DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 4/29/2025 have been fully considered but they are not persuasive.
In response to arguments on pages 11-12 of the remarks regarding the 101 rejection and subject matter eligibility test step 2A, Prong 1, the step asks whether the claim recites an abstract idea, and it is submitted that the determination of “a respective predicted utilization of a respective EV charge point at each of the plurality of candidate locations” as recited in claim 1 constitutes an abstract idea. The “machine learning model” is not specifically defined or described, and it is maintained that the recitation of the “machine learning model” is broad enough to encompass mathematical calculations, and is considered part of the abstract idea. For example, paragraph 0088 of the specification as originally filed discloses “The machine learning model is trained at 320 using the sets of static and dynamic features and usage corresponding to the sets of static and dynamic features from 316. This training correlates the different features, static and dynamic, with EV charge point utilization”, which can be interpreted as the “machine learning model” being a function of variables, which is a mathematical calculation. Even if the “machine learning model” is found to not be mathematical calculations, the claim still recites the abstract idea of determining “a respective predicted utilization of a respective EV charge point at each of the plurality of candidate locations”, and thus for step 2A, prong one, the claim includes an abstract idea.
In response to arguments on pages 13-14 of the remarks regarding the 101 rejection and subject matter eligibility test step 2A, Prong 2, it is maintained that generating a map is interpreted as the display of information, which is well-understood, routine, and conventional, and constitutes insignificant post solution activity, and is not a practical application of the abstract idea. Furthermore, even if the “machine learning model” is found to not be mathematical calculations, the “machine learning model” does not rise to a practical application because it can be considered an insignificant application utilizing a generic computer component.
In response to arguments on pages 15-17 of the remarks regarding the 101 rejection and subject matter eligibility test step 2B, it is maintained that the recitations of generating a representation of a map as recited in independent claims 1; the processor and memory of claim 9; and the storage medium and generating a representation of a map of claim 18 are not “significantly more” than the abstract idea as described in the rejection below. Applicant argues the claims recite “specific machine learning processes”, but the claims recite a generic “machine learning model”, which is not considered “significantly more”.
In response to arguments that primary reference TRIPATHI does not disclose the claimed “static map features”, the one or more point of interest locations can be considered “static map features”, and also the disclosure of the road intersections and the position of the road intersections determined by the positioning system can be considered “static map features” that “correspond to road network attributes”, within the broadest reasonable interpretation. It is therefore maintained that TRIPATHI discloses “static map features” as described in the rejection. Secondary reference HUSAREK is relied upon to teach the amended recitation of “a functional class for the road segment data or link data” as described in the rejection below. It is submitted that TRIPATHI as modified by HUSAREK teaches the method of claim 1, the apparatus of claim 9, and the computer program product of claim 18. Claim Objections
Claims 1-3 and 5 are objected to because of the following informalities:
In claim 1, line 12, --the-- should be inserted before “static map features”.
In claim 2, line 3, perhaps “a corresponding location” should be changed to --the corresponding candidate location--.
In claim 3, line 1, perhaps “candidate locations” should be changed to --each--.
In claim 5, line 3, “a respective predicted utilization” should be changed to --the respective predicated utilization--.
In claim 5, lines 3-4, “an EV charge point” should be changed to --the EV charge point--.
Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without reciting additional elements that integrate the judicial exception into a practical application. Moreover, the claims do not appear to recite additional elements that amount to significantly more than the judicial exception.
Claim 1 recites a method which produces a representation of a map including a plurality of candidate locations for EV charging points. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and is thus considered a Judicial Exception. The recited method does no more than automate the mental processes that a user can perform to determine and communicate control instructions. The step of generating a map can be considered an additional element, but the generation of the map amounts to the display of information, which is a well-understood, routine, and conventional function, and/or insignificant extra solution activity (see examples of activities that the courts have found to be insignificant extra-solution activity in MPEP 2106.05(g)). Furthermore, the mere generation of map and map information is considered a mental process (see MPEP 2106.04(a)(2)(III)(A)(“ a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”)). Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of generating a map is a well-known, routine, and conventional computer function.
Claims 2-8 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
Claim 9 recites an apparatus comprising at least one processor and at least one memory which predicts utilization of an EV charging point at a candidate location. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and are thus considered a Judicial Exception. These limitations appear to be an attempt to generally link the use of the judicial exception to the use of circuitry (i.e., “at least one processor and at least one memory”). The recited circuitry does no more than automate the mental processes that a user can perform to determine and communicate control instructions. Thus, the claim as a whole does not integrate the recited judicial exception (i.e., the predicted utilization of an EV charging point at a candidate location) into a practical application.
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of a processor and a memory are recited at a high level of generality.
Claims 10-17 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
Claim 18 recites a computer program product comprising at least one non-transitory computer-readable storage medium having program code which generates a representation of a map including a plurality of candidate locations for EV charge points. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and are thus considered a Judicial Exception. These limitations appear to be an attempt to generally link the use of the judicial exception to the use of circuitry (i.e., via the “storage medium” and “program code instructions”). The recited circuitry does no more than automate the mental processes that a user can perform to determine and communicate control instructions. The step of generating a map can be considered an additional element, but the generation of the map amounts to the display of information, which is a well-understood, routine, and conventional function, and/or insignificant extra solution activity (see examples of activities that the courts have found to be insignificant extra-solution activity in MPEP 2106.05(g)). Furthermore, the mere generation of map and map information is considered a mental process (see MPEP 2106.04(a)(2)(III)(A)(“ a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”)).. Thus, the claim as a whole does not integrate the recited judicial exception (i.e., determining a plurality of candidate locations for EV charge points) into a practical application.
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitation of a storage medium is recited at a high level of generality; and the additional element of generating a map is a well-known, routine, and conventional computer function.
Claims 19-20 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
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 (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 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(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over TRIPATHI (US 2017/0228840; cited in previous office action) in view of HUSAREK (WO 2022/043025A1; cited on PTO-892 with date 2/3/2025; English machine translation is included with office action).
Regarding claim 1, TRIPATHI discloses a method comprising:
receiving, from a map database (¶ 0023: an online map service can geocode each of the one or more point of interest locations. In an embodiment, the online map service determines the latitude and longitude coordinates of the address of the one or more point of interest locations), a representation of a road network comprising road segment data or link data, wherein the road segment data or link data is representative of roads, streets, or paths (¶ 0105: the application server 108 may identify one or more point of interest locations within the pre-defined area based on the map data. At step 610, the application server 108 may receive traffic information between a plurality of road intersections within the pre-defined area);
receiving an indication of a plurality of candidate locations for electric vehicle “EV” charge points (¶ 0077-0079: user may select a plurality of candidate locations);
determining static map features of the plurality of candidate locations (¶ 0061: the demand prediction unit 208 may be configured to identify one or more point of interest locations in the predefined area based on a map data of the predefined area), wherein each of the static map features correspond to road network attributes for the road segment data or link data proximate to a corresponding candidate location (¶ 0057: the traffic information may include information about traffic speeds, traffic density, and travel times between the plurality of road intersections within the pre-defined area. In an alternate embodiment, the database server 102 is periodically updated based on the traffic information gathered/received from the one or more sensors installed at a plurality of road intersections…In an embodiment, a positioning system (e.g., a GPS system) in conjunction with one or more sensors may be utilized to determine the traffic information at the plurality of road intersections; e.g., attributes may comprise position of intersections);
processing the plurality of candidate locations and static map features using a machine learning model (¶ 0099: in order to train the demand prediction unit 208 to predict the one or more locations for placement of one or more replenishment stations for one or more vehicles, the demand prediction unit 208 is trained using the training data set and a test data set; ¶ 0100-0101), the machine learning model trained on existing EV charge point locations (¶ 0052: the historical demand data includes the number of charge units consumed per hour at each of the plurality of existing replenishment stations in the predefined area. The plurality of existing replenishment stations may transmit the demand data periodically to the database server 102), existing EV charge point static map features (¶ 0061: see above; ¶ 0062: Based on the location of the one or more point interest locations, the demand prediction unit 208 may be configured to create a fourth data structure; ¶ 0063: after creation of the first data structure, the second data structure, the third data structure, and the fourth data structure, the demand prediction unit 208 may utilize canonical correlation analysis technique to create a first replenishment prediction model P1, a second replenishment prediction model P2, and a third replenishment prediction model P3), and existing EV charge point utilization (¶ 0052: historical demand data);
determining, based on the machine learning model, a respective predicted utilization of a respective EV charge point at each of the plurality of candidate locations (¶ 0074: the location selected by the user may be depicted using an interactive marker. The user-computing device 104 may be configured to transmit location information (geographical coordinates) associated with the selected location. Based on the received location information and the one or more input parameters, the demand prediction unit 208 may be configured to determine the first demand prediction, the second demand prediction, and the third demand prediction; ¶ 0102: After the training of the demand prediction unit 208 is complete based on the training data set, then the demand prediction unit 208 may be utilized to predict the replenishment demand at a plurality of locations within the pre-defined area. Additionally, in an embodiment, the demand prediction unit 208 may identify the one or more locations from the plurality of locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the plurality of locations and the pre-defined threshold); and
generating a representation of a map including the plurality of candidate locations, wherein candidate locations of the plurality of candidate locations are visually distinguished based on the respective predicted utilization of the respective EV charge point at each of the candidate locations (¶ 0080: the user interface is displayed on the user-computing device 104 that includes a map. In an embodiment, one or more user interactive markers, corresponding to the identified one or more locations, may be displayed on the map. In response to the display of the one or more user interactive markers, an input may be received from a user of the user-computing device 104 on the one or more user interactive markers. In response to the input, the replenishment demand at the identified location may be displayed on a display screen of the user-computing device 104 in a form of one or more graphical representations comprising a bar chart, a pie chart, a heat map, and/or a line chart).
TRIPATHI fails to disclose the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location.
HUSAREK discloses the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location (¶ 0073: structural environmental information for Region 3 and/or weather information for Region 3 and/or a number of non-electric vehicles in Region 3 can be collected and taken into account when determining the fifth data. The structural environment information particularly represents the condition of the roads and/or the traffic volume and/or the type of roads and/or the gradients and inclines of the roads in Region 3; ¶ 0076: the computing unit 8 is designed to determine the number of charging stations 2 to be installed, depending on the third, fourth and fifth data points…Thus, the determination of the number of charging stations to be installed is carried out based on a wide variety of extensive information and data. This allows for the most accurate and efficient determination of the number of charging stations to be installed in region 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the functional class for the road segment data or link data of HUSAREK into the method of TRIPATHI to produce an expected result of a method for determining a plurality of candidate locations based on functional class for the road segment data or link data. The modification would be obvious because one of ordinary skill in the art would be motivated to allow for the most accurate and efficient determination of the number of charging stations to be installed (HUSAREK, ¶ 0076).
Regarding claim 2, TRIPATHI discloses ranking the plurality of candidate locations for EV charge points based on their respective predicted utilization of EV charge points at a corresponding location (¶ 0077-0080).
Regarding claim 3, TRIPATHI discloses candidate locations of the plurality of candidate locations are visually distinguished using one or more highlighting effects to represent predicted utilization of EV charge points at a corresponding location (¶ 0080).
Regarding claim 4, TRIPATHI discloses the static map features of a candidate location of the plurality of candidate locations forms a candidate location vector for the candidate location, wherein the candidate location vector is input to the machine learning model to determine the respective predicted utilization of an EV charge point at the candidate location (¶ 0026-0028, 0030, 0074-0078).
Regarding claim 5, TRIPATHI discloses one or more of: generating a site value for each of the candidate locations of the plurality of candidate locations, wherein each site value is based on a respective predicted utilization of an EV charge point at a respective candidate location; and generating a recommended number of EV charge points for each of the candidate locations of the plurality of candidate locations, wherein the recommended number of EV charge points is based on the respective predicted utilization of an EV charge point at the respective candidate location (¶ 0026-0028, 0030, 0074-0078, 0102).
Regarding claim 6, TRIPATHI discloses the static map features of candidate locations further include at least one of: point-of-interest categories proximity to a respective candidate location; point-of-interest density proximate the respective candidate location; and population density proximate the respective candidate location (¶ 0061).
Regarding claim 7, TRIPATHI as modified by HUSAREK teaches the method as applied to claim 1, and TRIPATHI further discloses the respective predicted utilization of the respective EV charge point at each of the plurality of candidate locations comprises a maximum predicted utilization and a duration of the maximum predicted utilization (¶ 0079).
TRIPATHI fails to disclose a maximum predicted utilization percentage and a duration of the maximum predicted utilization percentage.
However, expressing the maximum predicted utilization as disclosed in TRIPATHI as a percentage would not provide new or unexpected results, and constitutes an obvious modification.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the maximum predicted utilization percentage into the method of TRIPATHI as modified by HUSAREK to produce an expected result of a method for predicting utilization of a charge point at a candidate location based the maximum predicted utilization percentage. The modification would be obvious because one of ordinary skill in the art would be motivated to express the predicted utilization as a performance metric or proportion.
Regarding claim 8, TRIPATHI discloses in response to the respective predicted utilization having a maximum predicted utilization percentage at 100% for a duration satisfying a predetermined threshold, increasing a number of EV charge points at a respective candidate location (¶ 0079: a predetermined threshold duration is implied when predicting the replenishment demand in terms of number of charge units consumed per hour; ¶ 0089, 0110).
Regarding claim 9, TRIPATHI discloses an apparatus comprising at least one processor (202, Fig. 2) and at least one memory (204, Fig. 2) including computer program code (¶ 0045: processor 202 comprises suitable logic, circuitry, interfaces, and/or code that may be configured to execute a set of instructions stored in the memory 204), the at least one memory and computer program code configured to, with the at least one processor, cause the apparatus to at least:
receive, from a map database (¶ 0023: an online map service can geocode each of the one or more point of interest locations. In an embodiment, the online map service determines the latitude and longitude coordinates of the address of the one or more point of interest locations), a representation of a road network comprising road segment data or link data, wherein the road segment data or link data is representative of roads, streets, or paths (¶ 0105: the application server 108 may identify one or more point of interest locations within the pre-defined area based on the map data. At step 610, the application server 108 may receive traffic information between a plurality of road intersections within the pre-defined area);
identify existing electric vehicle “EV” charge points and their respective locations (¶ 0052: the historical demand data includes the number of charge units consumed per hour at each of the plurality of existing replenishment stations in the predefined area. The plurality of existing replenishment stations may transmit the demand data periodically to the database server 102);
determine static map features for locations of the existing EV charge points (¶ 0061: the demand prediction unit 208 may be configured to identify one or more point of interest locations in the predefined area based on a map data of the predefined area), wherein each of the static map features correspond to road network attributes for the road segment data or link data proximate to a corresponding candidate location (¶ 0057: the traffic information may include information about traffic speeds, traffic density, and travel times between the plurality of road intersections within the pre-defined area. In an alternate embodiment, the database server 102 is periodically updated based on the traffic information gathered/received from the one or more sensors installed at a plurality of road intersections…In an embodiment, a positioning system (e.g., a GPS system) in conjunction with one or more sensors may be utilized to determine the traffic information at the plurality of road intersections; e.g., attributes may comprise position of intersections);
determine utilization of the existing EV charge points (¶ 0052: historical demand data);
train a machine learning model on the static map features for the locations of the existing EV charge points and the utilization of the existing EV charge points (¶ 0099: in order to train the demand prediction unit 208 to predict the one or more locations for placement of one or more replenishment stations for one or more vehicles, the demand prediction unit 208 is trained using the training data set and a test data set; ¶ 0100-0101);
provide the trained machine learning model for use in predicting utilization of an EV charge point at a candidate location (¶ 0102: After the training of the demand prediction unit 208 is complete based on the training data set, then the demand prediction unit 208 may be utilized to predict the replenishment demand at a plurality of locations within the pre-defined area. Additionally, in an embodiment, the demand prediction unit 208 may identify the one or more locations from the plurality of locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the plurality of locations and the pre-defined threshold).
TRIPATHI fails to disclose the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location.
HUSAREK discloses the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location (¶ 0073: structural environmental information for Region 3 and/or weather information for Region 3 and/or a number of non-electric vehicles in Region 3 can be collected and taken into account when determining the fifth data. The structural environment information particularly represents the condition of the roads and/or the traffic volume and/or the type of roads and/or the gradients and inclines of the roads in Region 3; ¶ 0076: the computing unit 8 is designed to determine the number of charging stations 2 to be installed, depending on the third, fourth and fifth data points…Thus, the determination of the number of charging stations to be installed is carried out based on a wide variety of extensive information and data. This allows for the most accurate and efficient determination of the number of charging stations to be installed in region 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the functional class for the road segment data or link data of HUSAREK into the apparatus of TRIPATHI to produce an expected result of an apparatus for predicting utilization of a charge point at a candidate location based on functional class for the road segment data or link data. The modification would be obvious because one of ordinary skill in the art would be motivated to allow for the most accurate and efficient determination of the number of charging stations to be installed (HUSAREK, ¶ 0076).
Regarding claim 10, TRIPATHI discloses the apparatus is further configured to: provide a candidate location and static map features associated with the candidate location; and determine, based on the machine learning model using the candidate location and the static map features associated with the candidate location, a predicted utilization of an EV charge point at the candidate location (¶ 0052, 0061, 0099-0101).
Regarding claim 11, TRIPATHI discloses the apparatus is further configured to: determine dynamic features for the locations of the existing EV charge points, wherein causing the apparatus to train the machine learning model on the static map features for the locations of the existing EV charge points and the utilization of the existing EV charge points comprises causing the apparatus to train the machine learning model on the static map features and dynamic features for the locations of the existing EV charge points and the utilization of the existing EV charge points, and input to the machine learning model the candidate location and static map features associated with the candidate location comprises inputting to the machine learning model the candidate location, the static map features associated with the candidate location, and dynamic features associated with the candidate location (¶ 0052, 0061, 0099-0101; ¶ 0047, 0057: dynamic features include traffic information).
Regarding claim 12, TRIPATHI discloses the static map features associated with the candidate location and the dynamic features associated with the candidate location form a candidate location vector, wherein the candidate location vector is input to the machine learning model (¶ 0026-0028, 0030, 0074-0078).
Regarding claim 13, TRIPATHI discloses the static map features of the candidate location further comprise one or more of: point-of-interest (POI) categories proximity to the candidate location, POI categories density relative to the candidate location, or population density proximate the candidate location (¶ 0061).
Regarding claim 14, TRIPATHI discloses the dynamic map features of the candidate location comprise one or more of: traffic density proximate the candidate location, weather proximate the candidate location, population estimates proximate the candidate location, event information, time of day, day of week, or season of year (¶ 0047, 0057).
Regarding claim 15, TRIPATHI discloses the apparatus is further configured to: input to the machine learning model a plurality of candidate locations for EV charge points and static map features associated with the plurality of candidate locations; determine, based on the machine learning model, a predicted utilization of EV charge points at the plurality of candidate locations for EV charge points; and rank the plurality of candidate locations for EV charge points based on their respective predicted utilization of EV charge points at a corresponding location (¶ 0077-0080).
Regarding claim 16, TRIPATHI discloses the apparatus is further configured to: identify a selected location from the plurality of candidate locations for EV charge points based on the ranked plurality of candidate locations for EV charge points; and provide a recommendation for establishment of an EV charge point at the selected location (¶ 0077-0080).
Regarding claim 17, TRIPATHI discloses the apparatus is further configured to: generate a representation of a map encompassing a plurality of candidate locations; and provide for visual distinction of the plurality of candidate locations based on predicted utilization of EV charge points at a corresponding location (¶ 0080).
Regarding claim 18, TRIPATHI discloses a computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code portions stored therein (¶ 0044-0046), the computer-executable program code portions comprising program code instructions configured to:
receive, from a map database (¶ 0023: an online map service can geocode each of the one or more point of interest locations. In an embodiment, the online map service determines the latitude and longitude coordinates of the address of the one or more point of interest locations), a representation of a road network comprising road segment data or link data, wherein the road segment data or link data is representative of roads, streets, or paths (¶ 0105: the application server 108 may identify one or more point of interest locations within the pre-defined area based on the map data. At step 610, the application server 108 may receive traffic information between a plurality of road intersections within the pre-defined area);
receive an indication of a plurality of candidate locations for electric vehicle “EV” charge points (¶ 0077-0079: user may select a plurality of candidate locations);
determine static map features of the plurality of candidate locations (¶ 0061: the demand prediction unit 208 may be configured to identify one or more point of interest locations in the predefined area based on a map data of the predefined area), wherein each of the static map features correspond to road network attributes for the road segment data or link data proximate to a corresponding candidate location (¶ 0057: the traffic information may include information about traffic speeds, traffic density, and travel times between the plurality of road intersections within the pre-defined area. In an alternate embodiment, the database server 102 is periodically updated based on the traffic information gathered/received from the one or more sensors installed at a plurality of road intersections…In an embodiment, a positioning system (e.g., a GPS system) in conjunction with one or more sensors may be utilized to determine the traffic information at the plurality of road intersections; e.g., attributes may comprise position of intersections);
process the plurality of candidate locations and static map features using a machine learning model (¶ 0099: in order to train the demand prediction unit 208 to predict the one or more locations for placement of one or more replenishment stations for one or more vehicles, the demand prediction unit 208 is trained using the training data set and a test data set; ¶ 0100-0101), the machine learning model trained on existing EV charge point locations (¶ 0052: the historical demand data includes the number of charge units consumed per hour at each of the plurality of existing replenishment stations in the predefined area. The plurality of existing replenishment stations may transmit the demand data periodically to the database server 102), existing EV charge point static map features (¶ 0061: see above; ¶ 0062: Based on the location of the one or more point interest locations, the demand prediction unit 208 may be configured to create a fourth data structure; ¶ 0063: after creation of the first data structure, the second data structure, the third data structure, and the fourth data structure, the demand prediction unit 208 may utilize canonical correlation analysis technique to create a first replenishment prediction model P1, a second replenishment prediction model P2, and a third replenishment prediction model P3), and existing EV charge point utilization (¶ 0052: historical demand data); and
determine, based on the machine learning model, a respective predicted utilization of a respective EV charge point at each of the plurality of candidate locations (¶ 0074: the location selected by the user may be depicted using an interactive marker. The user-computing device 104 may be configured to transmit location information (geographical coordinates) associated with the selected location. Based on the received location information and the one or more input parameters, the demand prediction unit 208 may be configured to determine the first demand prediction, the second demand prediction, and the third demand prediction; ¶ 0102: After the training of the demand prediction unit 208 is complete based on the training data set, then the demand prediction unit 208 may be utilized to predict the replenishment demand at a plurality of locations within the pre-defined area. Additionally, in an embodiment, the demand prediction unit 208 may identify the one or more locations from the plurality of locations for placement of the one or more replenishment stations based on the predicted replenishment demand at the plurality of locations and the pre-defined threshold); and
generate a representation of a map including the plurality of candidate locations, wherein candidate locations of the plurality of candidate locations are visually distinguished based on the respective predicted utilization of the respective EV charge point at each of the candidate locations (¶ 0080: the user interface is displayed on the user-computing device 104 that includes a map. In an embodiment, one or more user interactive markers, corresponding to the identified one or more locations, may be displayed on the map. In response to the display of the one or more user interactive markers, an input may be received from a user of the user-computing device 104 on the one or more user interactive markers. In response to the input, the replenishment demand at the identified location may be displayed on a display screen of the user-computing device 104 in a form of one or more graphical representations comprising a bar chart, a pie chart, a heat map, and/or a line chart).
TRIPATHI fails to disclose the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location.
HUSAREK discloses the static map features comprise a functional class for the road segment data or link data proximate the corresponding candidate location (¶ 0073: structural environmental information for Region 3 and/or weather information for Region 3 and/or a number of non-electric vehicles in Region 3 can be collected and taken into account when determining the fifth data. The structural environment information particularly represents the condition of the roads and/or the traffic volume and/or the type of roads and/or the gradients and inclines of the roads in Region 3; ¶ 0076: the computing unit 8 is designed to determine the number of charging stations 2 to be installed, depending on the third, fourth and fifth data points…Thus, the determination of the number of charging stations to be installed is carried out based on a wide variety of extensive information and data. This allows for the most accurate and efficient determination of the number of charging stations to be installed in region 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the functional class for the road segment data or link data of HUSAREK into the computer program product of TRIPATHI to produce an expected result of a computer program product for predicting utilization of a charge point at a candidate location based on functional class for the road segment data or link data. The modification would be obvious because one of ordinary skill in the art would be motivated to allow for the most accurate and efficient determination of the number of charging stations to be installed (HUSAREK, ¶ 0076).
Regarding claim 19, TRIPATHI discloses program code instructions to rank the plurality of candidate locations for EV charge points based on their respective predicted utilization of EV charge points at a corresponding location (¶ 0077-0080).
Regarding claim 20, TRIPATHI discloses candidate locations of the plurality of candidate locations are visually distinguished using one or more highlighting effects to represent the respective predicted utilization of EV charge points at a corresponding location (¶ 0080).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Manuel Hernandez/Examiner, Art Unit 2859 1/14/2026
/TAELOR KIM/Supervisory Patent Examiner, Art Unit 2859
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