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 .
Status of Claims
This Office Action is in response to the application filed on August 3, 2022. Claims 1-20 are pending. Claims 1 and 11 are independent.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on November 15, 2022, May 5, 2023 and August 21, 2023 have been considered. The submission is in compliance with the provisions of 37 CFR 1.97. The Forms PTO-1449 are signed and attached hereto.
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.
Claim 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process) and claim 11 is directed to a system. Therefore, claims 1 and 11 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
1. A computer-implemented method comprising:
training a machine learning model to predict at least one visitation metric that reflects visitation of points of interest (POIs) for which visitation metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more points of interest for which visitation metrics are currently available, and visitation metrics for the one or more points of interest;
using the trained machine learning model, determining a particular visitation metric for a particular POI for which visitation metrics are not currently available based on one or more inputs associated with the particular POI.
The examiner submits that the foregoing bolded limitations constitute a “mathematical concept” because under its broadest reasonable interpretation, the claim covers gathering and analyzing data and a “mental process” because under its broadest reasonable interpretation, the claim covers organizing human activity. Specifically, the “training” and using “a machine learning model” is mathematical processing. Further, the “determining a particular popularity metric” is resource allocation and planning. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the nonbolded portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
1. A computer-implemented method comprising:
training a machine learning model to predict at least one popularity metric that reflects popularity of points of interest (POIs) for which popularity metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more POIs for which popularity metrics are currently available, and popularity metrics for the one or more POIs;
using the trained machine learning model, determining a particular popularity metric for a particular POI for which popularity metrics are not currently available based on one or more inputs associated with the particular POI.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations, there is no transformation or reduction of a particular article to a different state or thing. More particularly, there is no recited change to the way the computer, ML model, network or hardware functions. There are no additional elements that apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Dependent claims 2-10 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 1-10 are ineligible under 35 USC §101. Claims 11-20 are ineligible under 35 USC §101 for at least the same reasons of claims 1-10.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4 of copending Application No. 17/822,103 (now U.S. Patent No. 11,867,524). Although the conflicting claims are not identical, they are not patentably distinct from each other because removing inherent and/or unnecessary limitations/step would be within the level of one of ordinary skill in the art.
It is well settled that the omission of an element/limitation, e.g. “popularity metric” and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Omission of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
Present application 17/880,450
Co-pending Application 17/822,103
now U.S. Patent No. 11,867,524
Claim 1:
a training a machine learning model to predict at least one popularity metric that reflects popularity of points of interest (POIs) for which popularity metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more POIs for which popularity metrics are currently available, and popularity metrics for the one or more POIs;
using the trained machine learning model, determining a particular popularity metric for a particular POI for which popularity metrics are not currently available based on one or more inputs associated with the particular POI.
Claim 1:
training a machine learning model to predict at least one visitation metric that reflects visitation of points of interest (POIs) for which visitation metrics are not currently available;
wherein training the machine learning model comprises training the machine learning model using a training dataset comprising:
visitation data for one or more points of interest for which visitation metrics are currently available, and visitation metrics for the one or more points of interest
using the trained machine learning model, determining a particular visitation metric for a particular POI for which visitation metrics are not currently available based on one or more inputs associated with the particular POI
Claim 2:
selecting the particular POI for placement of an electric vehicle charging station (EVCS) based, at least in part, on the particular popularity metric satisfying one or more criteria.
Claim 1:
selecting the particular POI for placement of an electric vehicle charging station (EVCS) based, at least in part, on the particular visitation metric satisfying one or more criteria
Claim 3:
wherein the particular popularity metric comprises a percentage of visit count per multiple segments of a time frame for the particular POI.
Claim 2:
wherein the particular visitation metric comprises a percentage of visit count where a visit duration falls between non-overlapping bounds for the particular point of interest
Claim 4:
wherein the one or more inputs includes one or more of: a road classification of a nearest road, a population density in a surrounding area, and a number of other POIs of the same category as the particular POI within a vicinity.
Claim 3:
wherein the one or more inputs includes one or more of: a road classification of a nearest road, a population density in a surrounding area, and a number of other POIs of the same category as the particular POI within a vicinity.
Claim 5:
generating, based on the particular popularity metric, instructions that specify to deliver charge to electric vehicles over a specific time frame at a specific rate;
transmitting the instructions to the EVCS to cause the EVCS to deliver charge to electric vehicles over the specific time frame at the specific rate.
.
Claim 4:
generating, based on the particular visitation metric, instructions that specify to deliver charge to electric vehicles over a specific time frame at a specific rate;
transmitting the instructions to the EVCS to cause the EVCS to deliver charge to electric vehicles over the specific time frame at the specific rate.
Claim 6:
calculating a deficiency value and a served value for a specific type of charger, wherein the deficiency value and the served value are calculated based on an essential population of electric vehicle drivers in a same category as the particular POI that exist in an area around the particular POI and one or more charging recommendations that indicate an amount of chargers of the specific type that the area around the particular POI can support;
calculating a first partial visitation lift value based on the deficiency value; calculating a second partial visitation lift value based on the served value;
calculating a total visitation lift value for the particular POI based on the first partial visitation lift value and the second partial visitation lift value.
Claim 1:
calculating a deficiency value and a served value for a specific type of charger, wherein the deficiency value and the served value are calculated based on an essential population of electric vehicle drivers in a same category as the particular POI that exist in an area around the particular POI and one or more charging recommendations that indicate an amount of chargers of the specific type that the area around the particular POI can support;
calculating a first partial visitation lift value based on the deficiency value; calculating a second partial visitation lift value based on the served value;
calculating a total visitation lift value for the particular POI based on the first partial visitation lift value and the second partial visitation lift value
Claim 7:
wherein selecting the particular POI for placement of an EVCS is additionally based on the total visitation lift value for the particular POI.
Claim 1:
wherein selecting the particular POI for placement of an EVCS is additionally based on the total visitation lift value for the particular POI.
Claims 11-17 are rejected on the same basis as claims 1-7 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEMETRA R SMITH-STEWART whose telephone number is (571)270-3965. The examiner can normally be reached 10am - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEMETRA R SMITH-STEWART/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661