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 .
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 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.
Status of Claims
Claims 1-13 and 15-20 remain pending, and are rejected.
Claim 21 has been added, and is rejected.
Claim 14 has been cancelled.
Response to Arguments
Applicant’s arguments filed on 11/18/2025 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 11/18/2025 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 11-12 of the Applicant’s Remarks, arguments are made that the claims recite numerous limitations that are not certain methods of organizing human activity, such as the elements of defining an area of interest in response to detecting the trigger event and trigger event attribute set, extracting trigger event data from a set of data environments, generating the trigger event attribute set from trigger event data, determining a location associated with the trigger event, retrieving an underlying geography of the location, selecting a set of perimeter points within a predefined threshold of distance, generating a virtual boundary about the determined location, aggregating historical rental data including a plurality of trigger event attribute sets, training one or more predictive analytics machine learning models, querying a database to identify the rentable unit associated with a rentable unit type, selecting a predictive analytics machine learning model, deploying the model to generate the rental price prediction, and determining terminal entity data comprising contact information of a terminal entity associated with ownership or occupancy of the identified rentable unit. On pages 13-15, the Applicant argues that the claims integrate the judicial exception into a practical application by providing the rental recommendations in a very specific and practical way, including additional elements which improve technical fields such as digital property management and short-term rental provisioning infrastructure.
The Applicant cites to the specification in support of technical improvements, such as greater confidence in the listed rental price being of fair market value, particularly during period of fluctuating rental demand, functioning independently of any manual activity of a terminal entity, informing of rental opportunities in real-time, generating rental price prediction based on analysis of historical rental pricing data and/or market trends. On page 16, arguments are made that the additional elements impose meaningful limits on any alleged judicial exception as they provide a specifically designed process to provide a particular outcome, and do not monopolize any judicial exception. On pages 17-18, it is argued that the claims recite significantly more than any alleged judicial exception, and are not well-understood, conventional, or routine, providing a technical solution to a technical problem, and provide improvements to technical fields.
Examiner respectfully disagrees. The listed limitations argued to not be directed to certain methods of organizing human activity are directed to the process of using sales data and product information to provide the recommendations, and are still directed to certain method of organizing human activity. The “defining an area of interest in response to detecting the trigger event and trigger event attribute set” merely represents determining information of some event to trigger a recommendation based on the event and attributes regarding the event. The “extracting trigger event data from a set of data environments” represents retrieving information of abstract events from some data source. The “generating the trigger event attribute set from trigger event data” only represents organizing or grouping the retrieved data. The “determining a location associated with the trigger event, retrieving an underlying geography of the location, selecting a set of perimeter points within a predefined threshold of distance, generating a virtual boundary about the determined location” merely represents looking up the geographical data of an event and plotting a boundary, as any user could with a map and pen to determine identify points of interest. The “aggregating historical rental data including a plurality of trigger event attribute sets, training one or more predictive analytics machine learning models” represents collecting historical rental data, which is sales data. The training of predictive analytics machine learning models is an additional elements, but only recites generic machine learning activity that is only applied to the abstract idea to provide an output. The “querying a database to identify the rentable unit associated with a rentable unit type, selecting a predictive analytics machine learning model, deploying the model” is merely retrieving information of a product, and the selecting and deploying of a predictive analytic machine learning model to generate the rental price prediction” does not involve any improvements or changes to machine learning technology, but represents the selecting of a tool, and merely applying the tool such that an outcome is received. The machine learning model is only a black box to receive inputs of the abstract idea to provide an output for the abstract idea, and predicting a rental price is a sales and marketing activity. The “determining terminal entity data comprising contact information of a terminal entity associated with ownership or occupancy of the identified rentable unit” also furthers the abstract idea, merely disclosing contact information of the rental unit. These elements are all directed to the abstract process of determining recommendations for rental units, merely accessing and analyzing various information of products, and identifying what to recommend to the user. Any additional element recited are only applied to the abstract idea, and the claims do not recite any improvements or changes to any technology. Furthermore, digital property management and short-term rental provisioning infrastructure only represent managing and organizing information of rental properties, and neither the claims nor the specification recite any particular machinery or data structure in constructing the system.
Greater confidence in the listed rental price being of fair market value, particularly during period of fluctuating rental demand, functioning independently of any manual activity of a terminal entity, informing of rental opportunities in real-time, and generating rental price prediction based on analysis of historical rental pricing data and/or market trends all represent sales and marketing activities. These elements are not part of any technology, and are all elements of providing accurate and relevant information for users looking for a product (rental unit). As discussed above, the claims do not improve or change any technology, and while the claims may provide steps that are not well-understood, convention, or routine, they are only so in context of the abstract idea, and not with any technical elements.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 11/18/2025 with respect to the rejection under 35 U.S.C. 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s amendments necessitated new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15-21 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-8 and 21 are directed to a method, which is a process. Claims 9-13 and 15-16 are directed to an apparatus. Claims 17-20 are directed to a computer program product, which is an article of manufacture. Therefore, claims 1-13 and 15-21 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of providing rental recommendations based on an event:
detecting an occurrence of a trigger event, wherein the trigger event comprises a change in conditions of a geographical region and is associated with a trigger event attribute set, wherein the trigger event attribute set includes a trigger event type and one or more trigger event type attributes;
defining, in response to detecting the trigger event, and based on the trigger event attribute set, an area of interest within a geographical region;
identifying a rentable unit within the area of interest that corresponds to a trigger event type attribute of the one or more trigger event type attributes;
generating, based on the trigger event attribute set, a rental price prediction for the rentable unit;
in an instance in which the price prediction satisfies a predefined rental price threshold:
generating, based on the rental price prediction, a personalized rental recommendation for the rentable unit;
outputting a rental prompt based on the personalized recommendation.
The recited limitations above set forth the process for providing rental recommendations based on an event. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to identifying rentable units in an area of interest by an event to recommend to a user based on a price, which is an advertising and marketing activity. Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
event monitoring engine;
prospect engine;
communications hardware;
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite various engines and communications hardware, the engines are described in the specification as software within the apparatus, which is a generic computer. Specification paragraphs [0041] and [0043] disclose the engines may utilize a processor, memory, or any other hardware component included in the apparatus. Specification paragraphs [0035-0039] discloses the apparatus as merely including a processor and memory, the components being any one or more of a list of generic components. Specification paragraph [0039] also discloses the communications hardware as any means such as a device or circuitry in either hardware or a combination of hardware and software that is configured to receive/transmit data. As such, it is evident that the additional elements are generic computing components that are being leveraged to provide a general link to a computing environment.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 9 (apparatus): Claim 9 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 9 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 17 (computer program product): Claim 17 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 17 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-8, 10-13, 15-16, and 18-21 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of providing rental recommendations based on an event, and do not recite any further additional elements. Thus, each of claims 2-8, 10-13, 15-16, and 18-21 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-8, 10-13, 15-16, and 18-21 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-8, 10-13, 15-16, and 18-21 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged (e.g., a computational engine and a set of data environments (claim 4); a database; a predictive analytics machine learning model from a set of trained predictive analytics machine learning models (claim 5)); however, the additional elements of claims 2-8, 10-13, 15-16, and 18-21 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-8, 10-13, 15-16, and 18-21 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-8, 10-13, 15-16, and 18-21 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-8, 10-13, 15-16, and 18-21 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 9-10, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable by Deak (US 20170358022 A1) in view of Watts (US 20200372556 A1), and in further view of Hartmann (US 20080154655 A1).
Regarding Claim 1: Deak discloses a method comprising:
detecting, by an event monitoring engine, an occurrence of a trigger event, wherein the trigger event is associated with a trigger event attribute set, wherein the trigger event attribute set includes a trigger event type and one or more trigger event type attributes; (Deak: [0040] – “the locational data may be evaluated to identify a set of location points (e.g., longitude/latitude values associated with physical locations). At 404, location point pairings may be generated from the set of location points. A location point pairing may comprise a departure location point and an arrival location point (e.g., the departure location point may occur earlier in time than the arrival location point”; Deak [0041] – “the location point pairings may be evaluated to identify a target location point pairing indicative of air flight travel from a target departure location point (e.g., a location point near a home airport near the user's home in Cleveland) to a target arrival location point (e.g., a location point near a Florida airport)”; Deak [0046] – “a recommendation of content for the destination location may be generated”).
identifying, by a prospect engine, a rentable unit within the area of interest that corresponds to a trigger event type attribute of the one or more trigger event type attributes; (Deak: [0046] – “a recommendation of content for the destination location may be generated. The content may be identified as a social network profile (e.g., a social network profile of a local restaurant), a coupon for a local business, hotel booking functionality (e.g., a link to a hotel booking app), car rental functionality”).
generating, by the prospect engine, a personalized rental recommendation for the rentable unit; (Deak: [0046] – “a recommendation of content for the destination location may be generated. The content may be identified as a social network profile (e.g., a social network profile of a local restaurant), a coupon for a local business, hotel booking functionality (e.g., a link to a hotel booking app), car rental functionality (e.g., a link to a car rental website… The content may be selected based upon whether the user is the personal air flight traveler or the business air flight traveler. For example, vacation content may be identified as the content when the user is the personal air flight traveler (e.g., a recommendation for a local tiki bar, a jungle tour, a monument, etc.). Business trip content may be identified as the content when the user is the business air flight traveler)”).
outputting, by communication hardware, a rental prompt based on the personalized rental recommendation. (Deak: [0049] – “the recommendation may be provided to the user. For example, the recommendation may be provided as a mobile alert, an email, a text message, a social network post or message, through an app or personal assistant functionality, etc. The recommendation may comprise the content or links to the content (e.g., a link to search results, a link to open a hotel booking app, etc.)”).
Deak does not explicitly teach a method comprising:
wherein the trigger event comprises a change in conditions of a geographical region;
defining, by the event monitoring engine, in response to detecting the trigger event, and based on the trigger event attribute set, an area of interest within the geographical region;
generating, by the prospect engine and based on the trigger event attribute set, a rental price prediction for the rentable unit;
in an instance in which the rental price prediction satisfies a predefined rental price threshold;
Notably, however, Deak does disclose identifying a trip and various location point pairings and type of traveler (Deak: [0041]; [0046]).
To that accord, Watts does teach a method comprising:
generating, by the prospect engine and based on the trigger event attribute set, a rental price prediction for the rentable unit; (Watts: [0085] – “service request platform 109 may recommend one or more vehicles for a user selection. In another embodiment, service request platform 109 may automatically select a vehicle from the plurality of vehicle for providing a service type, e.g., a moving-related service. In one example embodiment, service request platform 109 may select a low-priced vehicle, e.g., total rental cost within a user's budget, that is closest to the user location, e.g., current location or pick-up location, and is most suitable for transporting the items. Subsequently, the user is presented with a total cost estimate for the service”).
in an instance in which the rental price prediction satisfies a predefined rental price threshold; (Watts: [0085] – “may select a low-priced vehicle, e.g., total rental cost within a user's budget”).
It would have been obvious to one of the ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Deak disclosing a system for generating recommendations for rentals near a destination location of a user with the price estimate and budget of the user as taught by Watts. One of ordinary skill in the art would have been motivated to do in order to consolidate various aspects of finding a service, such as a receiving a quote, in a less stressful and time-consuming manner (Watts: [0034]).
Deak in view of Watts does not explicitly teach a method comprising:
wherein the trigger event comprises a change in conditions of a geographical region;
defining, by the event monitoring engine, in response to detecting the trigger event, and based on the trigger event attribute set, an area of interest within the geographical region;
Notably, however, Deak does disclose triggering recommendations for a target area that the user has been traveling to (Deak: [0040-0041]), and accessing local weather (Deak: [0048]).
To that accord, Hartmann does teach a method comprising:
wherein the trigger event comprises a change in conditions of a geographical region; (Hartmann: [0053] – “analyze predicted weather conditions corresponding to a selected travel itinerary to identify changes in the predicted weather conditions between the time of purchase and time of use or delivery. For example, the product may include travel to a selected destination (selected, for example, on the basis of sunny and clear predicted weather conditions at the destination) purchased one week in advance. In some instances, the predicted weather condition at the destination may change to cloudy or rainy conditions”).
defining, by the event monitoring engine, in response to detecting the trigger event, and based on the trigger event attribute set, an area of interest within the geographical region; (Hartmann: [0055] – “if the original travel product is a hotel package purchased with the understanding that weather at the selected destination (i.e. the hotel location) would include daytime high temperatures of at least 80 degrees, but later (and before the departure date) the predicted high temperature at the hotel drops to 70 degrees, the system may offer to the user a change in reservation to a second hotel (corresponding to a nearby or completely different destination, for example) having a predicted weather condition substantially equivalent to the selected weather condition”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Deak in view of Watts disclosing a system for generating recommendations for rentals near a destination location of a user with the trigger event comprising a change in conditions of the geographic region and defining an area of interest within the region in response to the trigger event as taught by Hartmann. One of ordinary skill in the art would have been motivated to do so in order to reduce the burden on users and host computing elements of separately searching travel products and weather conditions to various destinations as a selected price point (Hartmann: [0010]).
Regarding Claim 2: Deak in view of Watts and Hartmann discloses the limitations of claim 1 above.
Deak further discloses a method comprising:
extracting, by the event monitoring engine, trigger event data from a set of data environments; (Deak: [0040] – “the locational data may be evaluated to identify a set of location points (e.g., longitude/latitude values associated with physical locations). At 404, location point pairings may be generated from the set of location points. A location point pairing may comprise a departure location point and an arrival location point”; Deak [0041] – “the location point pairings may be evaluated to identify a target location point pairing indicative of air flight travel from a target departure location point”).
generating, by the event monitoring engine, the trigger event attributes set from the trigger event data. (Deak: [0041] – “the location point pairings may be evaluated to identify a target location point pairing indicative of air flight travel from a target departure location point”; Deak: [0059] – “A third mode of transportation (e.g., a train) may be correlated to a third speed value or a third speed range indicative of train travel”). In summary, the target location and travel data is determined, and the mode of travel is determined.
Regarding Claims 9 and 17: Claims 9 and 17 recite substantially similar limitations as claim 1. Therefore, claims 9 and 17 are rejected under the same rationale as claim 1 above.
Regarding Claim 10: Claim 10 recites substantially similar limitations as claim 2. Therefore, claim 10 is rejected under the same rationale as claim 2 above.
Regarding Claim 21: Deak in view of Watts and Hartmann discloses the limitations of claim 1 above.
Deak in view of Watts does not explicitly teach wherein the change in conditions of the geographical regions are detected from at least one data environment, wherein the at least one data environment includes one or more of a live event environment, a news source, a media source, and a weather information source. Notably, however, Deak does disclose triggering recommendations for a target area that the user has been traveling to (Deak: [0040-0041]), and accessing local weather (Deak: [0048]).
To that accord, Hartmann does teach wherein the change in conditions of the geographical regions are detected from at least one data environment, wherein the at least one data environment includes one or more of a live event environment, a news source, a media source, and a weather information source. (Hartmann: [0034] – “The weather prediction systems 15 may also comprise various electronic sources for weather information such as, for example, government weather prediction services (such as, for example, the United States National Weather Service), commercial weather prediction systems (and websites hosted thereby), university and/or research weather prediction systems. Furthermore, as described in further detail herein, the weather prediction systems 15 may be configured to be capable of providing predicted weather conditions for a plurality of destinations, but also observed and/or historical weather conditions for the plurality of destinations”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Deak in view of Watts disclosing a system for generating recommendations for rentals near a destination location of a user with the change in condition of the geographic regions detected from weather information data environments as taught by Hartmann. One of ordinary skill in the art would have been motivated to do so in order to reduce the burden on users and host computing elements of separately searching travel products and weather conditions to various destinations as a selected price point (Hartmann: [0010]).
Claims 3, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), and Hartmann (US 20080154655 A1), in view of Beaurepaire (US 20200242945 A1).
Regarding Claim 3: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
Deak further discloses determining, by the event monitoring engine and based on the trigger event attribute set, a location associated with the trigger event. (Deak: [0041] – “the location point pairings may be evaluated to identify a target location point pairing indicative of air flight travel from a target departure location point”).
The combination does not explicitly teach a method comprising:
retrieving, by the event monitoring engine, an underlying geography of the determined location;
selecting, by the event monitoring engine and based on the underlying geography, a set of perimeter points within a predefined threshold distance from the determined location;
generating, by the event monitoring engine and based on the selected set of perimeter points, a virtual boundary about the determined location.
Notably, however, Deak does disclose identifying recommendation content for the destination location, including car rentals and hotel rentals (Deak: [0046]; [0048]).
To that accord, Beaurepaire does teach a method comprising:
retrieving, by the event monitoring engine, an underlying geography of the determined location; (Beaurepaire: [0088] – “geographic database 109 includes node data records 603, road segment or link data records 605, POI data records 607, shared vehicle data records 609, HD mapping data records 611, and indexes 613, for example. More, fewer or different data records can be provided. In one embodiment, additional data records (not shown) can include cartographic (“carto”) data records, routing data, and maneuver data. In one instance, the additional data records (not shown) can include user mobility pattern data. In one embodiment, the indexes 613 may improve the speed of data retrieval operations in geographic database 109. In one embodiment, the indexes 613 may be used to quickly locate data without having to search every row in geographic database”).
selecting, by the event monitoring engine and based on the underlying geography, a set of perimeter points within a predefined threshold distance from the determined location; (Beaurepaire: [0046] – “defines a geofenced area around the POI to compute or cluster the one or more shared vehicle events that have occurred within a threshold proximity of the POI. By way of example, the geofenced area may be a zone, a perimeter, and/or a boundary set or assigned around or surrounding a central point (e.g., the POI)”).
generating, by the event monitoring engine and based on the selected set of perimeter points, a virtual boundary about the determined location. (Beaurepaire: [0046] – “defines a geofenced area around the POI to compute or cluster the one or more shared vehicle events that have occurred within a threshold proximity of the POI. By way of example, the geofenced area may be a zone, a perimeter, and/or a boundary set or assigned around or surrounding a central point (e.g., the POI). In one instance, the mapping module 203 can contextually define the radius of the geofenced area or the mapping module 201 may adapt the area in situations where a circle is not the most appropriate way of measuring such information. In one embodiment, the number and proximity of shared vehicles to the POI determines the specific shape of the geofence and/or the geofenced area).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the retrieving of the underlying geography, selecting a perimeter, and generating a boundary as taught by Beaurepaire. One of ordinary skill in the art would have been motivated to do so in order to offer shared vehicle (similar to rentals) around a point of interest (Beaurepaire: [0001]).
Regarding Claims 11 and 18: Claims 11 and 18 recite substantially similar limitations as claim 3. Therefore, claims 11 and 18 are rejected under the same rationale as claim 3 above.
Claims 4, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), and Hartmann (US 20080154655 A1), in view of Goyal (US 20230259846 A1).
Regarding Claim 4: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
The combination does not explicitly teach a method comprising:
aggregating, by a computational engine and from a set of data environments, historical rental data including a plurality of trigger event attribute sets comprising a set of rentable units associated with a plurality of rental unit types;
training, by the computational engine and based on the aggregated historical rental data, one or more predictive analytics machine learning models.
Notably, however, Deak does disclose historical travel information and recommendations for a trip (Deak: [0045-0046]).
To that accord, Goyal does teach a method comprising:
aggregating, by a computational engine and from a set of data environments, historical rental data including a plurality of trigger event attribute sets comprising a set of rentable units associated with a plurality of rental unit types; (Goyal: [0030] – “receives historical data relating to queries for services with known booking outcomes, for use as training data for the classification model. In particular, the historical data includes data records associated with booking queries over a time period; Goyal: [0031] – “a table of exemplary historical data records relating to service booking queries for a transport-related service is illustrated. Each data record includes “input data” comprising information contained in the particular “query-offer” message exchange, and “classification data” comprising information from a “booking” or associated message (e.g., fulfilment message) comprising at least an indicator of whether or not the service was booked/fulfilled”).
training, by the computational engine and based on the aggregated historical rental data, one or more predictive analytics machine learning models. (Goyal: [0034] – “the method creates (e.g., builds and trains) a classification model using a machine learning algorithm, the selected input features (e.g., raw and derived features) and the historical data received in step 210 as training data”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the aggregating of historical rental data including trigger event attribute sets and training machine learning models as taught by Goyal. One of ordinary skill in the art would have been motivated to do so in order to forecast demand for bookings in a time period (Goyal: [0003]).
Regarding Claims 12 and 19: Claims 12 and 19 recite substantially similar limitations as claim 4. Therefore, claims 12 and 19 are rejected under the same rationale as claim 4 above.
Claims 5, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), Hartmann (US 20080154655 A1), in view of Xie (US 20140257924 A1).
Regarding Claim 5: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
The combination does not explicitly teach a method comprising:
querying, by the prospect engine and based on the trigger event attribute set, a database to identify the rentable unit, wherein the rentable unit is associated with a rentable unit type;
selecting, by the prospect engine, and based on the rentable unit type and the trigger event attribute set, a predictive analytics machine learning model from a set of trained predictive analytics machine learning models;
deploying, by the prospect engine, the selected predictive analytics machine learning model to generate the rental price prediction for the identified rentable unit.
Notably, however, Deak does disclose providing recommendations for a trip (Deak: [0046]).
To that accord, Xie does teach a method comprising:
querying, by the prospect engine and based on the trigger event attribute set, a database to identify the rentable unit, wherein the rentable unit is associated with a rentable unit type; (Xie: [0070] – “For those data points that are associated with a property by its location (e.g. an average rent amount for specific properties in a zip code) and not per se specific to a particular property, those may all be pre-generated by the derivative characteristics module and placed in a table or other data structure organized by zip code, beds, square footage category, etc.”; Xie: Fig. 5 displays a table of properties by weighed average rent amount and property type).
selecting, by the prospect engine, and based on the rentable unit type and the trigger event attribute set, a predictive analytics machine learning model from a set of trained predictive analytics machine learning models; (Xie: [0049] – “when a user is requesting a rental value prediction for one or more rental properties, a user may be able to rank the various data sources themselves based on their preferences and how much they trust each data source. This ranking may then determine which models may be used to generate the prediction”).
deploying, by the prospect engine, the selected predictive analytics machine learning model to generate the rental price prediction for the identified rentable unit. (Xie: [0049] – “The system may then choose an appropriate model based on that ranking selection made by the user to calculate the rent prediction”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the database of rentable units and unit types, and selecting and deploying of a predictive machine learning model to generate the rental price as taught by Xie. One of ordinary skill in the art would have been motivated to do so in order to quickly generate rent predictions without requiring a qualified professional and strong data density (Xie: [0004-0005]).
Regarding Claims 13 and 20: Claims 13 and 20 recite substantially similar limitations as claim 5. Therefore, claims 13 and 20 are rejected under the same rationale as claim 5 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), and Hartmann (US 20080154655 A1), in view of Conant, II (US 20170330274 A1).
Regarding Claim 6: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
The combination does not explicitly teach determining, by the prospect engine, terminal entity data comprising contact information of a terminal entity associated with ownership or occupancy of the identified rentable unit. Notably, however, Deak does disclose providing recommendations for a trip (Deak: [0046]).
To that accord, Conant, II does teach determining, by the prospect engine, terminal entity data comprising contact information of a terminal entity associated with ownership or occupancy of the identified rentable unit. (Conant, II: [0045] – “store data structures linking product items, and respective owners, rental terms, owner geographic location, owner contact information, and other parameters used in making such a system function as described herein. The actions of the example process illustrated in FIG. 2 begin at block 205, following the establishment of the above data structures for a collection of product items submitted by various users of the system for rental to other users of the system”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the determining of contact information of the entity associated with ownership of the rentable unit as taught by Conant, II. One of ordinary skill in the art would have been motivated to do so in order to contact potential lenders in regard to availability for rental of the product (Contant, II: [0051]).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), and Hartmann (US 20080154655 A1), in view of Conant, II (US 20170330274 A1), and in further view of Hiltch (US 20200211132 A1).
Regarding Claim 7: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
The combination does not explicitly teach a method comprising:
calculating, by the prospect engine and based on terminal entity data, an interest score for a terminal entity;
wherein the interest score is calculated based on (i) financial data of the terminal entity, (ii) data for the identified rentable unit, (iii) digital engagement patterns of the terminal entity,
wherein the interest score is indicative of a likelihood of the terminal entity responding to a rental prompt;
wherein the communications hardware outputs the rental prompt in an instance in which the interest score satisfies a predefined interest score threshold.
Notably, however, Deak does disclose providing recommendations for a trip (Deak: [0046]).
To that accord, Conant, II does teach a method comprising:
calculating, by the prospect engine and based on terminal entity data, an interest score for a terminal entity; (Conant, II – “a software model that may be referred to herein as a “Rental Propensity Model” may be employed to calculating a “Rental Propensity Score,” which may represent a likelihood of a given user or potential renter becoming a renter of a particular product item based on a number of factors including, by way of example and not limitation, their purchase history, and their rental history. For example, the purchase history of a potential lender may show that a potential renter previously purchased the product item of interest to a renter/borrower, even though the potential lender has not submitted that product item to the system as being available for rent. In addition, a potential lender may have purchased or rented other product items or project supplies that are related in use or purpose to the product item of interest to a renter/borrower, increasing the likelihood that the potential renter will, in fact, own the product item of interest to the renter/borrower”).
wherein the interest score is calculated based on (ii) data for the identified rentable unit, (iii) digital engagement patterns of the terminal entity, (Conant, II: [0054] - “Additional factor that may be used in computing a “Rental Propensity Score” in accordance with aspects of the present disclosure include, by way of example but not limitation, the number and identifiers of product categories purchased from a merchant or its business partners, date of the most recent purchase from the merchant, whether the potential renter has a practice of redeeming loyalty reward points, the total dollar volume of product items purchased from the merchant by the potential lender, and demographic factors such as age, gender, occupation, household income, homeowner/renter status, among numerous others. Further factors that may be incorporated in the calculation of the Rental Propensity Score may include, for example, the number of peer-to-peer rental transactions as a lender, the average rating given to the potential lender as a lender, the number of previous responses to rental requests, the number of previous rental request transactions, and a number of other measure derived from the behavior of the potential lender related to peer-to-peer rental”).
wherein the interest score is indicative of a likelihood of the terminal entity responding to a rental prompt; (Conant, II: [0053] – “the purchase history of a potential lender may show that a potential renter previously purchased the product item of interest to a renter/borrower, even though the potential lender has not submitted that product item to the system as being available for rent. In addition, a potential lender may have purchased or rented other product items or project supplies that are related in use or purpose to the product item of interest to a renter/borrower, increasing the likelihood that the potential renter will, in fact, own the product item of interest to the renter/borrower, and agree to rent the product item of interest”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the calculating of a score that is indicative of the entity responding to a rental prompt as taught by Conant, II. One of ordinary skill in the art would have been motivated to do so in order to identify potential lenders so the individual does not need to purchase the item (Contant, II: [0002]).
The combination in view of Conant, II does not explicitly teach a method comprising:
wherein the interest score is calculated based on (i) financial data of the terminal entity;
wherein the communications hardware outputs the rental prompt in an instance in which the interest score satisfies a predefined interest score threshold.
Notably, however, Deak does disclose providing recommendations for a trip (Deak: [0046]).
To that accord, Hiltch does teach a method comprising:
wherein the interest score is calculated based on (i) financial data of the terminal entity; (Hiltch: [0047] – “At S320, the set of data is collected from a plurality of data sources. The set of data may include, for example and without limitation, a type of the owner, an identity of the owner, financial data of the owner, owner's CRE portfolio, performances of the owner's CRE portfolio, average holding duration, owner financial policy, owner's recent completed transactions, owner's public domain knowledge”; Hiltch: [0049] – “At S330, the collected set of data is analyzed. The analysis may be achieved using one or more machine learning technique”; Hiltch: [0050] – “At S340, at least one desirable CRE is determined, along with an owner associated with the at least one desirable CRE, and whether they are likely to sell one or more of the desirable CREs within a predefined time period”; Hiltch: [0051] – “At S350, a probability score that is indicative of the probability that the owner of the one or more desirable CREs is likely to sell at least one desirable CRE, within the predefined time period, is generated for at least one of the CREs of the owner.”).
wherein the communications hardware outputs the rental prompt in an instance in which the interest score satisfies a predefined interest score threshold. (Hiltch: [0036] – “the server 110 may be configured to generate a list of desirable CREs determined to have a probability score that is above a predetermined threshold. The list of desirable CREs may include only CREs the owner is likely to try selling, above a certainty level, within a predefined time period and/or CREs the owner is willing to sell, above a certainty level, within a predefined time period”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the score based on financial data and the score satisfying a predefined threshold as taught by Hiltch. One of ordinary skill in the art would have been motivated to do so in order to predict sales and availability of property investment (Hiltch: [0007]).
Regarding Claim 15: Claim 15 recites substantially similar limitations as claim 7. Therefore, claim 15 is rejected under the same rationale as claim 7 above.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Deak (US 20170358022 A1), Watts (US 20200372556 A1), and Hartmann (US 20080154655 A1), in view of Hammill (US 11,948,214 B1).
Regarding Claim 8: The combination of Deak, Watts, and Hartmann discloses the limitations of claim 1 above.
The combination does not explicitly teach a method comprising:
receiving, by communications hardware, a rental request, wherein the rental request is indicative of an affirmative response to the rental prompt;
generating, by the prospect engine, one or more rental documents for the rentable unit, wherein the one or more rental documents comprise one or more prefilled data fields;
providing, by communications hardware, the one or more rental documents to a rental platform.
Notably, however, Deak does disclose providing recommendations for a trip (Deak: [0046]).
To that accord, Hammill does teach a method comprising:
receiving, by communications hardware, a rental request, wherein the rental request is indicative of an affirmative response to the rental prompt; (Hammill: col. 12, ln. 59-col. 13, ln. 4 – “The owner module 108 sends, at step 406, a notification to the property owners 128 that have a property that fulfills the parameters for a net lease. For example, if the residential property data is within the parameters of the parameter database 122, then the owner module 108 may send a notification, such as an e-mail, automated phone call, notification through the net lease network 102, etc., to the owner. In some aspects, the owner module 108 may send the owner an estimate of a potential net lease agreement. The owner module 108 determines, at step 408, if the owner approves the notification”).
generating, by the prospect engine, one or more rental documents for the rentable unit, wherein the one or more rental documents comprise one or more prefilled data fields; (Hammill: col. 10, ln. 30-36 – “a contractual agreement document may be auto-populated based on the approved net lease terms. Electronic signatures for the contractual agreement document may be received and a digitally-printed and signed version of the contractual agreement document may be generated”).
providing, by communications hardware, the one or more rental documents to a rental platform. (Hammill: col. 10, ln. 49-54 – “net lease module 104 stores, at step 224, the data in the lease database. For example, the net lease module 104 stores the data created from the net lease terms in the lease database 118, such as a property owner ID, the property ID, the length of the lease or the years remaining on the net lease, the lease amount remunerated to the property owner 128, the annual increase of the net lease amount remunerated to the property owner, the fixed and variable costs per month for the property, the rent collected per month for the property, etc.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Deak, Watts, and Hartmann disclosing the system generating recommendations for rentals near a destination location of a user with the rental request indicative of an affirmative response, generating a rental document with prefilled data fields, and providing the document to the platform as taught by Hammill. One of ordinary skill in the art would have been motivated to do so in order to manage leases of rental property between owners and renters (Hammill: col. 1, ln. 32-35).
Regarding Claim 16: Claim 16 recites substantially similar limitations as claim 8. Therefore, claim 16 is rejected under the same rationale as claim 8 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 1/20/2026