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 is a Final Action in response to the claims filed on 03/12/2026.
Claims 1 – 2, 3, 14 – 15, 17, and 20 have been amended;
Claims 1 – 20 are currently pending in this application.
Response to Remarks
Examiner’s Response to Remarks
Rejections under 35 U.S.C. 101;
Rejections under 35 U.S.C. 103
Examiner’s Response to Rejections under 35 U.S.C. 101
Applicant argues the limitations of configuring the two travel keys.
Examiner respectfully disagrees. Applicant’s claim 1 is directed to a statutory category. However, claim 1, recites the abstract idea of mathematical concepts and merely uses a computer as a tool to perform mathematical concepts. The claim recites mathematical calculations because the claim is collecting data and determining travel keys and determining a duration of travel. A mathematical calculation is a mathematical operation or an act of calculating using mathematical methods to determine a variable or number, as we have with claim 1 determining a travel key. Accordingly claim 1 recites an abstract idea.
The judicial exception is not integrated into a practical application; as the additional elements are not significantly more than the judicial exception. The additional elements recited in claim 1, such as a method performed by at least one device including a hardware processor, a software application, one or more non-transitory computer readable media and executed by one or more hardware processors are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Claims 14 and 20 are substantially similar and recite the same subject matter and abstract idea as claim 1; and the dependent claims inherit the same deficiencies as the independent claims. Accordingly, claims 1 – 20, remain rejected under 35 U.S.C. 101.
Examiner’s Response to Rejections under 35 U.S.C. 103.
Applicant argues the amendments to the independent claims are not disclosed by Examiner’s recited art.
Examiner respectfully disagrees. Applicant has amended claims 1, 2, 4, 14 – 15, 17, and 20. Kumar teaches providing instructions and executing travel instructions from a current location to a first delivery location and second delivery determining travel time and travel distance; in addition, a new search and additional art has been added that were necessitated due to amendments. Thus, claims 1 – 20 remain rejected under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. §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 towards an abstract idea without significantly more.
Claims 1, 14, and 20 recites:
obtaining a first set of location elements of a first geographic location associated with a first activity;
determining a first travel key for the first geographic location using a first location element of the first set of location elements and a second location element of the first set of locations elements;
obtaining a second set of location elements of a second geographic location associated with a second activity;
determining a second travel key using a third location element of the second set of location elements and a fourth location element of the second set of location elements;
determining a duration of travel between the first activity and the second activity using the first travel key and the second travel key;
and performing a function using the duration of travel between the first activity and the second activity.
The limitations of claim 1, under its broadest reasonable interpretation, recites the abstract idea of
mathematical concepts but for the recitation of generic computer components (e.g., a method performed by at least one device including a hardware processor and a software application); and merely uses a computer as a tool to perform mathematical concepts. For example, claim 1 is observing a first set of location elements of a first geographic location associated with a first activity; evaluating a first travel key for the first geographic location using a first location element of the first set of location elements and a second location element of the first set of locations elements; observing a second set of location elements of a second geographic location associated with a second activity; evaluating a second travel key using a third location element of the second set of location elements and a fourth location element of the second set of location elements; evaluating a duration of travel between the first activity and the second activity using the first travel key and the second travel key; and evaluating a function using the duration of travel between the first activity and the second activity; and all involve evaluation of data and particularly recites mathematical calculations. Claim 1 recites mathematical calculations because the claim is collecting data and determining travel keys and determining a duration of travel. Applicant Spec. 35, a travel key may be composed of a combination of location elements of a corresponding geographic area, such as a combination of a country identifier (e.g., a country code) and at least a portion of a postal code (e.g., a zip code) of the geographic area. Accordingly, claim 1 recites an abstract idea of mathematical concepts.
Additionally, the claim limitations above further recite obtaining a first set of location elements of a first geographic location associated with a first activity; obtaining a second set of location elements of a second geographic location associated with a second activity; determining a second travel key using a third location element of the second set of location elements and a fourth location element of the second set of location elements; determining a duration of travel between the first activity and the second activity using the first travel key and the second travel key; and performing a function using the duration of travel between the first activity and the second activity. Applicant Spec. 25, further recites “helps businesses schedule, route, and equip mobile workers to complete service activities at a customer’s home, office, or installed asset location.” This falls within the abstract idea grouping of certain methods of organizing human activity, specifically commercial interactions in the form of business relations that fall under the subgrouping of activity between a person such as in the instant claim, a user, and a computer or the activity is between two computers. See MPEP 2106.04(a)(2)(II). Accordingly, claim 1 directed to an abstract idea of certain methods of organizing human activity.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2 and 15, recite observing the evaluating of the first travel key for the first geographic location comprises combining at least a portion of the first location element and at least a portion of the second location element to form the first travel key; and the evaluating of the second travel key for the second geographic location comprises: combining at least a portion of the third location element and at least a portion of the fourth location element to form the second travel key; claims 3 and 16 recite observing wherein: the first location element comprises a first country identifier of the first geographic location; the second location element comprises a first postal code of the first geographic location; the third location element comprises a second country identifier of the second geographic location; and the fourth location element comprises a second postal code of the second geographic location; claims 4 and 17 recite observing the evaluating of the first travel key comprises: selecting a first formula from a plurality of formulas based on a first country identifier of the first geographic location, each formula in the plurality of formulas corresponding to a different country identifier and being configured to generate travel keys; and generating the first travel key using the first formula based on the selecting of the first formula; claim 5 recites observing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code; evaluating that the first quantity does not meet a threshold value; responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code; and evaluating that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value; claim 6 recites observing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that includes a first set of consecutive digits of a postal code, evaluating a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code; evaluating that the first quantity does not meet a threshold value; responsive to the determining that the first quantity does not meet the threshold value, evaluating, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code; evaluating that the second quantity does not meet the threshold value; and responsive to the determining that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key including a city name corresponding to the postal code; claim 7 recites observing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that corresponds to a first geographic area, evaluating a first quantity of the plurality of instances that correspond to distinct geographic locations that are within the first geographic area; evaluating that the first quantity does not meet a threshold value; responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that corresponds to a second geographic area that is larger than and encompasses the first geographic area, a second quantity of the plurality of instances that correspond to distinct geographic locations that are within the second geographic area; and evaluating that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value; claim 8 recites observing accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that corresponds to a first geographic area, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that are within the first geographic area; determining that the first quantity does not meet a threshold value; responsive to the determining that the first quantity does not meet the threshold value, determining, for a second version of the travel key that corresponds to a second geographic area that is larger than and encompasses the first geographic area, a second quantity of the plurality of instances that correspond to distinct geographic locations that are within the second geographic area; determining that the second quantity does not meet the threshold value; and responsive to the determining that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key corresponding to a third geographic area that is larger than and encompasses the second geographic area; claim 9 recites observing the determining the duration of travel between the first activity and the second activity comprises: obtaining, from a database, a set of historical data comprising historical travel durations between the first travel key and the second travel key; and evaluating the duration of travel between the first activity and the second activity based on the historical travel durations between the first travel key and the second travel key; claim 10 recites observing the performing of the function of the software application comprises: evaluating a first particular time slot for the first activity and a second particular time slot for the second activity based on the duration of travel between the first activity and the second activity; and presenting, on a computing device, a recommendation to schedule the first activity for the first particular time slot and the second activity for the second particular time slot; claim 11 recites observing the performing of the function of the software application comprises: evaluating a particular time slot for the second activity based on the duration of travel between the first activity and the second activity; and presenting, on computing device, a recommendation to schedule the second activity for the particular time slot; claim 12 recites observing the software application comprises a field service management (FSM) software application; claim 13 recites observing the software application comprises a field service management (FSM) software application; the evaluating of the first travel key for the first geographic location comprises combining at least a portion of the first location element and at least a portion of the second location element to form the first travel key, the first location element comprises a first country identifier of the first geographic location, the second location element comprises a first postal code of the first geographic location; and the evaluating of the second travel key for the second geographic location comprises combining at least a portion of the third location element and at least a portion of the fourth location element to form the second travel key, the third location element comprises a second country identifier of the second geographic location, the fourth location element comprises a second postal code of the second geographic location; the determining the duration of travel between the first activity and the second activity comprises: observing, from a database, a set of historical data comprising historical travel durations between the first travel key and the second travel key; and computing the duration of travel between the first activity and the second activity based on the historical travel durations between the first travel key and the second travel key; and the performing of the function of the software application comprises: evaluating a particular time slot for the second activity based on the duration of travel between the first activity and the second activity; and presenting, on computing device, a recommendation to schedule the second activity for the particular time slot; claim 18 recites observing the operations further comprise: observing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code; evaluating that the first quantity does not meet a threshold value; responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code; and determining that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value; and claim 19 recites observing the operations further comprise: accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel; for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code; determining that the first quantity does not meet a threshold value; responsive to the determining that the first quantity does not meet the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code; evaluating that the second quantity does not meet the threshold value; and responsive to the evaluating that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key including a city name corresponding to the postal code. Accordingly all of the dependent claims are directed to the abstract idea identified above.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a method performed by at least one device including a hardware processor, performing a function of a software application, optimized for a machine-learning model prediction, and configuring the first travel key includes adjusting a first size associated with the first travel key based on one or more parameters associated with a machine learning model; in addition to reciting the additional elements of claim 1, claim 14 also recites the additional elements of one or more non-transitory computer readable media, executed by one or more hardware processors, and a software application; and in addition to reciting the additional elements of claim 1, claim 20 also recites the additional elements of a system, at least one device, a hardware processor, and system being configured to perform operations, and a software application and all are generic computer components, per Applicant’s Spec. ¶ 30, the term “device” or “processor” shall also be taken as a digital device. The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. Although Applicant has amended claim 1 to recite a machine learning model predictions, these amendments couple with the additional elements of a method performed by at least one device including a hardware processor, a software application, one or more non-transitory computer readable media and executed by one or more hardware processors are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The limitations of claims 14 and 20, substantially recite the same subject matter of claim 1 and also include the abstract ideas identified above. Claim 14 recites the additional elements of one or more non-transitory computer readable media, executed by one or more hardware processors, and a software application; claim 20 recites the additional elements of a system, at least one device, a hardware processor, and system being configured to perform operations, and a software application which are generic computer components as per Applicant’s Specifications shown below:
“[30] In an embodiment, the travel key detection system 100 is implemented on one or more digital devices. The term ?digital device? generally refers to any hardware device that includes a processor. A digital device may refer to a physical device executing an application or a virtual machine. Examples of digital devices include a computer, a tablet, a laptop, a desktop, a netbook, a server, a web server, a network policy server, a proxy server, a generic machine, a function-specific hardware device, a hardware router, a hardware switch, a hardware firewall, a hardware firewall, a hardware network address translator (NAT), a hardware load balancer, a mainframe, a television, a content receiver, a set-top box, a printer, a mobile handset, a smartphone, a personal digital assistant (PDA), a wireless receiver and/or transmitter, a base station, a communication management device, a router, a switch, a controller, an access point, and/or a client device.”
and thus are not practically integrated nor significantly more.
Dependent claims 2 – 13 and 15 – 19, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions using generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 20 are not patent eligible.
Claim Rejections – 35 U.S.C. §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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 1 – 4, 9 – 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Aman et al. (U.S. Patent No. 12,320,648 B1) hereinafter “Kumar” in view of Rubsamen, Roman et al. (EP 2958063 A1) in view of Zhang, Degan et al. (CN 109947098A) hereinafter “Zhang”.
Claims 1, 14, and 20:
Kumar teaches the following:
configuring, for the first geographic location, a first travel key that is optimized for a machine-learning model prediction, wherein the first travel key is determined using a first location element of the first set of location elements and a second location element of the first set of locations elements, wherein configuring the first travel key includes adjusting a first size associated with the first travel key based on one or more parameters; Kumar teaches in claim 3, receive, from a mobile device associated with a user account and executing a delivery application, a second request of the delivery application for a third travel time between a current location of the mobile device and the first item delivery location, wherein the delivery route is assigned to the user account; determine a second haversine distance between the current location and the first item delivery location, a fourth travel time generated by the pedestrian network model, a second travel path between the current location and the first item delivery location, and second data indicating a second geographic feature of a second travel segment along the second travel path; generate a third input to the artificial intelligence model based at least in part on the second haversine distance, the fourth travel time, the second travel path, and the second data; determine the third travel time based at least in part on a second output of the artificial intelligence model in response to the third input; and send a second response to the delivery application, the second response indicating the third travel time. Kumar teaches in col. 3, lines 3 – 23, Conventional techniques may involve using a haversine distance between locations or using a shortest path computed on a pedestrian road network. But, the haversine distance typically results in an underestimated distance, and therefore an underestimated travel time. Alternatively, the shortest path of the pedestrian road network typically results in an overestimated distance since the pedestrian road network does not account for the possibility of a user deviating from the road network (e.g., through a park). Thus, the pedestrian road network typically results in an overestimated travel time. In comparison, the embodiments provide a system capable of accounting for geographic features and freedom of movement, resulting a more accurate travel time estimation. In the case of route and/or resource planning, delivery routes and resources can be planned and used more accurately because of the improved travel time estimation, resulting in an improvement to resource deployment and use. In the additional or alternate case of delivery applications and/or user travel application, a better user experience is provided because of the improved travel time estimation.
obtaining a second set of location elements of a second geographic location associated with a second activity; Kumar teaches in col. 3, lines 53 – 57, determine a location of the second user (e.g., a family member, a friend) and estimate the travel time between the two users, or determine a location of a resource (e.g., a bicycle) and estimate the travel time to the resource;
configuring a second travel key wherein the second travel key is determined using a third location element of the second set of location elements and a fourth location element of the second set of location elements, wherein configuring the second travel key includes adjusting a second size associated with the second travel key; Kumar teaches in claim 3, receive, from a mobile device associated with a user account and executing a delivery application, a second request of the delivery application for a third travel time between a current location of the mobile device and the first item delivery location, wherein the delivery route is assigned to the user account; determine a second haversine distance between the current location and the first item delivery location, a fourth travel time generated by the pedestrian network model, a second travel path between the current location and the first item delivery location, and second data indicating a second geographic feature of a second travel segment along the second travel path; generate a third input to the artificial intelligence model based at least in part on the second haversine distance, the fourth travel time, the second travel path, and the second data; determine the third travel time based at least in part on a second output of the artificial intelligence model in response to the third input; and send a second response to the delivery application, the second response indicating the third travel time;
a duration of travel between the first activity and the second activity using the first travel key and the second travel key; Kumar teaches in col. 5, lines 34 – 41, a planning system, which can determine routes and resources for providing item deliveries to multiple locations can provide location data to a computer system, which is an example of the computer system in Fig. 1. The location data can include location coordinates (e.g., GPS coordinates) of parking locations and item delivery locations where items are to be delivered within a time interval (e.g., day, week, etc.); Kumar further teaches in col. 5, lines 42 – 47, determine a travel path between a first location and a second location, and the travel path can include a travel segment. Upon determining the travel segment, the computer system can access geographic feature data to identify a geographic feature that is associated with the travel segment.
and performing a function of a software application using the duration of travel between the first activity and the second activity; Kumar teaches in col. 2, lines 6 – 20, a computer system can receive a request of an application for a travel time between a first and a second location. The application can be, for instance, a route planning application, a resource planning application, a delivery application, or a user travel application. The computer system determines a travel path between the first location and the second location. The travel path includes a travel segment. The computer system determines data indicating a geographic feature of the travel segment. The geographic feature may be a distance range to travel the travel segment, where this distance range indicates a freedom of traveling the travel segment along many variable ways. The computer system can generate, based on the data indicating the geographic feature, an input to an AI model that is trained to predict travel times
Kumar teaches determining a location of a user, estimating a travel time, route planning, travel planning, and artificial intelligence model, and Rubsamen teaches point of sale, arrival and departure point, and geographic location, and Kumar and Rubsamen are similar where Kumar and Rubsamen teach user traveling from point to point and Rubsamen further teaches the following:
obtaining a first set of location elements of a first geographic location associated with a first activity; Rubsamen teaches in col. 2, lines 51 – 58, and col. 3, lines 1 – 5, systems, methods, and computer program products for dynamically determining availability for one or more travel inventory items based at least in part on one or more types of geographical location data associated with an inventory request. Generally, the one or more types of geographical location data associated with an inventory request may include a point of sale, a point of commencement, a point of sale internet protocol (IP) address, point of departure, a point of arrival, a point of sale global positioning system (GPS) coordinates identifier, and/or other such types of data that may correspond to a geographical location; Rubsamen teaches in col. 3, lines 6 – 13, inventory system may determine the one or more types of geographical location data, where each type of geographical location data identifies a particular geographical location associated with the particular type. The inventory system may select one or more types of the geographical location data to use when computing an availability for the one or more requested travel inventory items; Rubsamen teaches in col. 4, lines 31 – 36, geographical location information stored by the IP geolocation database may include, for example, a region, country identifier, state identifier, city code, postal code, latitude-longitude information, time zone, internet service provider (ISP), and/or a proxy service associated with the IP address.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an artificial intelligence (AI) model-based travel time estimation of Kumar with methods, systems, and computer program products for dynamically computing availability for travel inventory items managed by an inventory system of Rubsamen to assist businesses with determining geographical location information based on point of sale inventory items (Rubsamen Spec. col. 4, lines 17 – 18).
Kumar teaches determining a location of a user, estimating a travel time, route planning, travel planning, and artificial intelligence model; and Rubsamen teaches point of sale, arrival and departure point, and geographic location; and Zhang teaches a machine learning strategy, shortest path algorithm, and search optimization; and Kumar, Rubsamen, are similar where Kumar, Rubsamen, and Zhang teach user traveling from point to point and travel planning; and Zhang further teaches the following:
associated with a machine learning model; Zhang teaches in Pg. 21, ¶ 9, the distance priority optimal route selection method according to claim 1 based on machine learning strategy.
that is optimized for the machine learning model prediction based on the one or more parameters associated with the machine learning model; Zhang teaches in Pg. 4, ¶ 3, distance priority optimal route selection method key step based on machine learning strategy; Zhang teaches in Pg. 7, ¶ 4, 1st, the training of intensified learning priori knowledge, vehicle control device are interacted with known environment, priori knowledge are obtained, by this Secondary training process is denoted as a learning process; Simultaneously by constantly learning, discreet value, parameter are referred in the setting of timing undated parameter It can be most short travel distance, be also possible to minimum running time or integrate-cost minimum as standard, this standard can With by all costs, respectively by weight of different weights carry out, and the regulation of this weight then needs intensified learning constantly learning During carry out repeatedly dynamic adjust, until variation keep within the set threshold range.
predicting, by the machine learning model; Zhang teaches in Pg. 7, ¶¶ 1 – 2, present invention aim to address in intelligent vehicle traveling process path planning and path selection of both ask Topic. The mode that proposed adoption intensified learning technology and the optimization of the way of search of shortest path first combine realizes intelligent driving vehicle The path optimization that heat source considers, designs a kind of optimal path selection side of intensified learning strategy based on priori knowledge Method. This method for optimizing route can effectively help different type intelligent driving vehicle smoothly to plan that there are maximum height limit, width With the optimal path in the transportation network under the conditions of weight and accident and congestion obstacle. The OPABRL intelligent driving vehicle routing choice algorithm that the present invention designs, vehicle is by appropriate priori knowledge and strengthens Learn the mode that Q-Learning algorithm combines, obtains the intensified learning strategy based on priori knowledge, and according to this strategy, Intelligent driving vehicle path planning is realized in circumstances not known. Design solves the problems, such as that this principle is to pass through intelligent body in this algorithm After executing the movement in a behavior aggregate, another state is converted into from a state. And one can be provided simultaneously immediately Return value. The target of intelligent body is exactly to maximize its Total Return value, makes the corresponding selection movement of each state be by study Optimal. Here movement is optimal refer to that this movement executes after, maximum return can be obtained from the point of view of final result Value. The calculating of this return value is the weight that current state is executed to all each prediction return values next acted multiplied by it Then it sums it up. One benefit of nitrification enhancement is exactly, it requires no knowledge about environmental model can more optional movement Expect return value. Another benefit is not have to change, so that it may the problem of handling random transition and return value.
predicted by the machine learning model; Zhang teaches in Pg. 7, ¶¶ 1 – 2, present invention aim to address in intelligent vehicle traveling process path planning and path selection of both ask Topic. The mode that proposed adoption intensified learning technology and the optimization of the way of search of shortest path first combine realizes intelligent driving vehicle The path optimization that heat source considers, designs a kind of optimal path selection side of intensified learning strategy based on priori knowledge Method. This method for optimizing route can effectively help different type intelligent driving vehicle smoothly to plan that there are maximum height limit, width With the optimal path in the transportation network under the conditions of weight and accident and congestion obstacle. The OPABRL intelligent driving vehicle routing choice algorithm that the present invention designs, vehicle is by appropriate priori knowledge and strengthens Learn the mode that Q-Learning algorithm combines, obtains the intensified learning strategy based on priori knowledge, and according to this strategy, Intelligent driving vehicle path planning is realized in circumstances not known. Design solves the problems, such as that this principle is to pass through intelligent body in this algorithm After executing the movement in a behavior aggregate, another state is converted into from a state. And one can be provided simultaneously immediately Return value. The target of intelligent body is exactly to maximize its Total Return value, makes the corresponding selection movement of each state be by study Optimal. Here movement is optimal refer to that this movement executes after, maximum return can be obtained from the point of view of final result Value. The calculating of this return value is the weight that current state is executed to all each prediction return values next acted multiplied by it Then it sums it up. One benefit of nitrification enhancement is exactly, it requires no knowledge about environmental model can more optional movement Expect return value. Another benefit is not have to change, so that it may the problem of handling random transition and return value.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an artificial intelligence (AI) model-based travel time estimation of Kumar and methods, systems, and computer program products for dynamically computing availability for travel inventory items managed by an inventory system of Rubsamen with a distance priority optimal route selection method based on machine learning strategy of Zhang to assist businesses with implementing a system with machine learning models to determine a shortest path (Zhang Spec. Pg. 4, ¶ 4).
Claims 2 and 15:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar teaches determining a location of a user, estimating a travel time, route planning, travel planning, and artificial intelligence model, and Rubsamen teaches point of sale, arrival and departure point, and geographic location; Zhang teaches a machine learning strategy, shortest path algorithm, and search optimization; and Kumar, Rubsamen, and Zhang are similar where Kumar, Rubsamen, and Zhang teach user traveling from point to point and travel planning; and Rubsamen further teaches the following:
the determining of the first travel key for the first geographic location comprises combining at least a portion of the first location element and at least a portion of the second location element to form the first travel key; Rubsamen teaches in col. 5, lines 35 – 41, A point of departure (POD) may be determined based on the inventory request, where the point of departure corresponds to a board point of an earliest travel segment of a journey. If two or more travel segments have the same departure time, then the inventory system may determine the point of departure based on a business rule;
and the determining of the second travel key for the second geographic location comprises: combining at least a portion of the third location element and at least a portion of the fourth location element to form the second travel key; Rubsamen teaches in col. 8, lines 55 – 58, and col. 9 lines 1 – 5, determine availability for a travel inventory item based at least in part on a geographical location determined by the PAL module 138, where the PAL module may be configured to: determine one or more types of geographical location data associated with an inventory request; determine one or more geographical inventory controls associated with the one or more types of geographical location data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an artificial intelligence (AI) model-based travel time estimation of Kumar and a distance priority optimal route selection method based on machine learning strategy of Zhang with methods, systems, and computer program products for dynamically computing availability for travel inventory items managed by an inventory system of Rubsamen to assist businesses with determining geographical location information based on point of sales (Rubsamen Spec. col. 4, lines 17 – 18).
Claims 3 and 16:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Rubsamen further teaches the following:
the first location element comprises a first country identifier of the first geographic location; Rubsamen teaches in col. 10, lines 7 – 17, a first travel inventory item, and the selected types of geographical location data are a point of sale, a point of commencement, and a point of sale IP address, the inventory system may determine one or more geographical inventory controls for the travel inventory item associated with the point of sale, one or more geographical inventory controls for the travel inventory item associated with the point of commencement, and one or more geographical inventory controls for the travel inventory item associated with the point of sale IP address;
the second location element comprises a first postal code of the first geographic location; Rubsamen teaches in col. 4, line 34, postal code;
the third location element comprises a second country identifier of the second geographic location; Rubsamen teaches in col. 4, line 33, country identifier;
and the fourth location element comprises a second postal code of the second geographic location; Rubsamen teaches in col. 4, line 34 postal code; Rubsamen further teaches in col. 4, lines 38 – 43, the inventory system may determine a POSIP associated with a received inventory request, and the inventory system may determine whether to use the POSIP in determining an availability based at least in part on data stored in the IP geolocation database corresponding to the POSIP.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an artificial intelligence (AI) model-based travel time estimation of Kumar and a distance priority optimal route selection method based on machine learning strategy of Zhang with methods, systems, and computer program products for dynamically computing availability for travel inventory items managed by an inventory system of Rubsamen to assist businesses with determining geographical location information based on point of sales (Rubsamen Spec. col. 4, lines 17 – 18).
Claims 4 and 17:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein the determining of the first travel key comprises: selecting a first formula from a plurality of formulas based on a first country identifier of the first geographic location, each formula in the plurality of formulas corresponding to a different country identifier and being configured to generate travel keys; Kumar teaches in col. 7, lines 23 – 67, haversine model and pedestrian network model;
and generating the first travel key using the first formula based on the selecting of the first formula; Kumar teaches in col. 7, lines 40 – 43, The pedestrian network model data can include a travel time between the first location and the second location predicted by a pedestrian network model. Additionally, the pedestrian network model data can include a total distance of pedestrian network model-predicted travel segments of a travel path between the first location and the second location, and types of the pedestrian network model-predicted travel segments. The pedestrian network model can use a shortest path algorithm that snaps the locations to the arcs in a connected road network graph, and calculates the shortest path between the snapped positions.
Claim 9:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein the determining the duration of travel between the first activity and the second activity comprises: obtaining, from a database, a set of historical data comprising historical travel durations between the first travel key and the second travel key; Kumar teaches in col. 8, lines 22 – 28, referring to the training data collected based on a past delivery of a user, this training data includes an actual travel time (e.g., time between consecutive deliveries, which can be determined based on delivery scan events in a vehicle stop) and an actual travel path associated with a previous travel between locations (which can be determined based on GPS data) of the past delivery.
and computing the duration of travel between the first activity and the second activity based on the historical travel durations between the first travel key and the second travel key; Kumar teaches in col. 8, lines 33 – 41, during training, the training data is provided to the AI model and a loss function is minimized (e.g., via a gradient descent backpropagation algorithm). The loss function can include, for an actual travel path, a difference between an actual travel time of this travel path and a predicted travel time. The training refines the parameters of the ML model to minimize this difference such that the ML model can estimate the predicted travel time as accurately as possible.
Claim 10:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein the performing of the function of the software application comprises: determining a first particular time slot for the first activity and a second particular time slot for the second activity based on the duration of travel between the first activity and the second activity; Kumar teaches in col. 2, lines 6 – 15, a computer system can receive a request of an application for a travel time between a first and a second location. The application can be, for instance, a route planning application, a resource planning application, a delivery application, or a user travel application. The computer system determines a travel path between the first location and the second location. The travel path includes a travel segment. The computer system determines data indicating a geographic feature of the travel segment.
and presenting, on a computing device, a recommendation to schedule the first activity for the first particular time slot and the second activity for the second particular time slot; Kumar teaches in col. 10, lines 1 – 11, the delivery route data can be sent to the user device from a route planning system that executes a route planning application (e.g., the computer system executing the route planning application). This system can determine the travel time by requesting its estimation (e.g., via an application programming interface (API) call) from a travel time service (e.g., the travel time service) that then sends respond data indicating the travel time between each pair of two connected locations. Further, the response data can indicate the travel path (in case a deviation from the original travel path is determined) between each pair.
Claim 11:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein the performing of the function of the software application comprises: determining a particular time slot for the second activity based on the duration of travel between the first activity and the second activity; Kumar teaches in col. 8, lines 54 – 55, A travel time between the A1 and A2 may be based on the types of the travel segments.
and presenting, on computing device, a recommendation to schedule the second activity for the particular time slot; Kumar teaches in col. 2, lines 26 – 29, the computer system can determine the travel time based on an output of the AI model that indicates the travel time. The computer system sends a response indicating the travel time to the application for presentation at a user interface. Kumar teaches in col. 9, lines 31 – 36, the route planning application and/or the travel time service can communicate with a delivery application via an application programming interface (API). The delivery application can be executed on a user device associated with a user account for delivering the items to the locations.
Claim 12:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein the software application comprises a field service management (FSM) software application; Kumar teaches in col. 14, lines 4 – 7, the computer system generates a resource plan. The resource plan can include an allocation of drivers and delivery blocks for delivery routes within a reasonable distance based on the total travel times for the delivery routes.
Claim 13:
Kumar, Rubsamen, and Zhang teach claims 1, 14, and 20. Kumar further teaches the following:
wherein: the software application comprises a field service management (FSM) software application; Kumar teaches in col. 14, lines 4 – 7, the computer system generates a resource plan. The resource plan can include an allocation of drivers and delivery blocks for delivery routes within a reasonable distance based on the total travel times for the delivery routes.
the determining the duration of travel between the first activity and the second activity comprises: obtaining, from a database, a set of historical data comprising historical travel durations between the first travel key and the second travel key; Kumar teaches in col. 8, lines 22 – 28, referring to the training data collected based on a past delivery of a user, this training data includes an actual travel time (e.g., time between consecutive deliveries, which can be determined based on delivery scan events in a vehicle stop) and an actual travel path associated with a previous travel between locations (which can be determined based on GPS data) of the past delivery.
and computing the duration of travel between the first activity and the second activity based on the historical travel durations between the first travel key and the second travel key; Kumar teaches in col. 8, lines 33 – 41, during training, the training data is provided to the AI model and a loss function is minimized (e.g., via a gradient descent backpropagation algorithm). The loss function can include, for an actual travel path, a difference between an actual travel time of this travel path and a predicted travel time. The training refines the parameters of the ML model to minimize this difference such that the ML model can estimate the predicted travel time as accurately as possible.
and the performing of the function of the software application comprises: determining a particular time slot for the second activity based on the duration of travel between the first activity and the second activity; Kumar teaches in col. 8, lines 54 – 55, A travel time between the A1 and A2 may be based on the types of the travel segments.
and presenting, on computing device, a recommendation to schedule the second activity for the particular time slot; Kumar teaches in col. 2, lines 26 – 29, the computer system can determine the travel time based on an output of the AI model that indicates the travel time. The computer system sends a response indicating the travel time to the application for presentation at a user interface. Kumar teaches in col. 9, lines 31 – 36, the route planning application and/or the travel time service can communicate with a delivery application via an application programming interface (API). Kumar teaches in col. 9, lines 31 – 36, the route planning application and/or the travel time service can communicate with a delivery application via an application programming interface (API). The delivery application can be executed on a user device associated with a user account for delivering the items to the locations.
Kumar teaches determining a location of a user, estimating a travel time, route planning, travel planning, and artificial intelligence model; and Rubsamen teaches point of sale, arrival and departure point, and geographic location; and Zhang teaches a machine learning strategy, shortest path algorithm, and search optimization; and Kumar, Rubsamen, and Zhang are similar where Kumar, Rubsamen, and Zhang teach user traveling from point to point and travel planning; and Rubsamen further teaches the following:
the determining of the first travel key for the first geographic location comprises combining at least a portion of the first location element and at least a portion of the second location element to form the first travel key, the first location element comprises a first country identifier of the first geographic location, the second location element comprises a first postal code of the first geographic location; Rubsamen teaches in col. 5, lines 35 – 41, A point of departure (POD) may be determined based on the inventory request, where the point of departure corresponds to a board point of an earliest travel segment of a journey. If two or more travel segments have the same departure time, then the inventory system may determine the point of departure based on a business rule; above in claim 1, Rubsamen teaches in col. 4, lines 31 – 36, geographical location information stored by the IP geolocation database may include, for example, a region, country identifier, state identifier, city code, postal code, latitude-longitude information, time zone, internet service provider (ISP), and/or a proxy service associated with the IP address;
and the determining of the second travel key for the second geographic location comprises combining at least a portion of the third location element and at least a portion of the fourth location element to form the second travel key, the third location element comprises a second country identifier of the second geographic location, the fourth location element comprises a second postal code of the second geographic location; Rubsamen teaches in col. 8, lines 55 – 58, and col. 9 lines 1 – 5, determine availability for a travel inventory item based at least in part on a geographical location determined by the PAL module 138, where the PAL module may be configured to: determine one or more types of geographical location data associated with an inventory request; determine one or more geographical inventory controls associated with the one or more types of geographical location data;
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an artificial intelligence (AI) model-based travel time estimation of Kumar and a distance priority optimal route selection method based on machine learning strategy of Zhang with methods, systems, and computer program products for dynamically computing availability for travel inventory items managed by an inventory system of Rubsamen to assist businesses with determining geographical location information based on point of sales (Rubsamen Spec. col. 4, lines 17 – 18).
Subject Matter Overcoming Art of Record
7. The most closely applicable prior art of record is referred to herein as Kumar, Rubsamen, Zhang, Burrell, and Ji as applied above.
Claim 5:
accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel;
for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code;
and determining that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value.
Claim 6:
accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel;
for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity does not meet the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code;
determining that the second quantity does not meet the threshold value;
and responsive to the determining that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key including a city name corresponding to the postal code.
Claim 7:
accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel.
for a first version of a travel key that corresponds to a first geographic area, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that are within the first geographic area;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that corresponds to a second geographic area that is larger than and encompasses the first geographic area, a second quantity of the plurality of instances that correspond to distinct geographic locations that are within the second geographic area;
and determining that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value.
Claim 8:
accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel.
for a first version of a travel key that corresponds to a first geographic area, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that are within the first geographic area;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity does not meet the threshold value, determining, for a second version of the travel key that corresponds to a second geographic area that is larger than and encompasses the first geographic area, a second quantity of the plurality of instances that correspond to distinct geographic locations that are within the second geographic area;
determining that the second quantity does not meet the threshold value;
and responsive to the determining that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key corresponding to a third geographic area that is larger than and encompasses the second geographic area.
Claim 18:
wherein the operations further comprise: accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel;
for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity is less than the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code;
and determining that the second quantity meets the threshold value, wherein the second version of the travel key is used as the first travel key based on the determining that the second quantity meets the threshold value
Claim 19:
wherein the operations further comprise: accessing historical data stored in a database, the historical data comprising a plurality of instances of travel between geographic locations, each instance of travel in the plurality of instances of travel comprising a corresponding duration of travel;
for a first version of a travel key that includes a first set of consecutive digits of a postal code, determining a first quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the first set of consecutive digits of the postal code;
determining that the first quantity does not meet a threshold value;
responsive to the determining that the first quantity does not meet the threshold value, determining, for a second version of the travel key that includes a subset of the first set of consecutive digits of the postal code, a second quantity of the plurality of instances that correspond to distinct geographic locations that have corresponding postal codes that include the subset of the first set of consecutive digits of the postal code;
determining that the second quantity does not meet the threshold value;
and responsive to the determining that the second quantity does not meet the threshold value, using a third version of the travel key as the first travel key based on the determining that the second quantity does not meet the threshold value, the third version of the travel key including a city name corresponding to the postal code.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Kasioumis, Theodoros et al. (US 2022/0026228 A1) discloses A computer-implemented method of predicting energy use for a route including inputting map data of roads included in K trips in a geographical area, predictors of rate of energy use along the roads, and energy consumption data of the K trips.
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
06/12/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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