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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 10/29/2025. Claims 1-3 and 5-20 have been examined in this office action. As of the date of this communication, no Information Disclosure Statement (IDS) has been filed on behalf of this case.
Response to Amendment
Applicant’s amendments to claims 1-3, 5, 8-9, 17, 19-20 are acknowledged. Applicant’s cancellation of claim 4 is acknowledged.
Response to Arguments
On Remarks page 10, Applicant argues the 112 a/ 101 rejection are overcome by Applicant’s amendments. The Examiner respectfully disagrees as the Examiner has updated the rejections below to reflect Applicant’s amendments.
On Remarks pages 11-13, Applicant argues the prior art rejection based on Applicant’s amendments. The Examiner has carefully considered Applicant’s arguments however the Examiner respectfully disagrees. Dubois et al. teaches providing content to a user based on machine learning models that rank, e.g. provide a certain number of the results back based on relevance, quality, and distance. Dubois et al. further teaches in these machine learning models the system provides top N results back based on the predicted likelihood of booking based on a model that is trained based on historical booking data (See paragraphs 0082 and 0092). Here the predicted renter score and meeting the threshold would be the top N results provided back to the user based on the machine learning model from all the different results and user inputs, as defined in paragraph 0092.
Dubois et al. does not expressly teach the commonly known element of retraining the machine learning algorithm. However, as detailed in the prior art rejection below Xia which in the same field of booking rentals and from the same assignee as Dubois et al teaches retraining the machine learning algorithm for likelihood of booking conversion to have a model that will automatically change to compensate for seasonal and or other time specific market shifts (see column 8 lines 43- column 9 line 2).
Therefore the Examiner respectfully disagrees.
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 1-3 and 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3 and 5-18 recite a process as the claims recite a method. Claim 19 recite an article of manufacture as the claims recite a non-transitory computer readable medium being executed by a processing device. Claim 20 recites a machine as the claims recite a system with a memory and a processing device performing functions.
The claim(s) 1-3 and 5-20 recite(s) the idea of an intermediary or third party that provides services related to renting property between owners and renters. The claims recite observations, evaluations, judgements and or opinions that can be performed in the human mind or with aid of pen and paper and accordingly the claims recite a mental process.
Further the claims recite subject matter related to the managing personal behavior or relationships or interactions between people which include social activities and accordingly the claims recite a certain method of organizing human activities.
Mental processes and certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Specifically as recited in the claims:
The Examiner has underlined and bolded the additional elements for distinction. Limitations not bolded and underlined are considered part of the abstract idea.
1. A method comprising:
identifying property data associated with a plurality of properties;
identifying user data associated with one or more users;
determining, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score, for a subset of the plurality of properties, wherein the determining that the one or more users have the renter score that meets the corresponding threshold renter score comprises:
providing at least one of the user data or the property data as data input to a trained machine learning model;
receiving output from the trained machine learning model, the output being associated with predictive data; and
determining based on the predictive data, that a predicated renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties;
and causing a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users;
identifying a current renter score of the one or more users; and
re-training the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties.
2. The method of claim 1, wherein the property data property image data captured by an imaging device.
3. The method of claim 1, wherein: the user data comprises one or more of financial data, employer data, historical rental data, or payment history data; and the identifying of the user data comprises retrieving, via an application programming interface (API), the user data from one or more sources.
5. The method of claim 4 further comprising:
identifying at least one of historical property data associated with historical properties or historical user data associated with historical users;
identifying historical score data comprising at least one of historical renter score data of the historical users or historical threshold renter score data of the historical properties;
and training a machine learning model using data input comprising the historical user data and target output comprising the historical score data to generate the trained machine learning model.
6. The method of claim 1, wherein the one or more users comprise one or more of:a user; a group of users; or the user and a guarantor, wherein the user data further comprises: guarantor data associated with a relationship between the user and the guarantor; and percentage of rent responsible by the guarantor.
7. The method of claim 1, wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of a virtual reality tour of each of the subset of the plurality of properties.
8. The method of claim 1, wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of interaction data of a property of the subset of the plurality of properties, the interaction data comprising one or more of: quantity of users looking at the property; quantity of applications submitted for the property; quantity of favorites associated with the property; or reviews of the property, owner, or landlord.
9. The method of claim 1 further comprising calculating a first rent amount of a first property of the plurality of properties by comparing first property data of the first property to corresponding property data of one or more properties of the plurality of properties, wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of the first rent amount of the first property.
10. The method of claim 1 further comprising: receiving user input selecting a property of the subset of the plurality of properties; prepopulating, based on at least a portion of the user data of the one or more users, a digital application; and transmitting the digital application to an administrative device for approval of the digital application.
11. The method of claim 1 further comprising: receiving user input selecting a property of the subset of the plurality of properties; and dynamically authorizing the one or more users to rent the property without an application.
12. The method of claim 1 further comprising: automatically retrieving payment from a financial account of the one or more users; and automatically creating an executed digital lease and renting the property to the one or more users without transmitting the user data to an administrator device associated with the property.
13. The method of claim 1, wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of a rent amount and a bid option associated with a first property of the subset, wherein the method further comprises: displaying one or more of: a quantity of bids associated with the first property and a current bid amount; or a range of current bids; receiving, from the user device, bid data associated with the bid option; responsive to the bid data meeting the rent amount or a predetermined amount of time passing after the receiving of the bid data, selecting the bid data; and transmitting, to the user device, acceptance data.
14. The method of claim 1 further comprising, responsive to determining that the user device is within a predetermined distance of a digital lock device associated with a property, causing the digital lock device to unlock.
15. The method of claim 14, wherein the digital lock device is associated with access to one or more of the property, an amenity, or a door, the method further comprising determining that the access is to be accessible by the user device based on rent level associated with the user device.
16. The method of claim 1 further comprising: receiving a transfer request to transfer a property from a first user to a second user; and responsive to determining that a second renter score of the second user meets a threshold renter score for the property, causing the property to be transferred from the first user to the second user.
17. The method of claim 1 wherein the property data comprises image data associated with a property of the plurality of properties;
The property data and the user data are provided as the input to the trained machine learning model to receive the output, the trained machine learning model being trained based on data input comprising historical property data and historical user data and target output comprising historical renter score data
18. The method of claim 1 further comprising: authenticating, via a secondary account of the user, a user to enter a primary account associated with the plurality of properties; extracting user information associated with the user from the secondary account; and verifying, based on the user information extracted from the secondary account, at least a portion of the user data entered by the user.
19. A non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising: identifying property data associated with a plurality of properties; identifying user data associated with one or more users; determining, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score for a subset of the plurality of properties, wherein the determining that the one or more users have a renter score that meets the corresponding threshold renter score comprises: providing at least one of the user data or the property data as data input to a trained machine learning model; receiving output from the trained machine learning model, the output being associated with predictive data; and determining based on the predictive data, that a predicted renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties; and causing a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users;
Identifying a current renter score of the one or more scores; and
Retraining the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties
20. A system comprising: memory; and a processing device coupled to the memory, wherein the processing device is to: identify property data associated with a plurality of properties; identify user data associated with one or more users; determine, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score for a subset of the plurality of properties, and wherein to determine that the one or more users have the renter score that meets the corresponding threshold renter score, the processing device is :
Provide at least one of the user data or the property data as data input to a trained machine learning model;
Receive output from the trained machine learning model, the output being associated with predictive data; and
Determine, based on the predictive data, that a predicted renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties;
and cause a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users;
identify a current renter score of the one or more users; and
re-train the trained machine learning model based on input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties.
As per claim 1, the claims recite limitations a human or humans could perform. Specifically a human could take various constraints and determine properties that a user is prequalified for based on those constraints and display those to the user. The additional element that this information is displayed via a “user device” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite it being performed via a “user device” merely results in generally linking it to the field of computers.
Further as for Applicant’s amendments filed 10/29/2025 these recite mental process or human activity steps, specifically inputting data into a set of rules, e.g. model (that is set up based on previous data) to make a prediction like a score, where the score is compared to make a threshold to make a determination and then updating the set of rules, model over time based on feedback or past history. These are therefore part of the abstract idea. The additional elements that the model is a “trained machine learning model” and that the model is updated over time by “re-training ”merely results in apply it. Here there are no details about a particular trained or retrained machine learning model or how the machine learning model operates to derive the information other than it is trained or retained. The trained or retrained machine learning model is used to generally apply the abstract idea without placing any limitation on how the trained or retrained machine learning model operates to derive the information. In addition, the limitation only recites the idea of using a trained or retrained machine learning model without details on how this is accomplished. The claim omits any details as to how the trained or retrained machine learning model solves a technical problem and instead recites only the idea of solution or outcome. The claim invokes a generic trained machine learning model or retrained machine learning model as a tool for making the recited mathematical calculation rather than purporting to improve the technology or computer. Further this can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the field of computers. (additionally see USPTO example 48).
As per claim 2, the claims recite limitations a human or humans could perform. Specifically a human could collect various property information to provide a listing of a property for rent. The additional element that this information image data like a picture of the rental is captured by “an imaging device” like a camera merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite a picture of the rental is captured by “an imaging device” merely results in generally linking it to the field of computers.
As per claim 3, the claims recite limitations a human or humans could perform. Specifically a human could collect renter information to determine which rentals are appropriate for the renter. The additional element that this information is captured by an application programming interface (API) merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite this information is captured by an application programming interface (API) merely results in generally linking it to the field of computers.
As per claim 5, the claims recite limitations a human or humans could perform. Specifically a human could develop a model or equation based on historical data. The additional elements that this model is a “trained machine learning model” merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
As per claim 6, the claims recite limitations a human or humans could perform. Specifically the claims merely describe the different types of users in the rental relationship like a guarantor and how the rental rate is broken down between the different types of renters. There are no additional elements beyond those previously discussed.
As per claim 7, the claims recite limitations a human or humans could perform. Specifically a human or humans could display to a renter the different determined properties of interest. The additional element that the display or tour of the property rather than being text or pictures for example is “a virtual reality tour” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans by for example text or pictures or taking a user on a tour is instead recited as being “a virtual reality tour” merely results in generally linking it to the field of computers.
As per claim 8, the claims recite limitations a human or humans could perform. Specifically displaying statistics related to a specific property that tell a renter of interest in a property like heavy touring etc. or reviews of the property. There are no additional elements beyond those previously discussed above.
As per claim 9, the claims recite limitations a human or humans could perform. Specifically determining a rental amount based on other properties and displaying that information to a user. There are no additional elements beyond those previously discussed above.
As per claim 10, the claims recite limitations a human or humans could perform. Specifically filling out an application for oneself or another and transmitting it to another for approval. The additional element that this information is “digital” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite this information is “digital” merely results in generally linking it to the field of computers.
As per claim 11, the claims recite limitations a human or humans could perform. Specifically receiving a user selecting a property and authorizing a user to rent a property without an application. There are no additional elements beyond those previously discussed above.
As per claim 12, the claims recite limitations a human or humans could perform. Specifically a third party receiving payment and authorizing a transaction between or two parties like a rental lease. The additional element that this lease is “digital”, information is performed “automatically”, and users communicate with a third party via a “device” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite this lease is “digital”, information is performed “automatically”, and users communicate with a third party via a “device” merely results in generally linking it to the field of computers.
As per claim 13, the claims recite limitations a human or humans could perform. Specifically bidding for an item of interest like a property, performing the bid auction, and providing information regarding those that won the bid or auction. The additional element that users communicate with a third party via a “device” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite users communicate with a third party via a “device” merely results in generally linking it to the field of computers.
As per claim 14, the claims recite limitations a human or humans could perform. Specifically a human or humans could unlock a property for a renter when a renter arrives at a property or pass the keys to the renter. The additional elements that this is performed by a “digital lock device” and a “user device” merely results in apply it. Specifically the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing he result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it.” The limitations provide only a result oriented solution and lack details as to how the computer perform the modifications which is equivalent to the words apply it.
Further these limitations that could be performed by a human or humans that instead recite being performed by a “digital lock device” and a “user device” merely generally links the judicial exception to the field of computers.
As per claim 15, the claims recite limitations a human or humans could perform. Specifically a human or humans could unlock a property for a renter when a renter arrives at a property or pass the keys to the renter where the user could unlock the property, amenity, or door. Further a human or human could unlock the property based on a rent level of the user, like the user paid for the rental. The additional element that the limitation that could be performed by a human or humans are instead being recited as being performed by a “digital lock device” and “user device” results in apply it or generally linking it to the field of computers as discussed above in claim 14.
As per claim 16, the claims recite limitations a human or humans could perform. Specifically a human or humans transfer properties between individuals based on scores. There are no additional elements beyond those previously discussed above.
As per claim 17, the claims recite mental process and or certain methods of organzing human activities, in that the claims recite looking at images and user data to determine a score based on models, e.g. rules that use different historical data. The additional element that this is being performed by a “trained machine learning model” merely results in apply it or generally linking it to the field of computers, as detailed above with respect to claim 1.
As per claim 18, the claims recite limitations a human or humans could perform. Specifically a human or humans could authenticate a user for an account based on another account and verify information based on the other account. Further a user could extract information from an account and verify based on extracted information data entered by the user. There are no additional elements beyond those previously discussed above.
As per claim 19, the claims recite limitations a human or humans could perform. Specifically a human could take various constraints and determine properties that a user is prequalified for based on those constraints and display those to the user. The additional element that this information is displayed via a “user device” and the limitations are being performed by “a non-transitory machine-readable storage medium storing instructions which when executed cause a processing device to perform operations comprising:” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite it being performed via a “user device” and the limitations are being performed by “a non-transitory machine-readable storage medium storing instructions which when executed cause a processing device to perform operations comprising:” merely results in generally linking it to the field of computers.
Further as for Applicant’s amendments filed 10/29/2025 that claims recite mental process or human activity steps, specifically inputting data into a set of rules, e.g. model (that is set up based on previous data) to make a prediction like a score, where the score is compared to make a threshold to make a determination and updating the set of rules, model over time based on feedback or past history. These are therefore part of the abstract idea. The additional elements that the model is a “trained machine learning model” and that the model is updated over time by “re-training ”merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
As per claim 20 the claims recite limitations a human or humans could perform. Specifically a human could take various constraints and determine properties that a user is prequalified for based on those constraints and display those to the user. The additional element that this information is displayed via a “user device” and the limitations are being performed by “a system comprising: memory; and a processing device coupled to the memory, wherein the processing device is to” merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to the abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a human or humans that merely recite it being performed via a “user device” and the limitations are being performed by “a system comprising: memory; and a processing device coupled to the memory, wherein the processing device is to” merely results in generally linking it to the field of computers.
Further as for Applicant’s amendments filed 10/29/2025 the claims recite mental process or human activity steps, specifically inputting data into a set of rules, e.g. model (that is set up based on previous data) to make a prediction like a score, where the score is compared to make a threshold to make a determination and updating the set of rules, model over time based on feedback or past history. These are therefore part of the abstract idea. The additional elements that the model is a “trained machine learning model” and that the model is updated over time by “re-train ”of collected information merely results in apply it or generally linking it to the field of computers as discussed above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above under the practical application step.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 2 and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 2, Applicant recites wherein the property data comprises property image data captured by an imaging device
As per claim 17, Applicant recites wherein the property data comprises image data associated with a property of the plurality of properties; and the property data and the user data are provided as the input to the trained machine learning model to receive the output, the trained machine learning model being trained based on data input comprising historical property data and historical user data and target output comprising historical renter score data.
See MPEP 2161.01 Computer Programming, Computer Implemented Inventions, and 35 U.S.C. 112(a) or Pre-AIA 35 U.S.C. 112, First Paragraph [R-07.2022] (cited herein):
The Federal Circuit has explained that a specification cannot always support expansive claim language and satisfy the requirements of 35 U.S.C. 112 "merely by clearly describing one embodiment of the thing claimed." LizardTech v. Earth Resource Mapping, Inc., 424 F.3d 1336, 1346, 76 USPQ2d 1731, 1733 (Fed. Cir. 2005). The issue is whether a person skilled in the art would understand the inventor to have invented, and been in possession of, the invention as broadly claimed. In LizardTech, claims to a generic method of making a seamless discrete wavelet transformation (DWT) were held invalid under 35 U.S.C. 112, first paragraph, because the specification taught only one particular method for making a seamless DWT and there was no evidence that the specification contemplated a more generic method. "[T]he description of one method for creating a seamless DWT does not entitle the inventor . . . to claim any and all means for achieving that objective." LizardTech, 424 F.3d at 1346, 76 USPQ2d at 1733.
Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
The level of detail required to satisfy the written description requirement varies depending on the nature and scope of the claims and on the complexity and predictability of the relevant technology. Ariad, 598 F.3d at 1351, 94 USPQ2d at 1172; Capon v. Eshhar, 418 F.3d 1349, 1357-58, 76 USPQ2d 1078, 1083-84 (Fed. Cir. 2005). Computer-implemented inventions are often disclosed and claimed in terms of their functionality. For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. The critical inquiry is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682. 114 USPQ2d 1349, 1356 (citing Ariad Pharm., Inc. V. Eli Lilly & Co, 598 F.3d 1336, 1351, 94 USPQ2d 1161, 1172 (Fed. Cir. 2010) in the context of determining possession of a claimed means of accessing disparate databases).
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made. For more information regarding the written description requirement, see MPEP § 2162- § 2163.07(b).
Here the Examiner looks to the specification and finds the following support for the claimed limitation:
Paragraph 00104: In some embodiments, the processing logic is further to: receive a transfer request to transfer a property from a first user to a second user; and responsive to determining that a second renter score of the second user meets a threshold renter score for the property, cause the property to be transferred from the first user to the second user.
[00105] In some embodiments, the processing logic is further to: receive image data associated with a property of the plurality of properties; and dynamically determine, based on the image data, at least one of a rent amount for the property or a threshold renter score for the property.
As per claim 2, Applicant amends the claim to recite the property data comprises property image data captured by an imaging device. Further Applicant amends claim 1 to recite that the property image data (which is defined in claim 2 as image data) is (in the alternative) input into a machine learning model to receive predictive data and determine a user predicted renter score. This amended limitation is not found in the specification as filed with respect to image data. Rather Applicant merely recites image data with respect to a threshold renter score for the property which is used as a comparison to the amended “predicted renter score” in the claim as amended.
Even if it was determined that the “threshold renter score” mapped to the “predicted renter score” which the Examiner does not contend based on the above, Applicant’s specification does not disclose the algorithm, for how to get from the inputted image data to the resulting score. Rather Applicant merely recites a broad class of computer implementation, e.g. by a computer, through the generic recitation of a trained machine model (additionally see Applicant’s specification at paragraph 0033), rather than "a finite sequence of steps for solving a logical or mathematical problem or performing a task" (see MPEP 2161.01). Here Applicant merely recites inputs (images) and outputs (a score) but not how this machine learning model (e.g. computer implementation) gets from the inputs to the outputs, e.g. a finite sequence of steps for solving a logical or mathematical problem or performing a task
Therefore one of ordinary skill in the art would not understand how the inventor intended the function to be performed. Therefore based on the MPEP recited sections above the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention.
As per claim 17,
Applicant amends claim 1 to recite that the property data (which is defined in claim 17 as image data) is input into a machine learning model to receive predictive data and determine a user predicted renter score. This amended limitation is not found in the specification as filed with respect to image data. Rather Applicant merely recites image data with respect to a threshold renter score for the property which is used as a comparison to the amended “predicted renter score” in claim 1, from which claim 17 that defines the property data as image data depends.
Even if it was determined that the “threshold renter score” mapped to the “predicted renter score” which the Examiner does not contend based on the above, Applicant’s specification does not disclose the algorithm, for how to get from the inputted image data to the resulting score. Rather Applicant merely recites a broad class of computer implementation, e.g. by a computer, through the recitation of a generic trained machine model (additionally see Applicant’s specification at paragraph 0033), rather than "a finite sequence of steps for solving a logical or mathematical problem or performing a task" (see MPEP 2161.01). Here Applicant merely recites inputs (images) and outputs (a score) but now how this machine learning (e.g. computer implementation) gets from the inputs to the outputs, e.g. a finite sequence of steps for solving a logical or mathematical problem or performing a task
Therefore one of ordinary skill in the art would not understand how the inventor intended the function to be performed. Therefore based on the MPEP recited sections above the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention.
Therefore claims 2 and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claim(s) 1-3,5, 9, 17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable by Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796).
As per claim 1, Dubois et al. teaches A method comprising: (see abstract and paragraphs 0022 and 0039, Examiner’s note: method for a listing system for connecting buyers and sellers).
identifying property data associated with a plurality of properties; (see paragraphs 0039, 0026, 0046, and Figure 4, Examiner’s note: teaches a posting user posting information regarding a property listing to the system).
identifying user data associated with one or more users; (see paragraphs 0081 and 0089, Examiner’s note: users inputting searching requirements or filters for rentals).
Determining, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score for a subset of the plurality of properties, wherein the determining that the one or more users have the renter score that meets the corresponding threshold renter score comprises: providing at least one of the user data or the property data as data input to a trained machine learning model; receiving output from the trained machine learning model, the output being associated with predictive data; and determining, based on the predictive data, that a predictive renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties; (see paragraphs 0082, 0092, and 0104, Examiner’s note: display engine ranks the candidate listings by balancing relevance, quality and distance using machine learning model, and further teaches perform a first pass ranking to select a top N results based on a model that predicts likelihood of booking based on trained historical booking data. It is noted that only one of user data or property data is required by the claim as amended).
Causing a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users (see paragraphs 0030, 0050, 0092-0093, and Figure 7B, Examiner’s note: teaches providing results from the machine learning to the user on the display (see paragraph 0092-0093), teaches the user may use a device like a smartphone (see paragraphs 0030), further teaches displaying that information on a screen (see paragraph 0050 and Figure 7B)).
Dubois et al. does not expressly teach retraining a model over time or more specifically as recited in the claims identifying a current renter score of the one or more users; and re-training the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties.
However, Xia et al. which is also in the art of booking rentals (see column 2 lines 45-60) and from the same assignee as Dubois et al. teaches identifying a current renter score of the one or more users; and re-training the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties (see column 8 lines 43- column 9 line 2, Examiner’s note: retraining with conversion information like booking data from booking within a set number of days prior to the calculation to have a model that will automatically change to compensate for seasonal and or other time specific market shifts).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. with the aforementioned teachings from Xia et al. with the motivation of providing a way to update a model that will automatically change to compensate for seasonal and or other time specific market shifts over time (see Xia et al. column 8 lines 43- column 9 line 2), when a model that takes into consideration historical booking data to make a prediction or likelihood of conversion booking is known (see Dubois et al. paragraphs 0092 and 0092).
As per claim 2, Dubois et al. teaches
wherein the property data comprises one or more of property image data captured by an imaging device (see paragraphs 0026-0027, 0030, 0032, and 0095, Examiner’s note: teaches providing image data to a user in a search result (see paragraphs 0026-0027 and 0095), and the user device can be a camera (see paragraphs 0030 and 0032). It is noted that only one of “property data and user data” is required to be provided to the machine learning as amended in claim 1).
As per claim 3, Dubois et al. teaches
wherein: the user data comprises one or more of financial data, employer data, historical rental data, or payment history data; (see paragraphs 0082 and 0092, Examiner’s note: only one of required by the claims and historical booking data could read on historical rental data or payment history data as broadly recited in the claims).
and the identifying of the user data comprises retrieving, via an application programming interface (API), the user data from one or more sources (see paragraphs 0033, 0035, 0043, 0114, and Figure 3, Examiner’s note: teaches information is provided via APIs).
As per claim 5, Dubois et al. teaches
further comprising: identifying at least one of historical property data associated with historical properties or historical user data associated with historical users; identifying historical score data comprising at least one of historical renter score data of the historical users or historical threshold renter score data of the historical properties; and training a machine learning model using data input comprising the historical user data and target output comprising the historical score data to generate the trained machine learning model. (see paragraph 0092-0093, Examiner’s note: using machine learning to provide results where the machine learning is trained on historical booking data).
As per claim 9, Dubois et al. teaches
further comprising calculating a first rent amount of a first property of the plurality of properties by comparing first property data of the first property to corresponding property data of one or more properties of the plurality of properties, wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of the first rent amount of the first property (see Figure 4 and paragraphs 0024, 0073, 0101, and 0104, Examiner’s note; teaches displaying the weekly price like 965 a week (see Figure 4). Further teaches information is ranked and displayed based on price (see paragraph 0024, 0073, 0101, and 0104)
As per claim 17, Dubois et al teaches
Wherein the property data comprises image data associated with a property of the plurality of properties; and (see paragraphs 0026-0027, 0030, 0032, and 0095, Examiner’s note: teaches providing image data to a user in a search result (see paragraphs 0026-0027 and 0095), and the user device can be a camera (see paragraphs 0030 and 0032).
And the user data are provided as the input to the trained machine learning model to receive the output, the trained machine learning model being trained based on data input comprising historical property data and historical suer data and target output comprising historical renter score data (see paragraph 0082, 0092, 0104, Examiner’s note: display engine ranks the candidate listings by balancing relevance, quality and distance using machine learning model, and further teaches perform a first pass ranking to select a top N results based on a model that predicts likelihood of booking based on trained historical booking data).
Dubois does not expressly teach inputting image data into the machine learning model to generate a score or more specifically as recited in the claims and the property data
However, Xia et al. which is also in the art of booking rentals (see column 2 lines 45-60) and from the same assignee as Dubois teaches inputting image data into the machine learning model to generate a score or more specifically as recited in the claims and the property data (see column 6 lines 58-62, column 14 lines 50-65, column 16 lines 45-55, Examiner’s note: engagement information includes images and providing properties with listing with similar features).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. with the aforementioned teachings from Xia et al. with the motivation of using another known user interest element to track and provide targeted listings to based on expected engagement (see Xia et al. column 6 lines 58-62, column 14 lines 50-65, column 16 lines 45-55), when providing targeted information based on expected engagement from tracked data is known (see Dubois et al. paragraphs 0082 and 0092).
As per claim 19, Dubois et al teaches A non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising: (see paragraph 0022, 0126, and claim 15, Examiner’s note: machine readable medium being executed by a machine).
identifying property data associated with a plurality of properties; (see paragraph 0039, 0026, 0046, and Figure 4, Examiner’s note: teaches a posting user posting information regarding a property listing to the system).
identifying user data associated with one or more users; (see paragraphs 0081 and 0089, Examiner’s note: users inputting searching requirements or filters for rentals).
Determining, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score for a subset of the plurality of properties, wherein the determining that the one or more users have the renter score that meets the corresponding threshold renter score comprises: providing at least one of the user data or the property data as data input to a trained machine learning model; receiving output from the trained machine learning model, the output being associated with predictive data; and determining, based on the predictive data, that a predictive renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties; (see paragraphs 0082, 0092, and 0104, Examiner’s note: display engine ranks the candidate listings by balancing relevance, quality and distance using machine learning model, and further teaches perform a first pass ranking to select a top N results based on a model that predicts likelihood of booking based on trained historical booking data. It is noted that only one of user data or property data is required by the claim).
Causing a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users (see paragraph 0030, 0050, 0092-0093, and Figure 7B, Examiner’s note: teaches providing results from the machine learning to the user on the display (see paragraph 0092-0093), teaches the user may use a device like a smartphone (see paragraphs 0030), further teaches displaying that information on a screen (see paragraph 0050 and Figure 7B)).
Dubois et al. does not expressly teach retraining a model over time or more specifically as recited in the claims identifying a current renter score of the one or more users; and re-training the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties.
However, Xia et al. which is also in the art of booking rentals (see column 2 lines 45-60) and from the same assignee as Dubois et al. teaches identifying a current renter score of the one or more users; and re-training the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties (see column 8 lines 43- column 9 line 2, Examiner’s note: training with conversion information like booking data from booking within a set number of days prior to the calculation to have a model that will automatically change to compensate for seasonal and or other time specific market shifts).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. with the aforementioned teachings from Xia et al. with the motivation of providing a way to update a model that will automatically change to compensate for seasonal and or other time specific market shifts over time (see Xia et al. column 8 lines 43- column 9 line 2), when a model that takes into consideration historical booking data to make a prediction or likelihood of conversion booking is known (see Dubois et al. paragraphs 0092 and 0092).
As per claim 20, Dubois et al teaches A system comprising: (see paragraph 0022, Examiner’s note: system).
memory; and a processing device coupled to the memory, wherein the processing device is to: (see paragraph 0022, 0126, and claim 15, Examiner’s note: machine readable medium being executed by a machine).
identify property data associated with a plurality of properties; (see paragraph 0039, 0026, 0046, and Figure 4, Examiner’s note: teaches a posting user posting information regarding a property listing to the system).
identify user data associated with one or more users; (see paragraphs 0081 and 0089, Examiner’s note: users inputting searching requirements or filters for rentals).
Determine, based on the property data and the user data, that the one or more users have a renter score that meets a corresponding threshold renter score for a subset of the plurality of properties, wherein the determining that the one or more users have the renter score that meets the corresponding threshold renter score comprises: provide at least one of the user data or the property data as data input to a trained machine learning model; receive output from the trained machine learning model, the output being associated with predictive data; and determine, based on the predictive data, that a predictive renter score associated with the one or more users meets the corresponding threshold renter score of each of the subset of the plurality of properties; (see paragraphs 0082, 0092, and 0104, Examiner’s note: display engine ranks the candidate listings by balancing relevance, quality and distance using machine learning model, and further teaches perform a first pass ranking to select a top N results based on a model that predicts likelihood of booking based on trained historical booking data. It is noted that only one of user data or property data is required by the claims).
Cause a graphical representation of the subset of the plurality of properties to be displayed via a user device to the one or more users (see paragraph 0030, 0050, 0092-0093, and Figure 7B, Examiner’s note: teaches providing results from the machine learning to the user on the display (see paragraph 0092-0093), teaches the user may use a device like a smartphone (see paragraphs 0030), further teaches displaying that information on a screen (see paragraph 0050 and Figure 7B)).
Dubois et al. does not expressly teach retraining a model over time or more specifically as recited in the claims identify a current renter score of the one or more users; and re-train the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties.
However, Xia et al. which is also in the art of booking rentals (see column 2 lines 45-60) and from the same assignee teaches identify a current renter score of the one or more users; and re-train the trained machine learning model based on data input comprising the at least one of the user data or the property data and target output comprising the current renter score to generate an updated trained machine learning model configured to determine whether users are prequalified to rent one or more properties (see column 8 lines 43- column 9 line 2, Examiner’s note: training with conversion information like booking data from booking within a set number of days prior to the calculation to have a model that will automatically change to compensate for seasonal and or other time specific market shifts).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. with the aforementioned teachings from Xia et al. with the motivation of providing a way to update a model that will automatically change to compensate for seasonal and or other time specific market shifts over time (see Xia et al. column 8 lines 43- column 9 line 2), when a model that takes into consideration historical booking data to make a prediction or likelihood of conversion booking is known (see Dubois et al. paragraphs 0092 and 0092).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Experian (“What is a Guarantor an Apartment and Do I need one ”) Internet Archive Way Back Machine Capture Date of 8/24/2021.
As per claim 6, Dubois et al. teaches
wherein the one or more users comprise one or more of: a user; a group of users; or the user and a guarantor, (see paragraph 0043, Examiner’s note: teaches a user, note only one is required by the claims).
Dubois in view of Xia et al. does not expressly teach wherein the user data further comprises: guarantor data associated with a relationship between the user and the guarantor; and percentage of rent responsible by the guarantor.
However, Experian teaches wherein the user data further comprises: guarantor data associated with a relationship between the user and the guarantor; and percentage of rent responsible by the guarantor (see pages 2-3, Examiner’s note: guarantor is responsible for the rent and any other charges incurred during the lease term that a tenant cannot pay. Guarantor can be a parent, or a guarantor service where you pay 4% to 10% of the of the annual rent to).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois in view of Xia et al. with the aforementioned teachings from Experian with the motivation of automating a manual function to accomplish the same result of using a commonly known type of person who guarantees the person renting the property gets paid (see Experian pages 2-3), when a user renting a property is known (see Dubois paragraph 0043).
Examiner additionally notes here for Applicant’s convenience MPEP 2114 (cited herein):
When determining whether a computer-implemented functional claim would have been obvious, examiners should note that broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) ("Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years."); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Lo (United States Patent Application Publication Number: US 2021/0072879).
As per claim 7, Dubois et al. teaches
wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of a tour of each of the subset of the plurality of properties (see Figures 3-4 and paragraph 0046, Examiner’s note: displaying multiple listings and detailed information about a listing).
Dubois in view of Xia et al. does not expressly teach a virtual reality tour of each of the listings
However, Lo which is in the art of generating virtual reality tours (see abstract) teaches a virtual reality tour of each of the listings (see paragraph 0003 and Figure 2, Examiner’s note: virtual reality tours of property listings are becoming increasingly common as they allow potential buyers to virtually tour a property from the comfort of their own home and Figure 2 shows rent).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois in view of Xia et al. with the aforementioned teachings from Lo with the motivation of providing a way of providing an increasingly common feature that allows potential buyers to virtually tour a property from the comfort of their own home (see Lo paragraph 0003 and Figure 2), when showing information about a property for purchase is known (see Dubois et al. paragraph 0003 and Figure 2).
Claim(s) 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Reis et al. (United States Patent Application Publication Number: US 2021/0264801).
As per claim 8, Dubois et al. teaches
wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of interaction data of a property of the subset of the plurality of properties, the interaction data comprising one or more of: information about the property (see Figure 4, Examiner’s note: teaches different information being displayed about the property).
Dubois et al. in view of Xia et al. does not expressly teach one or more of quantity of users looking at the property; quantity of applications submitted for the property; quantity of favorites associated with the property; quantity of unique property listing reviews of the property; or reviews of the property, owner, or landlord.
However, Reis et al. which is in the art of listings (see Figure 5) teaches one or more of quantity of users looking at the property; quantity of applications submitted for the property; quantity of favorites associated with the property; or reviews of the property, owner, or landlord (see Figure 5 and paragraph 0029, Examiner’s note: listing may include guest user reviews of the accommodation, it is noted only one is required).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Reis et al. with the motivation of providing a commonly known feature of providing reviews of something someone is going to buy to a potential buyer (see Reis et al. paragraph 0029 and Figure 5), when providing information of something someone is going to buy is known (See Dubois et al. Figure 4).
As per claim 11, Dubois et al. teaches
further comprising: receiving user input selecting a property of the subset of the plurality of properties; (see paragraph 0082 and 0104, and Figure 4, Examiner’s note: system records booking data and allows a user to book it! On the interface).
Dubois et al. in view of Xia et al. does not expressly teach and dynamically authorizing the one or more users to rent the property without an application.
However, Reis which is in the art of listings and rentals (see Figure 5) teaches and dynamically authorizing the one or more users to rent the property without an application (see paragraph 0057, Examiner’s note: teaches booking module auto books listings of an accommodation recommendation for a guest user. Booking module creates a new booking using the list, the time period, guest information, and payment details. Here Reis does not teach a user submitting an application).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Reis with the motivation of providing a way to allow a user to auto book a property that they see online (see Reis paragraph 0057), when Dubois et al. teaches allowing a user to select a property and book online is known (see Dubois paragraph 0082 and 0104, and Figure 4).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Yu et al. (United States Patent Application Publication Number: US 2019/0258818)
As per claim 10, Dubois et al. teaches
further comprising: receiving user input selecting a property of the subset of the plurality of properties; (see paragraph 0082 and 0104, and Figure 4, Examiner’s note: system records booking data and allows a user to book it! On the interface).
Dubois et al. in view of Xia et al. does not expressly teach prepopulating, based on at least a portion of the user data of the one or more users, a digital application; and transmitting the digital application to an administrative device for approval of the digital application.
However, Yu et al. which is in the art of requesting information on an individual (see abstract) teaches prepopulating, based on at least a portion of the user data of the one or more users, a digital application; and transmitting the digital application to an administrative device for approval of the digital application (see paragraph 0036 and 0045, Examiner’s note: teaches a requesting entity is a landlord and autofill an application provided to the requesting entity).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Yu with the motivation of using a known feature to auto populate or auto fill an application based on known information so a user doesn’t have to enter that information manually (see Yu paragraph 0036 and 0045) when allowing a user to book a rental and information about the user operating the system is known (see Dubois et al. 0082 and 0104, and Figure 4)
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Bayer et al. (United States Patent Application Publication Number: US 2019/0066002).
As per claim 12, Dubois et al. in view of Xia et al. does not expressly teach further comprising: automatically retrieving payment from a financial account of the one or more users; and automatically creating an executed digital lease and renting the property to the one or more users without transmitting the user data to an administrator device associated with the property.
However, Bayer et al. which is in the art of renting a property (see Figure 7) teaches further comprising: automatically retrieving payment from a financial account of the one or more users; and automatically creating an executed digital lease and renting the property to the one or more users without transmitting the user data to an administrator device associated with the property (see paragraphs 0014-0016, Examiner’s note: real time approval for group reservations and payments and sending a confirmation of the booking after payment is received, where this may be done via credit cards (e.g. financial accounts). Here Bayer et al. does not teach sending it to a administrator associated with the property rather it being done in real time upon payment).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Bayer et al. with the motivation of providing a way to provide a real time booking confirmation upon payment processing (see Bayer et al. paragraphs 0014-0015), when allowing a user to book a rental property online is known (see Dubois et al. Figure 4) is known.
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Vlahoplus et al. (united States Patent Application Publication Number: US 2002/009183).
As per claim 13,Dubois et al. teaches
wherein the causing of the graphical representation of the subset of the plurality of properties to be displayed comprises causing display of a rent amount (see Figure 4, Examiner’s note: rent amounts).
Dubois et al. in view of Xia et al. does not expressly teach and a bid option associated with a first property of the subset, wherein the method further comprises: displaying one or more of: a quantity of bids associated with the first property and a current bid amount; or a range of current bids; receiving, from the user device, bid data associated with the bid option; responsive to the bid data meeting the rent amount or a predetermined amount of time passing after the receiving of the bid data, selecting the bid data; and transmitting, to the user device, acceptance data.
However, Vlahoplus et al. which is in the art of electronic bidding (see abstract, Figure 36B-D, 37B-C, 41B, 42A-b), teaches and a bid option associated with a first property of the subset, wherein the method further comprises: displaying one or more of: a quantity of bids associated with the first property and a current bid amount; or a range of current bids; receiving, from the user device, bid data associated with the bid option; responsive to the bid data meeting the rent amount or a predetermined amount of time passing after the receiving of the bid data, selecting the bid data; and transmitting, to the user device, acceptance data (see Figure 36B-D, 37B-C, 41B, 42A-b, paragraph 0105, 0122, 0170, 0227, 0249, and 0257, Examiner’s note: trading of properties, where the highest bidder can be selected after time, the user is notified where the user may be using a portable computer, and also teaches providing the bids to the user upon request).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Vlahoplus et al. with the modification of providing an additional way to sell or rent properties through a known pricing structure like bidding or auctions (see Vlahoplus et al. Figure 36B-D, 37B-C, 41B, 42A-b, paragraph 0105, 0122, 0170, 0227, 0249, and 0257), when selling or renting property is known (see Dubois et al. Figure 4).
Claim(s) 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Schmidt-Lackner et al. (United States Patent Application Publication Number: US 2020/0250907).
As per claim 14, Dubois et al. in view of Xia et al. does not expressly teach further comprising, responsive to determining that the user device is within a predetermined distance of a digital lock device associated with a property, causing the digital lock device to unlock.
However, Schmidt-Lackner et al. which is in the art of automated entry (see abstract) teaches further comprising, responsive to determining that the user device is within a predetermined distance of a digital lock device associated with a property, causing the digital lock device to unlock (see Figure 7 and paragraph 0059-0060, Examiner’s note: using GPS to determine a location and unlocking a door to a property when near a location).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Schmidt-Lackner et al. with the motivation of providing a way to allow automated entry into the rental (see Schmidt-Lackner et al. Figure 7 and paragraph 0059-0060), when allowing automated bookings of rentals is known (see Dubois et al. Figure 4)
As per claim 15, Dubois et al. in view of Xia et al. does not expressly teach wherein the digital lock device is associated with access to one or more of the property, an amenity, or a door, the method further comprising determining that the access is to be accessible by the user device based on rent level associated with the user device.
However, Schmidt-Lackner et al. which is in the art of automated entry (see abstract) teaches wherein the digital lock device is associated with access to one or more of the property, an amenity, or a door, the method further comprising determining that the access is to be accessible by the user device based on rent level associated with the user device (see Figure 7 and paragraph 0059-0060, Examiner’s note: using GPS to determine a location and unlocking a door to a property when near a location. Further teaches payment being made to enter (e.g. rent level) (see Figure 7 and paragraphs 0059-0060). Further teaches these properties may be purchase or rentals (see paragraph 0032)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Schmidt-Lackner et al. with the motivation of providing a way to allow automated entry into the rental after they have paid (see Schmidt-Lackner et al. Figure 7 and paragraph 0059-0060), when allowing automated bookings of rentals is known (see Dubois et al. Figure 4)
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Flex “How to Handle a Lease Takeover for an Apartment” Internet Archive Way Back Machine Capture date of 5/18/2021.
As per claim 16, Dubois et al. in view of Xia et al. does not expressly teach further comprising: receiving a transfer request to transfer a property from a first user to a second user; and responsive to determining that a second renter score of the second user meets a threshold renter score for the property, causing the property to be transferred from the first user to the second user.
However, Flex which is in the art of apartment leases (see page 1) teaches further comprising: receiving a transfer request to transfer a property from a first user to a second user; and responsive to determining that a second renter score of the second user meets a threshold renter score for the property, causing the property to be transferred from the first user to the second user (see pages 1-3, Examiner’s note: lease takeover for the current user, signing a new lease for the new renter, and the new renter must pass background and credit checks on the landlord).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Flex with the motivation of automating a manual function to accomplish the same result according to established functions of allowing another user to continue a lease and therefore the original user not having to pay for the remaining balance and a landlord still getting the money of the original agreement (see Flex pages 1-3), when allowing a user to rent a property is known (see Dubois et al. Figure 4).
Examiner additionally notes here for Applicant’s convenience MPEP 2114 (cited herein):
When determining whether a computer-implemented functional claim would have been obvious, examiners should note that broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) ("Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years."); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dubois et al. (United States Patent Application Publication Number: US 2022/0375009) further in view of Xia et al. (United States Patent Number: US 11,625,796) further in view of Okta “What is Social Login and Is it Worth Implementing” Internet Archive Way Back Machine Capture date of 9/20/2020.
As per claim 18, Dubois et al. teaches
An application or social networking associated with the plurality of properties (see paragraphs 0032 and 0036 and Figure 7B, Examiner’s note: teaches applications to authenticate the user, and social networking (see paragraphs 0032 and 0036). Further teaches multiple properties (see Figure 7B)).
Dubois et al. in view of Xia et al. does not expressly teach Single sign on or social sign on or more specifically as recited further comprising: authenticating, via a secondary account of the user, a user to enter a primary account associated with the information of the website; extracting user information associated with the user from the secondary account; and verifying, based on the user information extracted from the secondary account, at least a portion of the user data entered by the user.
However, Okta teaches Single sign on or social sign on or more specifically as recited further comprising: authenticating, via a secondary account of the user, a user to enter a primary account associated with the information of the website; extracting user information associated with the user from the secondary account; and verifying, based on the user information extracted from the secondary account, at least a portion of the user data entered by the user (see pages 1, 3-4, 6, Examiner’s note: allows users to skip the registration process (see page 1). Teaches the social networks provide the data to the other party (see pages 3-4). Teaches users don’t need to fill out all the registration forms (see page 6). Teaches for organizations that use this like the websites this increases certification and account linking (see pages 6-7)).
Examiner’s note: applicant’s specification teaches social sign in performs this function, see paragraph 00108 cited herein “The processing logic can cause a prompt to be displayed via the user device of the user to prompt the user to login to an account or sign up for the prequalification system. In some embodiments, the processing logic can prompt the user for a username and/or password. In other embodiments, the user can use Fast Identity Online (FIDO) to log in to an account or sign up for the prequalification system. In some embodiments, the user can use a secondary account associated with a web platform or any social media, such as Google, Apple, Facebook, Microsoft, and/or Yahoo. In some embodiments, the processing logic can automatically and dynamically extract or retrieve information from the secondary account. The system can extract or the user can input a name of the user, a phone number of the user, an email of the user, a date of birth of the user, an address of the user, a previous address of the user, a gender of the user, and/or any other information associated with the user.”
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Dubois et al. in view of Xia et al. with the aforementioned teachings from Okta with the motivation of using a known type of identity verification (see Okta pages 1, 3-4, 6), when Dubois et al. specifically teaches using apps to authenticate a user (see Dubois et al. paragraph 0032) is known.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lammert, JR et al. (United States Patent Publication Number: US 2019/0019261) teaches a system for real estate inspections and valuations based on images (see abstract and title)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626