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 .
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/178,076 filed on 03/03/2023.
Claims 1-25 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/03/2023 has been reviewed and entered into the record.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance” (published on 1/7/2019 in Fed. Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the “2019 PEG”).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claimed system (claims 1-10), method (claims 11-18), and non-transitory computer-readable medium (claims 19-25) are directed to potentially eligible categories of subject matter (process, machine, and article of manufacture), and therefore claims 1-25 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea grouping by setting forth limitations for managing personal behavior or relationships or interactions (user-specific/weighted recommendations to aid a user in selecting a charging station), and also recite activities that fall within the “Mental Processes” abstract idea grouping by reciting steps that, but for the generic computer recited in the claim, could be performed in the human mind via observation, evaluation, judgment, and/or opinion With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below:
a storage configured to maintain, for each of a plurality of charging stations, charging station scores indicating ratings of properties of the charging stations, and maintain, for each of a plurality of users, user weights defining a relative weighting of each of the ratings descriptive of user preferences (The maintaining of scores and use weights describes activity for managing personal behavior or interactions because the maintained data may be indicative of past user behavior and/or user preferences [Spec. at pars. 20-21], and but for the generic computer/storage implementation, the maintaining of the scores/weights could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, or with the aid of pen and paper to maintain the scores/weights. In addition, the “maintain” step is insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution data gathering activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)); and
a processor configured to
receive a charger request from a vehicle, the charger request including an identifier of a sender of the charger request and a location of the vehicle (The receiving of a request describes activity for managing personal behavior or interactions because the request may be from a vehicle user/driver/sender attempting to identify a charging station to charge their vehicle, and but for the generic computer implementation, the receiving could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion. This step, when implemented by a computer/processor, may also be considered insignificant extra-solution activity which, for the same reasons as provided in the analysis of the “maintain” steps above [which is adopted here as well], fails to amount to a practical application or add significantly more to the abstract idea);
identify one or more charging stations in proximity to the location of the vehicle, for each identified charging station, compute a user-specific charger score using the plurality of charging station scores for the charging station weighted according to the user weights corresponding to the identifier (The steps for identifying and computing user-specific charge scores are considered activities for managing personal behavior or interactions because they may be based on user/driver/sender behavior and/or preferences pursuant identifying a user-specific charging station to charge their vehicle, and but for the generic computer implementation, the identify and computer steps could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion), and
send a charger recommendation to the vehicle responsive to the charger request, the charger recommendation including, for each of the one or more charging stations, a location of the charging station and the user-specific charger score corresponding to the charging station (The step for sending a recommendation is considered activity for managing personal behavior or interactions because the recommendation and sending thereof may be based on user/driver/sender behavior and/or preferences and directly in support of a user seeking a user-specific charging station to charge their vehicle, and but for the generic computer implementation, the sending of recommendation could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, or with the aid of pen and paper or verbally [e.g., a verbal dispatch instruction to the vehicle operator]. This step, when implemented by a computer/processor, may also be considered insignificant extra-solution output activity which, for the same reasons as provided in the analysis of the “maintain” and “receive” steps above [which is adopted here as well], fails to amount to a practical application or add significantly more to the abstract idea).
Independent claims 11 and 19 recite limitations similar to the limitations discussed above and have been determined to recite the same abstract idea(s) as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d) the judicial exception is not integrated into a practical application. The additional elements recited in independent claims 1, 11, and 19 include a storage, a processor, a non-transitory computer-readable medium. These elements have been fully considered, but are not sufficient to integrate the abstract idea into a practical application because they amount to generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment). See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With respect to the maintain/receive/send activity, in addition to merely being implemented by the generic computer, these activities also fall under insignificant extra-solution activity, which is not enough to amount to a practical application. See MPEP 2106.05(g). Furthermore, these additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements recited in independent claims 1, 11, and 19 include a storage, a processor, a non-transitory computer-readable medium. These additional elements fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Applicant's Specification describes generic and/or off-the-shelf computing devices for implementing the invention covering virtually any computing device under the sun (Spec. at par. [0028], noting for example that “various types of portable computing device, such as cellular phones, tablet computers, smart watches, laptop computers, portable music players, or other devices having processing and communications capabilities”). Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the maintain/receive/send steps, in addition to being implemented by a generic computer, these activities also fall under insignificant extra-solution activity, and such extra-solution activities have been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea, as noted by the CAFC with respect to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. See also, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). See also, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent claims 2-10, 12-18, and 20-25 recite the same abstract idea as recited in the independent claims under the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings, and with the exception of the additional elements addressed below, are directed to further details that merely narrow the abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry along with the same or similar generic computing elements as independent claims 1/11/19 and addressed above (which is incorporated herein), which fail to integrate the abstract idea into a practical application or add significantly more to the claims.
With respect to the additional receive activity in claims 2/12/20, this activity amount to insignificant extra-solution data gathering activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
With respect to the unsupervised clustering in claims 3/13/21, this limitation is recited at a high level of generality and the claims fail to provide any details, technique, or algorithm as to how the unsupervised clustering is performed, and as admitted in the Specification, the unsupervised clustering could be implemented with mathematical algorithms such as k-means clustering (Spec. at par. [0038]), and thus falls under the “Mathematical Concepts” abstract idea grouping, such that “Adding one abstract idea (math) to another abstract idea” (fundamental economic practice) “does not render the claim non-abstract.” See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea and thus subjected to further analysis in part two of the Alice/Mayo test). Nevertheless, even if evaluated as an additional element, the unsupervised clustering is recited at a high level of generality and fails to yield a technical improvement or otherwise add a practical application. See MPEP 2106.05(f) and 2106.05(h). Furthermore, given the high-level of generality and lack of “how” as to its implementation by the claimed invention, the unsupervised clustering, even when implemented by a computer, is similar to merely adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (e.g., generic computing environment), which does not amount to a practical application or significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Furthermore, under Step 2B, it is noted that unsupervised clustering is well-understood, routine and conventional in the art. See, e.g., Sun et al., US 2014/0372351, noting in par. [0038] that “machine learning algorithm used to implement the classifier 304 may include any machine learning algorithm known in the art, including, for example, a supervised or unsupervised learning algorithm.” See also, Ur et al., US 2021/0334812, noting in par. [0095] that “ implementing an unsupervised clustering model as known in the art.”
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to integrate the judicial exception into a practical application and fails to add significantly to the claims beyond the abstract idea itself.
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.
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.
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.
Claims 1, 10-11, and 19 are rejected under 35 U.S.C. 103 as unpatentable over Miller et al. (US 2017/0168493, hereinafter “Miller”) in view of Starns (US 2019/0204097).
Claim 1: Miller teaches a system for customer-centric dynamic charging station assessment (par. 3: vehicle charging method and system includes identifying a charging location that a vehicle user may want to use to charge a vehicle), comprising:
a storage (pars. 35, 52, and Fig. 1: server may be connected to a DataMart, data store, or data warehouse as a repository for server data) configured to
maintain, for each of a plurality of charging stations, charging station scores indicating ratings of properties of the charging stations (pars. 3, 14-15, 52, and 56: charge location rating for identified charge stations; server 126 that has a data store 122 for holding rating data; charge stations having a safety rating; charge stations having a utility rating; rating on which the charge station is ranked), and
maintain, for each of a plurality of users, user weights defining a relative weighting of each of the ratings descriptive of user preferences (pars. 18, 41, 52-53, 61, and Fig. 6: weighted user rating including a trusted third party rating associated with the vehicle user. In an example, the method may include adjusting a desired user rating based on a battery state of charge and identity of the vehicle user; the desired user rating will be weighted to give more weight; user rating, for example, a certain number of stars out of a maximum number (e.g., four out of five stars) or a numerical rating (e.g., a number out of ten). The user rating can be based on any impression of the charging station. The user rating may take into account intangible impressions of the charging location, appearance, helpfulness of others, “feel” of the location, overcrowded, abandoned, quality of the charging experience, and the like; user rating weighting system 801 that compiles a user rating using ratings from a plurality of users and may weight ratings from different users in different ways. Some users may influence the overall user rating more than other users. The user rating of a given charging location is received from a user. A user rating subsystem 803 stores the individual user ratings and overall user ratings. The user rating weighting system 801 includes a verified user weight); and
a processor (pars. 36: system may include many processors; controller or processor would generally include any number of processors, ASICs, ICs, Memory (e.g., Flash, ROM, RAM, EPROM, and/or EEPROM) and software code to co-act with one another to perform a series of operations), configured to
receive a charger request from a vehicle, the charger request including an identifier of a … location of the vehicle (Abstract and pars. 3 and 56: Along with the request, data may be sent identifying vehicle location, route, and desired safety rating; vehicle sends data to a server related to its current location and intended route),
identify one or more charging stations in proximity to the location of the vehicle (pars. 34 and 37: A plurality of proximate charging stations can be generated based on distance from the route and convenience; system may use many different factors in determining proximate charging stations. For instance, the charging station location system may identify the driver's route and location of next expected charge event (usually home, or work)),
for each identified charging station, compute a user-specific charger score using the plurality of charging station scores for the charging station weighted according to the user weights corresponding to the identifier (pars. 8-9, 11, 14, 18, 52-53, and 63: identifying charge stations each having a user rating that exceeds the desired user rating; vehicle user may use the interface module to select third party data to be followed or weighted more heavily in rating and selecting charging locations; identifying charge stations accessible from the route and, for each of the charge stations, a weighted user rating including a trusted third party rating associated with the vehicle user; overall user rating may respond to real-time changes at a charging location by asking for a rating after each charging event. The present systems and methods provide for a user rating system that can take into account such intangibles and other criteria as described herein), and
send a charger recommendation to the vehicle responsive to the charger request, the charger recommendation including, for each of the one or more charging stations, a location of the charging station and the user-specific charger score corresponding to the charging station (pars. 14-15, 56, 60, and Figs. 2-3 and 7: display indicators for the identified charge stations having a safety rating that exceeds the desired safety rating and a user rating that exceeds the desired user rating; filters the charge station user ratings based on the trusted user ratings as previously associated with the vehicle user; At 713, the user ratings may be used to assist a vehicle in determining which charging location to show [i.e., recommend] to a user when a vehicle is in need of a charge, e.g., as described above with regard to FIGS. 1-4. The rating provided may be a particular user's rating or an overall rating. The ratings may be sent through a communication network back to the vehicle; The user ratings can be displayed within the vehicle at a user interface (e.g., the display 600 shown in FIG. 6). The user may request additional information regarding a specific charging location by selecting that specific charging location).
Miller does not explicitly teach the request as including an identifier of a sender of the … request (Examiner’s Note: Miller does teach the request, as discussed above, and teaches employing an identifier for identifying the vehicle user, but does not specifically teach the identifier as being include in the actual request – See, e.g., par. 14: identification of a current user of the vehicle).
Starns teaches a request that includes an identifier of a sender of the … request (pars. 45 and 152: request may include a requestor identifier).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller with Starns because the references are analogous since they are both directed to automated features for managing transportation assets to accommodate customers/users, which is within Applicant’s field of endeavor of managing customer-centric vehicle charging operations, and because modifying Miller to include a identifier or the request sender, as taught by Starns, would be a compatible and beneficial extension of Miller’s existing features for sending requests and for identifying users (Miller, abstract and par. 14), and would help to ensure that a reply to the user request is personalized for a specific identified user’s preferences, ratings, or the like; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 10: Miller further teaches wherein ratings include cleanliness of the charging stations, available points of interests (POIs), competitive price, fast charge availability, and/or well-lit conditions (pars. 35 and 43-44: rating may include: 1. data obtained from the cloud, 2. data obtained from other users (third party data, which can be designated as trusted data by a vehicle user), 3. proximity data indicating the distance between the charge station and other attractions (e.g., malls, stores, restaurants, activity centers, etc.); utility rating could also include the cost of electricity at each particular recharge station).
Claim 11: Miller teaches a method for customer-centric dynamic charging station assessment (par. 3: vehicle charging method and system includes identifying a charging location that a vehicle user may want to use to charge a vehicle), comprising:
receiving a charger request from a vehicle, the charger request including an identifier of a … location of the vehicle (Abstract and pars. 3 and 56: Along with the request, data may be sent identifying vehicle location, route, and desired safety rating; vehicle sends data to a server related to its current location and intended route);
identifying one or more charging stations in proximity to the location of the vehicle (pars. 34 and 37: A plurality of proximate charging stations can be generated based on distance from the route and convenience; system may use many different factors in determining proximate charging stations. For instance, the charging station location system may identify the driver's route and location of next expected charge event (usually home, or work));
for each identified charging station, computing a user-specific charger score using a plurality of charging station scores for the charging station weighted according to the user weights corresponding to the identifier (pars. 8-9, 11, 14, 18, 52-53, and 63: identifying charge stations each having a user rating that exceeds the desired user rating; vehicle user may use the interface module to select third party data to be followed or weighted more heavily in rating and selecting charging locations; identifying charge stations accessible from the route and, for each of the charge stations, a weighted user rating including a trusted third party rating associated with the vehicle user; overall user rating may respond to real-time changes at a charging location by asking for a rating after each charging event. The present systems and methods provide for a user rating system that can take into account such intangibles and other criteria as described herein); and
sending a charger recommendation to the vehicle responsive to the charger request, the charger recommendation including, for each of the one or more charging stations, a location of the charging station and the user-specific charger score corresponding to the charging station (pars. 14-15, 56, 60, and Figs. 2-3 and 7: display indicators for the identified charge stations having a safety rating that exceeds the desired safety rating and a user rating that exceeds the desired user rating; filters the charge station user ratings based on the trusted user ratings as previously associated with the vehicle user; At 713, the user ratings may be used to assist a vehicle in determining which charging location to show [i.e., recommend] to a user when a vehicle is in need of a charge, e.g., as described above with regard to FIGS. 1-4. The rating provided may be a particular user's rating or an overall rating. The ratings may be sent through a communication network back to the vehicle; The user ratings can be displayed within the vehicle at a user interface (e.g., the display 600 shown in FIG. 6). The user may request additional information regarding a specific charging location by selecting that specific charging location).
Miller does not explicitly teach the request as including an identifier of a sender of the … request (Examiner’s Note: Miller does teach the request, as discussed above, and teaches employing an identifier for identifying the vehicle user, but does not specifically teach the identifier as being include in the actual request – See, e.g., par. 14: identification of a current user of the vehicle).
Starns teaches a request that includes an identifier of a sender of the … request (pars. 45 and 152: request may include a requestor identifier).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller with Starns because the references are analogous since they are both directed to automated features for managing transportation assets to accommodate customers/users, which is within Applicant’s field of endeavor of managing customer-centric vehicle charging operations, and because modifying Miller to include a identifier or the request sender, as taught by Starns, would be a compatible and beneficial extension of Miller’s existing features for sending requests and for identifying users (Miller, abstract and par. 14), and would help to ensure that a reply to the user request is personalized for a specific identified user’s preferences, ratings, or the like; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19: Miller teaches a non-transitory computer-readable medium comprising instructions for customer-centric dynamic charging station assessment that, when executed by one or more computing devices, cause the one or more computing devices to perform operations (pars. 3, 36, 61, and claim 1: system may include many processors; Memory (e.g., Flash, ROM, RAM, EPROM, and/or EEPROM) and software code to co-act with one another to perform a series of operation vehicle charging; method and system includes identifying a charging location that a vehicle user may want to use to charge a vehicle; computing device, circuitry, or a server; processor is programmed) including to:
receive a charger request from a vehicle, the charger request including an identifier of a … location of the vehicle (Abstract and pars. 3 and 56: Along with the request, data may be sent identifying vehicle location, route, and desired safety rating; vehicle sends data to a server related to its current location and intended route);
identify one or more charging stations in proximity to the location of the vehicle (pars. 34 and 37: A plurality of proximate charging stations can be generated based on distance from the route and convenience; system may use many different factors in determining proximate charging stations. For instance, the charging station location system may identify the driver's route and location of next expected charge event (usually home, or work));
for each identified charging station, compute a user-specific charger score using a plurality of charging station scores for the charging station weighted according to the user weights corresponding to the identifier (pars. 8-9, 11, 14, 18, 52-53, and 63: identifying charge stations each having a user rating that exceeds the desired user rating; vehicle user may use the interface module to select third party data to be followed or weighted more heavily in rating and selecting charging locations; identifying charge stations accessible from the route and, for each of the charge stations, a weighted user rating including a trusted third party rating associated with the vehicle user; overall user rating may respond to real-time changes at a charging location by asking for a rating after each charging event. The present systems and methods provide for a user rating system that can take into account such intangibles and other criteria as described herein); and
send a charger recommendation to the vehicle responsive to the charger request, the charger recommendation including, for each of the one or more charging stations, a location of the charging station and the user-specific charger score corresponding to the charging station (pars. 14-15, 56, 60, and Figs. 2-3 and 7: display indicators for the identified charge stations having a safety rating that exceeds the desired safety rating and a user rating that exceeds the desired user rating; filters the charge station user ratings based on the trusted user ratings as previously associated with the vehicle user; At 713, the user ratings may be used to assist a vehicle in determining which charging location to show [i.e., recommend] to a user when a vehicle is in need of a charge, e.g., as described above with regard to FIGS. 1-4. The rating provided may be a particular user's rating or an overall rating. The ratings may be sent through a communication network back to the vehicle; The user ratings can be displayed within the vehicle at a user interface (e.g., the display 600 shown in FIG. 6). The user may request additional information regarding a specific charging location by selecting that specific charging location).
Miller does not explicitly teach the request as including an identifier of a sender of the … request (Examiner’s Note: Miller does teach the request, as discussed above, and teaches employing an identifier for identifying the vehicle user, but does not specifically teach the identifier as being include in the actual request – See, e.g., par. 14: identification of a current user of the vehicle).
Starns teaches a request that includes an identifier of a sender of the … request (pars. 45 and 152: request may include a requestor identifier).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller with Starns because the references are analogous since they are both directed to automated features for managing transportation assets to accommodate customers/users, which is within Applicant’s field of endeavor of managing customer-centric vehicle charging operations, and because modifying Miller to include a identifier or the request sender, as taught by Starns, would be a compatible and beneficial extension of Miller’s existing features for sending requests and for identifying users (Miller, abstract and par. 14), and would help to ensure that a reply to the user request is personalized for a specific identified user’s preferences, ratings, or the like; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2-4, 12-14, and 20-22 are rejected under 35 U.S.C. 103 as unpatentable over Miller et al. (US 2017/0168493, hereinafter “Miller”) in view of Starns (US 2019/0204097), as applied to claims 1, 11, and 19 above, and further in view of Telpaz et al. (US 2022/0318859, hereinafter “Telpaz”).
Claims 2/12/20: Miller further teaches wherein the processor is further configured to: receive vehicle data from a plurality of vehicles, the vehicle data being descriptive of charging events of the plurality of vehicles at the plurality of charging stations (par. 52: Additional vehicles 130, 132 may provide information to the rating and charge station location server 126 over the network); aggregate the vehicle data into charger visits (CV) records according to benchmark information included in the vehicle data (pars. 59-61: Compiling user ratings may include performing statistical analysis on the user ratings, e.g., averaging, weighting, or discarding certain ratings; Compiling may also include weighting the user ratings. Weighting may include giving greater effect to certain user's ratings in the overall user rating or in the statistical analysis. Weighting can also go the other way; a certain user's rating may be given less effect in the overall user rating. For example, a verified user with numerous ratings may be given a greater weight in the analysis for their user rating. An unverified user may be given less weight); and determine the user weights … (pars. 18, 53, 59, and 61: may also include weighting the user ratings. Weighting may include giving greater effect to certain user's ratings in the overall user rating or in the statistical analysis. Weighting can also go the other way; For example, a verified user with numerous ratings may be given a greater weight in the analysis for their user rating), but does not teach perform clustering of CV records in view of the ratings of the properties to categorize the vehicle data into user behaviors, and determining…according to the clustering.
Telpaz teaches perform clustering of CV records in view of the ratings of the properties to categorize the vehicle data into user behaviors, and determining…according to the clustering (pars. 44, 50, 59, 62-64, and 67: determines if the charging station 120 is part of a cluster of charging stations; revises the charging station dataset 140 to include the identification of such a cluster (e.g., cluster ID) upon the determination; characteristics can include unique identifiers of vehicles that have been charged at the charging stations in the cluster 410, number of fast-charging stations in the cluster 410, average charging duration at the cluster 410, average charging price at the cluster 410, etc.; one or more clusters 410 are analyzed by a prediction model to determine if each of the clusters 410 is a public charging station or a private charging station; user-behavior model is trained to identify various characteristics of the charging sessions to identify specific preferences of the user 102 when charging the vehicle 110 based on certain spatial and temporal contextual data. The spatial contextual data includes a location of the user 102 from which the user 102 selects a particular charging station 120. The temporal contextual data includes a time of the day, day of the week, etc., at which the user 102 selects a particular charging station 120. The user-behavior model is used to determine a correlation between the spatial and temporal contextual data and the characteristics of the charging station 120 (or cluster 410) that is selected by the user 102. For example, the nearby amenities within a predetermined vicinity of the charging station 120 are ranked to determine the importance of such amenities to the user 102 (e.g., nearby restroom, café, etc.); charging stations that meet such conditions are given more weight when suggesting charging stations to the user 102 in the mornings).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller/Starns with Telpaz because the references are analogous since they are each directed to automated features for managing transportation assets to accommodate customers/users, which is within Applicant’s field of endeavor of managing customer-centric vehicle charging operations, and because modifying Miller/Starns to incorporate the teachings of Telpaz, as claimed, would serve the motivation to analyze and present charging stations for electric vehicles based on users’ charging patterns (Telpaz at par. 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 3/13/21: Miller does not teach the limitation of claims 3/13/21.
However, Telpaz teaches wherein the clustering is performed using one or more unsupervised clustering techniques (par. 47: The clustering algorithm can be a known algorithm such as density-based spatial clustering of applications with noise (DBSCAN) or any other such algorithm [Examiner’s Note: DBSCAN is an unsupervised machine learning algorithm]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Miller/Starns/Telpaz by employing Telpaz’s unsupervised clustering technique, as claimed, in order to serve the motivation to analyze and present charging stations for electric vehicles based on users’ charging patterns (Telpaz at par. 1) without requiring human participation in the clustering and in order to find hidden and/or complex structures, patterns, or anomalies in the data; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 4/14/22: Miller teaches wherein the benchmark information is vehicle [information] (as discussed above in the rejection of parent claims 2/12/20, which is incorporated herein), but does not teach odometer information.
However, Starns teaches odometer information (par. 159: Examples of data transmitted from the autonomous vehicle 160 may include, e.g., telemetry and sensor data, determinations/decisions based on such data, vehicle condition or state (e.g., battery/fuel level, tire and brake conditions, sensor condition, speed, odometer, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Miller/Starns/Telpaz by utilizing Starns’ odometer information as the information for Miller’s benchmark, as claimed, in order to use relevant information tied to a vehicle and/or user (e.g., driver) when evaluating behavior related to vehicle usage (since odometer information is appreciated as a useful metric tied to user/vehicle behavior); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable over the prior art
Claims 5-9, 15-18, and 23-25 are allowable over the prior art. In particular, the prior art of record does not teach or render obvious the set of limitations of: correlate the vehicle data into charge-attempt charging-status (CACS) records descriptive of whether charge attempts defined by the vehicle data were successful or unsuccessful, and whether the charge attempts were single-attempt or multiple-attempt; assign reliability scores to the charging stations based on the CACS records; and update the ratings of properties of the charging stations to include the reliability scores based on the vehicle data, as recited and arranged in claims 5/15/23 when taken in combination with the limitations inherited from their respective parent claims. Claims 6-9, 16-18, and 24-25 depend from claims 5/15/23 and therefore are allowable over the prior art based on inheritance of the subject matter from parent claims 5/15/23. Claims 5-9, 15-18, and 23-25 are not allowed, however, because they stand rejected under 35 USC §101, as discussed above. In addition, even if the §101 rejection of these claims is overcome, dependent claims 5-9, 15-18, and 23-25 would be objected to as dependent upon rejected base claims and would be allowable only if rewritten in independent form including all of the limitations of their base claims and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Allamsetty et al. (US 2024/0211522): discloses a charging station search engine in an electric vehicle management system, including requests that include user preferences (par. 3) and include charging location result scores (par. 38).
Teske (US 2019/0383637): discloses features for displaying charging options for an electric vehicle, including user/vehicle profiles and historical user data (par. 29).
S. Shahriar, A. R. Al-Ali, A. H. Osman, S. Dhou and M. Nijim, "Machine Learning Approaches for EV Charging Behavior: A Review," in IEEE Access, vol. 8, pp. 168980-168993, 2020: discloses techniques for analyzing consumer EV charging behavior using machine learning techniques, including unsupervised learning to find structures and patterns and cluster analysis.
S. P. R. and S. P., "Cloud based Smart EV Charging Station Recommender," 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA, Pune, India, 2022, pp. 1-7: discloses features of a customer-oriented EV charging station recommender.
Y. Zhao, Z. Wang, Y. Man, H. Wen, W. Han and P. Wang, "Intelligent charging recommendation model based on collaborative filtering," 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2021, pp. 7-10: discloses techniques for analyzing a user’s historical behavior data to provide intelligent recommendations for EV charging.
Z. Tian et al., "Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 11, pp. 3098-3109, Nov. 2016: discloses a real-time charging recommendation system for EV taxis based on driver recharging behavior patterns.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
01/29/2026