DETAILED ACTION
This is the first office action on the merits of the instant application, which was filed November 19, 2024 as a continuation of US 18/214,807, filed June 27, 2023, which was a continuation of US 17/573,914, filed January 12, 2022, which was a continuation of US 16/269,263, filed February 6, 2019. The application contains Claims 1-20.
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 .
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.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The instant application is purported to be a continuation of three previous applications. The term “prospective user” is recited throughout the claims of the instant application, but does not appear in any of the previous applications. The term further does not appear in the specification of the instant application, except where the claims are restated in the Brief Summary section, or in the Abstract.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claims recite, variously, “prospective user” and “potential user”, with modifiers such as “at least one”, “first”, “second”, etc., with no clear indication if there is a distinction between a “prospective user” and a “potential user”. There is no support in the specification to distinguish the two, as “prospective user” is not found in the specification, save for the summary and abstract, and is not found in the preceding applications to which the instant application claims priority.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Lehmann et al. (US 2011/0246404 A1).
Lehmann et al. teaches, according to claims 1, 8 and 15, a computer-implemented method of facilitating a first-mile/last-mile transfer of a vehicle, the computer-implemented method comprising:
analyzing, via one or more processors, a route to determine, respectively, an incentive to be offered to a prospective user (Lehmann et al., at least para. [0118], “According to embodiments of the invention, the potential trip data objects are implemented as multidimensional vectors. Each vector comprises trip related and user-related specifications. Each specification is represented by one dimension of the vector. A vector could for example comprise dimensions representing the place of departure, the starting time, the destination, the maximum price a user is willing to pay as passenger, the minimum expected reward as driver…”; and para. [0136], “The unevenness of driver/passenger distributions in different regional districts, e.g. urban or rural districts, can be reduced e.g. by automatically increasing the price a driver gets for acting as driver. In districts characterized by an excess of drivers, the price a driver receives may be diminished by the trip sharing service. In districts characterized by a shortage of registered users being willing to act as drivers, the price per mile paid to a driver may be increased automatically by the trip sharing service, thereby increasing the motivation for participants of the service to act as drivers.”);
analyzing, via the one or more processors, a plurality of user profiles using a trained machine learning model to determine at least one potential user likely to accept the incentive, wherein the trained machine learning model is trained using at least one of a first historical data set of ride sharing data indicating a price that was paid, profile information about a vehicle operator, or a second historical data set of ride sharing data indicating geographical details of a ride that was given (Lehmann et al., at least para. [0074], “…The global trip history is a trip history comprising the trip history of all registered users of the trip sharing service. Each trip history of a registered user comprises trip data objects representing past trips of said user. The global trip history comprises at least a first trip history of a first user and a second trip history of a second user. For each registered user of the trip sharing service a user-specific trip prediction algorithm is generated based on the trip history of each registered user…”); and
causing, via the one or more processors, a message including the incentive to be displayed to the at least one potential user via an electronic device of the at least one potential user (Lehmann et al., at least para. [0020], “Processing devices comprising one or multiple logical interfaces for application data are operable to receive the current application data and use the received application data as additional input parameter for the trip prediction algorithm. Received application data can be, for example, the starting time, date and place as well as the destination of trip events…”).
Regarding claims 2, 9 and 16, the route is a first route and the prospective user is a first prospective user, the computer-implemented method further comprising: analyzing, via the one or more processors, a second route to determine a cost to be offered to a second prospective user; wherein the ride of the second historical data set of ride sharing data shares geographical details with the second route (Lehmann et al., at least para. [0101], “In the following, processing steps will be described transforming a data object comprising at least the starting time and date, the starting place and the destination of a trip, in the following referred to as `predicted trip data object`, in a potential trip data object. Each potential trip data object is created by a trip sharing service hosted on a type II processing device. Each created potential trip data object is compared in a matching method against other potential trip data objects of other registered users of a trip sharing service and for allocating users of matching potential trip data objects to each other as trip accompanies.”).
Regarding claims 3, 10 and 17, determining the at least one potential user includes: determining, via the one or more processors, a distance between the second prospective user and a potential user associated with the plurality of user profiles; and further determining, via the one or more processors, the at least one potential user based upon the distance (Lehmann et al., at least para. [0140], “…The chances of a second potential passenger starting in the second district are, however, bad, because he will probably live not close enough to most drivers of the first district to be picked up and there exist only few drivers in his own district. The situation changes in case the second user starting from the second district is assigned the driver role: the chances of a second potential passenger starting from within the first district to be picked up are only slightly diminished as many alternative drivers exist in his district. The chances of a second potential driver starting in the second district are, however, significantly increased, because he has higher chances of living close enough to the starting place of the second user to be picked up and there do not exist many alternatives in his district. As a result of reducing the unequal distribution of driver/passenger fractions, the trip sharing service is operable to allocate drivers and passengers to each other highly efficiently.”).
Regarding claims 4, 11 and 18, the determining of the distance includes: minimizing, via the one or more processors, the distance between the second prospective user and the potential user using a graph theoretic algorithm; wherein the at least one potential user includes a user with the minimized distance (Lehmann et al., at least para. [0190], “The steps 800-810 can likewise be executed on the server hosting the trip sharing service. In this case, for each registered user of the trip sharing service a user-specific trip prediction algorithm is created based on the trip history of each registered user…”; para. [0191], “…The matching method is executed as described previously. As a result, the first user is allocated in step 814 to at least one second user corresponding to the best matching potential trip data object as trip accompany.”; and para. [0140], “…As the distance between the starting place of the driver and the pick-up place of a passenger should be as short as possible, it is highly beneficial to assign the driver role preferentially to the second user starting his trip from the second district with low driver fraction…”).
Regarding claims 5, 12 and 19, the method further comprises receiving, via the one or more processors, an acknowledgement of the message from the at least one potential user; and causing, via the one or more processors, a confirmation to be displayed to the at least one potential user (Lehmann et al., at least para. [0120], “…the successful allocation of two users as trip accompanies for a particular trip requires the explicit or automatic acceptance of the respective trip accompany by both users.”; and para. [0020], “Processing devices comprising one or multiple logical interfaces for application data are operable to receive the current application data and use the received application data as additional input parameter for the trip prediction algorithm. Received application data can be, for example, the starting time, date and place as well as the destination of trip events stored to an electronic calendar of a calendar application…”).
Regarding claims 6, 13 and 20, the method further comprises receiving, via the one or more processors, telematics information from the vehicle; and based upon the telematics information, providing, via the one or more processors, the incentive to the at least one potential user (Lehmann et al., at least para. [0173], “…In case the client device 500 comprises an interface 505 to receive vehicle data of the first user's car, the set of input parameters 501 may further comprise vehicle data, e.g. the filling level of the car's gas tank. In case the client device 500 comprises a component for determining its current position, e.g. a GPS antenna, the current place 504 may be determined by the client device 500 and may be used as further input parameter 501.”; and para. [0020], “Processing devices comprising one or multiple logical interfaces for application data are operable to receive the current application data and use the received application data as additional input parameter for the trip prediction algorithm. Received application data can be, for example, the starting time, date and place as well as the destination of trip events stored to an electronic calendar of a calendar application…”).
Regarding claims 7 and 14, the method further comprises receiving, via the one or more processors, telematics information from the vehicle, wherein the trained machine learning model is further trained using the telematics information (Lehmann et al., at least para. [0186], “…the collecting step 801 in addition comprises the step 803 of determining the current position of the processing device, the step 804 of gathering vehicle data, the step 805 of gathering external data and the step 806 of gathering application data. The amount and type of gathered input parameters corresponds to the input parameters stored to the trip data objects of the trip history of the user. Only in case the trip prediction algorithm learned, during training, the correlation of a particular input parameter with a trip chosen by the user, the usage of additional input parameters as input for the trip prediction algorithm will increase the prediction accuracy of said algorithm.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONALD J. WALLACE whose telephone number is
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/DONALD J WALLACE/Primary Examiner, Art Unit 3665