidate expansion map or the candidate map Shao: Figure 3, [0068]-[0073], Figure 4, [0081]-[0084]).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 19, the claim recites the same substantive limitations as claim 10 and is rejected using the same teachings.
Regarding Claim 11, the combinations of Jing and Shao further disclose the method of claim 9, wherein the preset iterative condition includes at least one of the number of round of iteration being not less than a preset round value (Shao: Figure 3, [0068]-[0073]: preset threshold value; Figure 4, [0081]-[0084]);
the evaluation value of the first candidate expansion map being not less than a preset evaluation value (Shao: Figure 3, [0068]-[0073]: preset threshold value; Figure 4, [0081]-[0084]); and
in at least two consecutive rounds of iteration, a change of the evaluation value of the first candidate expansion map being smaller than a preset change value (Shao: Figure 3, [0068]-[0073]: preset threshold value; Figure 4, [0081]-[0084]).
Motivation to combine Jing with Shao is same here as Claim 5.
Regarding Claim 12, the combinations of Jing and Shao further disclose the method of claim 1, wherein the Internet of Things system for construction planning of the charging pile in the smart city further comprises a user platform, a service platform, a sensor network platform, and an object platform (Shao: Figure 2);
the management platform includes a general database of the management platform and a plurality of management sub-platforms (Shao: Figure 2, [0029], [0030]);
the sensor network platform includes a plurality of sensor network sub-platforms (Shao: [0038]);
different regions to be expanded correspond to different sensor network sub-platforms (Shao: Figure 2, [0029], [0030], [0038]); the different sensor network sub-platforms correspond to different management sub platforms (Shao: Figure 2, [0029], [0030], [0038]);
the region feature of the region to be expanded is obtained based on the object platform and uploaded to the corresponding management sub-platform based on the sensor network sub-platform corresponding to the region to be expanded (Shao: Figures 1 & 2, [0038]-[0045]);
the method further comprising:
transmitting the at least one target construction site to the service platform through the general database of management platform and uploading the at least one target construction site to the user platform based on the service platform (Shao: Figures 1 & 2, [0037]-[0045]: The special transport management platform may provide special transport information (such as quantity, travel time, location, etc.) for the information management platform through a special transport database).
Motivation to combine Jing with Shao is same here as Claim 13.
Regarding Claim 13, Jing disclose an Internet of Things system for construction planning of a charging pile in a smart city (Jing: abstract),
obtain region feature of a region to be expanded (Jing: page 13 (1) - Data acquisition: Note the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data));
determining at least one candidate construction site based on the region feature (Jing pages 13-14 (2) - Data pre-processing; (3) Rating of utilization; (4) Model training: Note how the POI point-of-interest data, residential area data, charging station data of the existing charging pile in an area etc. (region feature data) are utilized in model training for the selection of obtaining the optimal construction planning scheme of site selection (i.e. candidate construction site) and volume fixing of the charging pile of the Internet of things),
wherein the region feature at least includes distribution of people flow in the region to be expanded (Jing: page 13 (1) - Data acquisition: the residential area data comprises position data, permanent population data, occupied area and economic income of the residential area (note that the site information published by the operator is continuously collected within a period of time-which is construed as distribution of people flow in the region to be expanded)) and distribution of existing charging piles in the region to be expanded (Jing: page 13 (1) - Data acquisition: charging station data of the existing charging pile in an area within 5 km around the charging pile are obtained (note that the site information published by the operator is continuously collected within a period of time-distribution of existing charging piles in the region to be expanded));
determining at least one target construction site based on the at least one candidate construction site (Jing: page 14 (4) Model training: obtaining the optimal construction planning scheme of site selection and volume fixing of the charging pile of the Internet of things);
Jing do not explicitly disclose:
a user platform, a service platform, a management platform, a sensor network platform, and an object platform;
the sensor network platform includes a plurality of sensor network sub-platforms;
different regions to be expanded correspond to different the sensor network sub-platforms;
the management platform includes a general database of the management platform
and a plurality of management sub-platforms;
the object platform is configured to obtain region feature of a region to be
expanded;
the sensor network sub-platform is configured to obtain the region feature of the
corresponding region to be expanded based on the object platform, and upload the
region feature to the corresponding management sub-platform;
the management sub-platform is configured to perform operations including:
transmitting at least one target
general database of the management platform; and
the service platform is configured to upload the at least one target
to the user platform.
Shao disclose:
a user platform, a service platform, a management platform, a sensor network platform, and an object platform (Shao: Figure 2);
the sensor network platform includes a plurality of sensor network sub-platforms (Shao: [0038]);
different regions to be expanded correspond to different the sensor network sub-platforms (Shao: Figure 2, [0029], [0030], [0038]);
the management platform includes a general database of the management platform and a plurality of management sub-platforms (Shao: Figure 2, [0029], [0030]);
the object platform is configured to obtain region feature of a region to be expanded (Shao: [0029], [0030], [0039]);
the sensor network sub-platform is configured to obtain the region feature of the corresponding region to be expanded based on the object platform, and upload the region feature to the corresponding management sub-platform (Shao: Figures 1 & 2, [0038]-[0045]);
the management sub-platform is configured to perform operations including:
transmitting at least one target (Shao: Figures 1 & 2, [0037]-[0045]: The special transport management platform may provide special transport information (such as quantity, travel time, location, etc.) for the information management platform through a special transport database); and
the service platform is configured to upload the at least one target (Shao: Figures 1 & 2, [0037]-[0045]: The special transport management platform may provide special transport information (such as quantity, travel time, location, etc.) for the information management platform through a special transport database).
Jing and Shao are analogous art because they are from the same field of endeavor. They both relate to IoT infrastructure.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above IoT charging pile construction planning development application, as taught by Jing, and incorporating the use of control management platform as well as plurality of management sub-platforms., as taught by Shao.
One of ordinary skill in the art would have been motivated to do this modification in order to improve efficiency and quality of IoT charging pile construction planning.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xiuting Yuan (Pub. No.: US 20200335228 A1) teaches a system, a method and a computer-readable storage medium for realizing Internet of Things for smart city based on street lamps and lamp posts.
Fatemi et al. (Pub. No.: US 20220101103 A1) teaches a graph structure having nodes and edges is represented as an adjacency matrix, and nodes of the graph structure have node features. A computer-implemented method and system for generating a graph structure are provided.
Xu et al. (A Deployment Model of Charging Pile based on Random Forest for Shared Electric Vehicle in Smart Cities,2018, IEEE, pp 49-54) conceptually presents deployment of the charging piles in the parking station of the shared electric vehicle, and propose a charging pile deployment model based on the random forest algorithm.
Kong et al. (Charging Pile Siting Recommendations via the Fusion of Points of Interest and Vehicle Trajectories, China Communications • November 2017, pp 29–38) propose the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories.
He et al. (Optimal Site Selection Planning of EV Charging Pile Based on Genetic Algorithm, China Communications, IEEE, 2020, pp 1799–1803) presents an optimal site selection model for multi-objective planning of EV charging pile using the genetic algorithm to solve the model.
8. Examiner’s Remarks: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings 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 responses, to fully consider the references 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, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Correspondence Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IFTEKHAR A KHAN whose telephone number is (571)272-5699. The examiner can normally be reached on M-F from 9:00AM-6:00PM (CST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached on (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IFTEKHAR A KHAN/Primary Examiner, Art Unit 2187