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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/19/2023 is in compliance with provisions 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following sections follow the 2019 PEG guidelines for analyzing subject matter.
The analysis below of the claims’ subject matter eligibility follows the 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”)
and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial
Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined
whether the claim is directed to one of the four statutory categories of invention, i.e., process,
machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the
statutory categories, the second step in the analysis is to determine whether the claim is directed
to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first
prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception
(e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If
it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis
proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the
claims integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical application, the
analysis proceeds to determining whether the claim is a patent-eligible application of the
exception (Step 2B). If an abstract idea is present in the claim, any element or combination of
elements in the claim must be sufficient to ensure that the claim integrates the judicial exception
into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determine, for each predicted location of the one or more predicted locations of the machine learning prediction, a distance and a transportation mode between that predicted location and a lodging unit of the remote location, wherein the distance and the transportation mode indicate a transportation amount associated with that predicted location; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determine a compound metric indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and transportation amounts for the one or more predicted locations; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
one or more memories; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
and one or more processors, communicatively coupled to the one or more memories, configured to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive, from a user device of a user, an indication of a remote location; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
retrieve interaction data relating to interactions between a plurality of entities and the user; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
determine, using a machine learning model and based on the interaction data, a machine learning prediction indicating one or more predicted locations of the user at the remote location; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a device associated with the lodging unit and based on the total amount satisfying a condition, an indication to secure the lodging unit for the user. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
one or more memories; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
and one or more processors, communicatively coupled to the one or more memories, configured to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive, from a user device of a user, an indication of a remote location; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
retrieve interaction data relating to interactions between a plurality of entities and the user; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
determine, using a machine learning model and based on the interaction data, a machine learning prediction indicating one or more predicted locations of the user at the remote location; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a device associated with the lodging unit and based on the total amount satisfying a condition, an indication to secure the lodging unit for the user. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to a method of making predictions on input data, this is nothing more than data gathering and processing. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
wherein the condition is that the total amount is a lowest total amount among a plurality of total amounts for a plurality of lodging units. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “selecting the lowest value from outputs” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “selecting the lowest value from outputs” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Please see the analysis of claim 1 above. Regarding the method steps recited in claim 1, these steps cover mental processes based on data prediction and selection.
Therefore, claim 3 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
receive, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receive, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
Claim 4 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
wherein the machine learning prediction includes one or more entity identifiers, and wherein the one or more predicted locations are associated with the one or more entity identifiers. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “prediction including one or more of specific criteria” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “prediction including one or more of specific criteria” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
wherein the machine learning prediction includes one or more entity categories. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “prediction including one or more of specific criteria” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “prediction including one or more of specific criteria” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 5.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 5 are incorporated.
identify, from a data set indicating attractions associated with the remote location, one or more entities associated with the one or more entity categories, wherein the one or more predicted locations are associated with the one or more entities. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “prediction including one or more of specific criteria” of base claim 5) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “prediction including one or more of specific criteria” of base claim 5) cannot provide an inventive concept. The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
determine, based on map data, a prediction that the user is to travel to a first predicted location and a second predicted location, of the one or more predicted locations, sequentially, wherein the transportation amounts for the one or more predicted locations reflects the prediction that the user is to travel to the first predicted location and the second predicted location sequentially. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “determining a first and second prediction based on data” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “determining a first and second prediction based on data” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 1.
Step 1: The claim recites a system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
determine, based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
using an additional machine learning model and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein the one or more processors are further configured to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
using an additional machine learning model and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein the one or more processors are further configured to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 9Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determining, by the device using a machine learning model and based on the interaction data, a machine learning prediction of a behavior of the user at the remote location; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determining, by the device, a distance and a transportation mode for each of one or more predicted locations associated with the behavior of the user and a lodging unit of the remote location, wherein the distance and the transportation mode indicate a transportation amount; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
determining, by the device, a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and transportation amounts for the one or more predicted locations; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
receiving, by a device from a user device of a user, an indication of a remote location; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
retrieving, by the device, interaction data relating to interactions between a plurality of entities and the user; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
transmitting, by the device to the user device, information indicating the total amount associated with the lodging unit. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receiving, by a device from a user device of a user, an indication of a remote location; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
retrieving, by the device, interaction data relating to interactions between a plurality of entities and the user; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
transmitting, by the device to the user device, information indicating the total amount associated with the lodging unit. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
As an ordered whole, the claim is directed to a method of making predictions on input data, this is nothing more than data gathering and processing. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 10 incorporates the rejection of claim 9.Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 9 are incorporated. Please see the analysis of claim 9 above. Regarding the method steps recited in claim 9, these steps cover mental processes based on data prediction and selection.
Therefore, claim 10 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
receiving, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receiving, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
Claim 11 incorporates the rejection of claim 9.Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 9 are incorporated.
processing the content to determine an entity category of interest to the user that is associated with the location, wherein the machine learning prediction is determined further based on the entity category of interest to the user. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
obtaining content posted by the user that is associated with data or metadata indicating a location that is different from a residence location of the user; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
obtaining content posted by the user that is associated with data or metadata indicating a location that is different from a residence location of the user; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
Claim 12 incorporates the rejection of claim 11.Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated. Please see the analysis of claim 11 above. Regarding the method steps recited in claim 11, these steps cover mental processes based on data prediction and selection.
Therefore, claim 12 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
wherein the content is an image or a video, and wherein processing the content comprises performing image recognition on the image or the video to determine the entity category associated with the location. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the content is an image or a video, and wherein processing the content comprises performing image recognition on the image or the video to determine the entity category associated with the location. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 13 incorporates the rejection of claim 11.Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated. Please see the analysis of claim 11 above. Regarding the method steps recited in claim 11, these steps cover mental processes based on data prediction and selection.
Therefore, claim 13 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
wherein the content is text, and wherein processing the text comprises performing natural language processing on the text to determine the entity category associated with the location. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the content is text, and wherein processing the text comprises performing natural language processing on the text to determine the entity category associated with the location. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 14 incorporates the rejection of claim 9.Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 9 are incorporated. Please see the analysis of claim 9 above. Regarding the method steps recited in claim 9, these steps cover mental processes based on data prediction and selection.
Therefore, claim 14 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
determining, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
determining, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 15Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
determine a distance and a transportation mode for each of one or more predicted locations associated with the behavior of the user and a lodging unit of the location, wherein the distance and the transportation mode indicate a transportation amount; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
one or more instructions that, when executed by one or more processors of a device, cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
determine, using a machine learning model and based on interaction data relating to interactions between a plurality of entities and a user, a machine learning prediction of a behavior of the user at a location; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a user device of the user, information indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and transportation amounts for the one or more predicted locations. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
one or more instructions that, when executed by one or more processors of a device, cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
determine, using a machine learning model and based on interaction data relating to interactions between a plurality of entities and a user, a machine learning prediction of a behavior of the user at a location; (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a user device of the user, information indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and transportation amounts for the one or more predicted locations. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to a method of making predictions on input data, this is nothing more than data gathering and processing. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 16 incorporates the rejection of claim 15.Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on data prediction and selection.
Therefore, claim 16 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a device associated with the lodging unit, an indication to secure the lodging unit for the user. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
transmit, to a device associated with the lodging unit, an indication to secure the lodging unit for the user. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 17 incorporates the rejection of claim 15.Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
wherein the machine learning prediction of the behavior of the user indicates the one or more predicted locations as an itinerary. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “predicting an itinerary of the user” of base claim 15) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “predicting an itinerary of the user” of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 18 incorporates the rejection of claim 15.Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
wherein the machine learning model is trained to determine the machine learning prediction based on a feature set that includes one or more of an entity category associated with an interaction, a location associated with an interaction, an amount associated with an interaction, or a time associated with an interaction. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “prediction including one or more of specific criteria” of base claim 15) cannot meaningfully integrate the judicial exceptions into a practical applications. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., “prediction including one or more of specific criteria” of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 19 incorporates the rejection of claim 15.Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on data prediction and selection.
Therefore, claim 19 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 20 incorporates the rejection of claim 15.Step 1: The claim recites a non-transitory computer-readable medium, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on data prediction and selection.
Therefore, claim 20 is directed to an abstract idea – Mental processes (i.e., can performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgements as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional element:
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive, from the user device of the user, an indication of the location and a set of constraints associated with the user. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
receive, from the user device of the user, an indication of the location and a set of constraints associated with the user. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
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.
Claims 1, 3, 4, 8 are rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Cella (US 20210342836 A1)
Regarding claim 1, PAO teaches one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: (See e.g. [0087], One or more memory buses (which may each include an address bus and a data bus) may couple processor 802 to memory 804)
receive, from a user device of a user, an indication of a remote location; (See e.g. [0017], the requestor [a user] 110 may use a transportation application running on a requestor computing device [device of a user] 120 to request a ride from a specified pick-up location to a specified drop-off location [a remote location]) (Examiner’s notes, the applicant’s specification (01/19/2023) has defined remote location to be a destination, see e.g. [0016], “For example, the remote location may be a destination to which the user is to travel.”)
retrieve interaction data relating to interactions between a plurality of entities and the user; (See e.g. [0061], Identity management services 604 may also manage and control access to provider and requestor data [the user] maintained by the transportation management system 602, such as driving and/or ride histories [plurality of entities], vehicle data, personal data, preferences, usage patterns as a ride provider and as a ride requestor, profile pictures, linked third-party accounts (e.g., credentials for music or entertainment services, social-networking systems, calendar systems, task-management systems, etc.) and any other associated information.)
determine, using a machine learning model and based on the interaction data, a machine learning prediction indicating one or more predicted locations of the user at the remote location; (See e.g. [0039], the machine learning model may learn to predict, generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input representing basic location information [locations of the user at the remote location] (e.g., geographic coordinates, a place name associated with known geographic coordinates, and/or a street address associated with known geographic coordinates). The learning performed by the machine learning model during a training phase may include determining the respective importance (e.g., the relative effectiveness) of various reference expressions in facilitating rendezvous between ride requestors 245 and ride providers 240 [based on the interaction data] for specific locations and/or types of locations.)
determine, for each predicted location of the one or more predicted locations of the machine learning prediction, a distance and a transportation [mode] between that predicted location and a lodging unit of the remote location, (See e.g. [0017], The ride request may also include transport information, such as, e.g., a pick-up location, a drop-off location, a “best fit/predictive” location (e.g., a particular location in the origination/destination region suitable for pick-up/drop-off at a given time))(See e.g. [0078 – 79], the training of the model may involve deep Long Short Term Memory (LSTM) recurrent neural networks (e.g., with four layers), a vocabulary size of about 30k words (when tf>1), and an embedding size of 128. In this example embodiment, the input data may be represented using the following format: #name1 big box store [predicted location] #street1 third street #number1 3207 #distance1 29 #name2 hotel x [a lodging unit] #street2 avenue C #number2 804 #distance2 [a distance] 41)
PAO does not teach wherein the distance and the transportation mode indicate a transportation amount associated with that predicted location; determine a compound metric indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations; and transmit, to a device associated with the lodging unit and based on the total amount satisfying a condition, an indication to secure the lodging unit for the user.
Otillar teaches wherein the distance and the transportation mode indicate a transportation amount associated with that predicted location; (See e.g. [0061], the machine learning engine 265 may include information in a feature vector indicating that the user has booked transportation to a given location (e.g., booked a flight, cruise ship ride, or bus ride on a certain date) [transportation mode]) (See e.g. [0075], A feature vector and training label indicate that the users who arrive at SFO airport and are traveling to downtown San Francisco often take the Bay Area Rapid Transit (BART) subway 440 (e.g., because the subway fare is less than the cost of a rental car) [transportation amount]) determine a compound metric indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations; (See e.g. [0051], the online system 100 dynamically adds at impression time certain data to the content item, such as information about current prices of services [compound metric] or information based on the user's travel itinerary.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO and Otillar before them, to include Otillar’s value association which would allow PAO’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
PAO and Otillar do not teach transmit, to a device associated with the lodging unit and based on the total amount satisfying a condition, an indication to secure the lodging unit for the user.
Cella teaches transmit, to a device associated with the lodging unit and based on the total amount satisfying a condition, an indication to secure the lodging unit for the user. (See e.g. [1186], In embodiments, accommodations may be provided with configured forward market parameters 4208 (including conditional parameters) apart from access tokens 4008 to events, such as where a hotel room or other accommodation is booked in advance upon meeting a certain condition (such as one relating to a price within a given time window)…Thus, demand for accommodations can be aggregated in advance and conveniently fulfilled by automatic recognition (such as by monitoring systems 3306) of conditions that satisfy pre-configured commitments represented on a blockchain (e.g., distributed ledger) and automatic initiation (optionally including by smart contract execution) of settlement or fulfillment of the demand (such as by automated booking of a room or other accommodations).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Cella before them, to include Cella’s automated action based on threshold trigger PAO and Otillar’s model to automatically book lodging when a satisfying condition is met. One would have been motivated to make such a combination in order to increase the speed and reliability of the model for users to make plans, as suggested by Cella (US 20210342836 A1) (0017)
Regarding claim 3, PAO, Otillar and Cella teach the system of claim 1. PAO further teaches receive, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (See e.g. [0042], the method may include providing the received data to a human-understandable geospatial descriptor generator as input. For example, the human-understandable descriptor generator may be configured to, predict [machine learning prediction], generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input data representing basic location information (e.g., geographic coordinates, a place name associated with known geographic coordinates, and/or a street address associated with known geographic coordinates).) (See e.g. [0001], through a transportation application installed on a mobile device, a ride requestor [the user] may submit a request for a ride from a starting location to a destination at a particular time. [the set of constraints.])
Regarding claim 4, PAO, Otillar and Cella teach the system of claim 1. PAO further teaches wherein the machine learning prediction includes one or more entity identifiers, and (See e.g. [0017], The ride request may also include transport information, such as, e.g., a pick-up location, a drop-off location, a “best fit/predictive” location (e.g., a particular location in the origination/destination region suitable for pick-up/drop-off at a given time), preferred pick-up/drop-off location type (e.g., a curb segment), or any other suitable information for indicating the requestor's transportation preferences and/or objectives.) (See e.g. [0023], For example, ride requestor 245a has dropped pin 260a at Hotel Z (208) [entity identifiers] on the map, indicating Hotel Z as the desired pick-up location)
wherein the one or more predicted locations are associated with the one or more entity identifiers. (See e.g. [0039], the machine learning model may learn to predict, generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input representing basic location information (e.g., geographic coordinates, a place name associated with known geographic coordinates [entity identifiers], and/or a street address associated with known geographic coordinates).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO and Otillar before them, to include Otillar’s value association which would allow PAO’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
Regarding claim 8, PAO, Otillar and Cella teach the system of claim 1. PAO does not teach wherein the one or more processors are further configured to: determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference.
Otillar teaches wherein the one or more processors are further configured to: determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (See e.g. [0125], a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.) (See e.g. [0076], The online system 100 receives information from the client device 110 indicating Sheryl's intent to switch from the rental car to subway…The online system 100 can also modify the itinerary information of Sheryl's trip based on the request, e.g., to remove the rental car booking from her trip and add information about one or more subway trips that Sheryl can take downtown.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO and Otillar before them, to include Otillar’s value association which would allow PAO’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Cella (US 20210342836 A1) further in view of Bahnsen (US 20240110806 A1) further in view of Rowley (US 20190325507 A1)
Regarding claim 2, PAO, Otillar and Cella teach the system of claim 1.
PAO, Otillar and Cella do not teach wherein the condition is that the total amount is a lowest total amount among a plurality of total amounts for a plurality of lodging units.
Rowley teaches wherein the condition is that the total amount is a lowest total amount among a plurality of total amounts for a plurality of lodging units. (See e.g. [0051], After receiving a purchase commitment from the member, the system then acquires the asset (e.g., hotel room) from whichever feed provides the lowest acquisition cost, such as a hotel aggregator (e.g., Expedia), or the like.) (See e.g. [0088], Other rules may require pricing to be displayed as total stay, nightly stay, tax inclusive/exclusive, or that no king size beds be made available to smokers.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Cella and Rowley before them, to include Rowley’s running total, which would allow PAO, Otillar and Cella’s model to incorporate total value into its predictions. One would have been motivated to make such a combination in order to better predict and monetize user behavior, as suggested by Rowley (US 20190325507 A1) (0040)
Claims 5 – 7 are rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Cella (US 20210342836 A1) further in view of Bahnsen (US 20240110806 A1)
Regarding claim 5, PAO, Otillar and Cella teach the system of claim 1.
PAO, Otillar and Cella do not teach wherein the machine learning prediction includes one or more entity categories.
Bahnsen teaches wherein the machine learning prediction includes one or more entity categories. (See e.g. [0028], The user's data may indicate that the user frequently visits seafood restaurants and jazz clubs [entity categories] in their home town, so the techniques of the present disclosure may generate an experience recommendation featuring a seafood restaurant and a jazz club in the new city for the user to experience.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Cella and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO, Otillar and Cella’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 6, PAO, Otillar, Cella and Bahnsen teach the system of claim 5.
PAO, Otillar and Cella do not teach identify, from a data set indicating attractions associated with the remote location, one or more entities associated with the one or more entity categories, wherein the one or more predicted locations are associated with the one or more entities.
Bahnsen teaches identify, from a data set indicating attractions associated with the remote location, one or more entities associated with the one or more entity categories, wherein the one or more predicted locations are associated with the one or more entities. (See e.g. [0028], The user's data may indicate that the user frequently visits seafood restaurants and jazz clubs [entity categories] in their home town, so the techniques of the present disclosure may generate an experience recommendation featuring a seafood restaurant and a jazz club in the new city for the user to experience.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Cella and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO, Otillar and Cella’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 7, PAO, Otillar and Cella teach the system of claim 1
PAO and Cella do not teach wherein the transportation amounts for the one or more predicted locations reflects the prediction that the user is to travel to the predicted location Otillar teaches wherein the transportation amounts for the one or more predicted locations reflects the prediction that the user is to travel to the predicted location (See e.g. [0075], A feature vector and training label indicate that the users who arrive at SFO airport and are traveling to downtown San Francisco often take the Bay Area Rapid Transit (BART) subway 440 (e.g., because the subway fare is less than the cost of a rental car) [transportation amount])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Cella before them, to include Otillar’s value association which would allow PAO and Cella’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
PAO, Otillar and Cella do not teach determine, based on map data, a prediction that the user is to travel to a first predicted location and a second predicted location, of the one or more predicted locations, sequentially
Bahnsen teaches determine, based on map data, a prediction that the user is to travel to a first predicted location and a second predicted location, of the one or more predicted locations, sequentially (See e.g. [0093], the suggested experience-focused navigation session to the user as an appointment on the computing device… the ordered list may include (i) at least a second location and (ii) the sequential navigation directions from the first location to the second location.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Cella and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO, Otillar and Cella’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Claims 9, 10, 14, 15, 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Bahnsen (US 20240110806 A1)
Regarding claim 9, PAO teaches receiving, by a device from a user device of a user, an indication of a remote location; (See e.g. [0017], the requestor [a user] 110 may use a transportation application running on a requestor computing device [device of a user] 120 to request a ride from a specified pick-up location to a specified drop-off location [a remote location])
retrieving, by the device, interaction data relating to interactions between a plurality of entities and the user; (See e.g. [0061], Identity management services 604 may also manage and control access to provider and requestor data [the user] maintained by the transportation management system 602, such as driving and/or ride histories [plurality of entities], vehicle data, personal data, preferences, usage patterns as a ride provider and as a ride requestor, profile pictures, linked third-party accounts (e.g., credentials for music or entertainment services, social-networking systems, calendar systems, task-management systems, etc.) and any other associated information.)
determining, by the device using a machine learning model and based on the interaction data, a machine learning prediction of [a behavior of] the user at the remote location; (See e.g. [0039], the machine learning model may learn to predict, generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input representing basic location information [locations of the user at the remote location] (e.g., geographic coordinates, a place name associated with known geographic coordinates, and/or a street address associated with known geographic coordinates). The learning performed by the machine learning model during a training phase may include determining the respective importance (e.g., the relative effectiveness) of various reference expressions in facilitating rendezvous between ride requestors 245 and ride providers 240 [based on the interaction data] for specific locations and/or types of locations.)
determining, by the device, a distance and a transportation [mode] for each of one or more predicted locations associated with the behavior of the user and a lodging unit of the remote location, (See e.g. [0017], The ride request may also include transport information, such as, e.g., a pick-up location, a drop-off location, a “best fit/predictive” location (e.g., a particular location in the origination/destination region suitable for pick-up/drop-off at a given time))(See e.g. [0078 – 79], the training of the model may involve deep Long Short Term Memory (LSTM) recurrent neural networks (e.g., with four layers), a vocabulary size of about 30k words (when tf>1), and an embedding size of 128. In this example embodiment, the input data may be represented using the following format: #name1 big box store [predicted location] #street1 third street #number1 3207 #distance1 29 #name2 hotel x [a lodging unit] #street2 avenue C #number2 804 #distance2 [a distance] 41)
PAO does not teach wherein the distance and the transportation mode indicate a transportation amount; determining, by the device, a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations; and transmitting, by the device to the user device, information indicating the total amount associated with the lodging unit.
Otillar teaches wherein the distance and the transportation mode indicate a transportation amount; (See e.g. [0061], the machine learning engine 265 may include information in a feature vector indicating that the user has booked transportation to a given location (e.g., booked a flight, cruise ship ride, or bus ride on a certain date) [transportation mode]) (See e.g. [0075], A feature vector and training label indicate that the users who arrive at SFO airport and are traveling to downtown San Francisco often take the Bay Area Rapid Transit (BART) subway 440 (e.g., because the subway fare is less than the cost of a rental car) [transportation amount])
determining, by the device, a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations; (See e.g. [0051], the online system 100 dynamically adds at impression time certain data to the content item, such as information about current prices of services [compound metric] or information based on the user's travel itinerary.)
transmitting, by the device to the user device, information indicating the total amount associated with the lodging unit. (See e.g. [0119], the incentive may be determined based on whether the user stayed at the hotel for a threshold number of nights or for a threshold number of trips within a certain time period. [based on the total amount] The content engine 270 provides 940 the selected content item for display on a client device 110 of the user within a predetermined period of time following the check-out date of the user.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO and Otillar before them, to include Otillar’s value association which would allow PAO’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
PAO and Otillar do not teach a behavior of the user at the remote location;
Bahnsen teaches a behavior of the user at the remote location; (See e.g. [0012], the systems of the present disclosure may track the user's progress through that experience-focused navigation session (per a user opt-in) and derive various indications of satisfaction with the experience-focused navigation session based on the user's behavior. For example, if a user has followed a recommended experience, the system may ask the user to explicitly rate (e.g., on a scale of 1 to 10) the experience-focused navigation session on one or more dimensions, such as cost, quality, entertainment value, appropriateness, etc.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO and Otillar’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 10, PAO, Otillar and Bahnsen teach the method of claim 9. PAO further teaches receiving, from the user device, a set of constraints associated with the user, wherein the machine learning prediction and the transportation mode are in accordance with the set of constraints. (See e.g. [0042], the method may include providing the received data to a human-understandable geospatial descriptor generator as input. For example, the human-understandable descriptor generator may be configured to, predict [machine learning prediction], generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input data representing basic location information (e.g., geographic coordinates, a place name associated with known geographic coordinates, and/or a street address associated with known geographic coordinates).) (See e.g. [0001], through a transportation application installed on a mobile device, a ride requestor [the user] may submit a request for a ride from a starting location to a destination at a particular time. [the set of constraints.])
Regarding claim 14, PAO, Otillar and Bahnsen teach the method of claim 9
PAO and Bahnsen do not teach determining, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference.
Otillar teaches determining, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (See e.g. [0076], The online system 100 receives information from the client device 110 indicating Sheryl's intent to switch from the rental car to subway…The online system 100 can also modify the itinerary information of Sheryl's trip based on the request, e.g., to remove the rental car booking from her trip and add information about one or more subway trips that Sheryl can take downtown.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Otillar’s value association which would allow PAO and Bahnsen’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
Regarding claim 15, PAO teaches one or more instructions that, when executed by one or more processors of a device, cause the device to: (See e.g. [0086], Data in the data caches may be copies of data in memory 804 or storage 806 for instructions executing at processor 802 to operate on; the results of previous instructions executed at processor 802 for access by subsequent instructions executing at processor 802 or for writing to memory 804 or storage 806)
determine, using a machine learning model and based on interaction data relating to interactions between a plurality of entities and a user, a machine learning prediction of [a behavior of] the user at a location; (See e.g. [0039], the machine learning model may learn to predict, generate, and output human-understandable geospatial descriptors for specified locations in response to receiving input representing basic location information [locations of the user at the remote location] (e.g., geographic coordinates, a place name associated with known geographic coordinates, and/or a street address associated with known geographic coordinates). The learning performed by the machine learning model during a training phase may include determining the respective importance (e.g., the relative effectiveness) of various reference expressions in facilitating rendezvous between ride requestors 245 and ride providers 240 [based on the interaction data] for specific locations and/or types of locations.)
determine a distance and a transportation [mode] for each of one or more predicted locations associated with the [behavior of the user and a] lodging unit of the location, (See e.g. [0078 – 79], the training of the model may involve deep Long Short Term Memory (LSTM) recurrent neural networks (e.g., with four layers), a vocabulary size of about 30k words (when tf>1), and an embedding size of 128. In this example embodiment, the input data may be represented using the following format: #name1 big box store [predicted location] #street1 third street #number1 3207 #distance1 29 #name2 hotel x [a lodging unit] #street2 avenue C #number2 804 #distance2 [a distance] 41)
PAO does not teach wherein the distance and the transportation mode indicate a transportation amount; transmit, to a user device of the user, information indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations.
Otillar teaches wherein the distance and the transportation mode indicate a transportation amount; (See e.g. [0061], the machine learning engine 265 may include information in a feature vector indicating that the user has booked transportation to a given location (e.g., booked a flight, cruise ship ride, or bus ride on a certain date) [transportation mode]) (See e.g. [0075], A feature vector and training label indicate that the users who arrive at SFO airport and are traveling to downtown San Francisco often take the Bay Area Rapid Transit (BART) subway 440 (e.g., because the subway fare is less than the cost of a rental car) [transportation amount])
transmit, to a user device of the user, information indicating a total amount associated with the lodging unit, wherein the total amount includes a lodging amount associated with the lodging unit and [transportation] amounts for the one or more predicted locations. (See e.g. [0119], the incentive may be determined based on whether the user stayed at the hotel for a threshold number of nights or for a threshold number of trips within a certain time period. [based on the total amount] The content engine 270 provides 940 the selected content item for display on a client device 110 of the user within a predetermined period of time following the check-out date of the user.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO and Otillar before them, to include Otillar’s value association which would allow PAO’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
PAO and Otillar do not teach a behavior of the user at the remote location;
Bahnsen teaches a behavior of the user at the remote location; (See e.g. [0012], the systems of the present disclosure may track the user's progress through that experience-focused navigation session (per a user opt-in) and derive various indications of satisfaction with the experience-focused navigation session based on the user's behavior. For example, if a user has followed a recommended experience, the system may ask the user to explicitly rate (e.g., on a scale of 1 to 10) the experience-focused navigation session on one or more dimensions, such as cost, quality, entertainment value, appropriateness, etc.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO and Otillar’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 17, PAO, Otillar and Bahnsen teach the non-transitory computer-readable medium of claim 15
PAO and Otillar do not teach wherein the machine learning prediction of the behavior of the user indicates the one or more predicted locations as an itinerary. Bahnsen teaches wherein the machine learning prediction of the behavior of the user indicates the one or more predicted locations as an itinerary. (See e.g. [0016], a semantic mapping corresponding to the user based on one or more user preferences and a location history included in the user data; determining, by the one or more processors, a suggested experience-focused navigation session for the user based on the semantic mapping and the current location of the user, wherein the suggested experience-focused navigation session includes an ordered list of one or more suggested points of interest)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO and Otillar’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 18, PAO, Otillar and Bahnsen teach the non-transitory computer-readable medium of claim 15
PAO and Otillar do not teach wherein the machine learning model is trained to determine the machine learning prediction based on a feature set that includes one or more of an entity category associated with an interaction, a location associated with an interaction, an amount associated with an interaction, or a time associated with an interaction.
Bahnsen teaches wherein the machine learning model is trained to determine the machine learning prediction based on a feature set that includes one or more of an entity category associated with an interaction, a location associated with an interaction, an amount associated with an interaction, or a time associated with an interaction. (See e.g. [0056], For example, the 2 hours allotted within the suggested experience may be insufficient to adequately enjoy a meal at the designated restaurant (e.g., the first POI).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO and Otillar’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 19, PAO, Otillar and Bahnsen teach the non-transitory computer-readable medium of claim 15
PAO and Bahnsen do not teach wherein the one or more instructions, when executed by the one or more processors, further cause the device to: determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference.
Otillar teaches wherein the one or more instructions, when executed by the one or more processors, further cause the device to: determine, using an additional machine learning model and based on the interaction data, a transportation mode preference of the user, wherein the transportation mode corresponds to the transportation mode preference. (See e.g. [0125], a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.) (See e.g. [0076], The online system 100 receives information from the client device 110 indicating Sheryl's intent to switch from the rental car to subway…The online system 100 can also modify the itinerary information of Sheryl's trip based on the request, e.g., to remove the rental car booking from her trip and add information about one or more subway trips that Sheryl can take downtown.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar and Bahnsen before them, to include Otillar’s value association which would allow PAO and Bahnsen’s model to incorporate distance and price into its predictions. One would have been motivated to make such a combination in order to establish stronger correlations and predictions by the model, as suggested by Otillar (US 20180276572 A1) (0064)
Regarding claim 20, PAO, Otillar and Bahnsen teach the non-transitory computer-readable medium of claim 15. PAO further teaches wherein the one or more instructions, when executed by the one or more processors, further cause the device to: receive, from the user device of the user, an indication of the location and a set of constraints associated with the user. (See e.g. [0086], Data in the data caches may be copies of data in memory 804 or storage 806 for instructions executing at processor 802 to operate on; the results of previous instructions executed at processor 802 for access by subsequent instructions executing at processor 802 or for writing to memory 804 or storage 806) (See e.g. [0027], ride requestor 245a may call ride provider 240a to suggest that they rendezvous “on the corner of Second St. and Ave. B.” Similarly, in response to ride provider 240c accepting the ride requested by ride requestor 245c, for which pin 260c was dropped on Hotel X (232), ride requestor 245c may call or text ride [from the user device of the user] provider 240c to say, “I'm waiting in the loading zone,” [a set of constraints associated with the user.] indicating Loading Zone 250e outside of Hotel X [an indication of the location] (232).)
Claims 11 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Bahnsen (US 20240110806 A1) further in view of Koukoumidis (US 11038974 B1)
Regarding claim 11, PAO, Otillar and Bahnsen teach the method of claim 9
PAO, Otillar and Bahnsen do not teach obtaining content posted by the user that is associated with data or metadata indicating a location that is different from a residence location of the user; and processing the content to determine an entity category of interest to the user that is associated with the location,
Koukoumidis teaches obtaining content posted by the user that is associated with data or metadata indicating a location that is different from a residence location of the user; and processing the content to determine an entity category of interest to the user that is associated with the location (See e.g. [C2:L65 – 3], the user state may refer to the user's activity (e.g., user is scrolling through his newsfeed or user is currently running) and/or a description of the user's status (e.g., user is on vacation or user is 100 miles away from home). The context may include date/time, location, and other metadata and content of posts.) (See e.g. [C9:L20 – 25], As an example and not by way of limitation, content objects may include information regarding things or activities of interest to the user, such as, for example, movie show times, movie reviews, restaurant reviews, restaurant menus, product information and reviews, or other suitable information.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Bahnsen and Koukoumidis before them, to include Koukoumidis’s method to collect metadata/data from user’s posts, which would allow PAO, Otillar and Bahnsen’s model to incorporate users posted to gather addition accurate data on the user for its predictions. One would have been motivated to make such a combination in order to improve classification and content extracting information from the user post, as suggested by Koukoumidis (US 11038974 B1) (C18:L38 – 47)
PAO, Otillar and Koukoumidis do not teach wherein the machine learning prediction is determined further based on the entity category of interest to the user.
Bahnsen teaches wherein the machine learning prediction is determined further based on the entity category of interest to the user. (See e.g. [0028], The user's data may indicate that the user frequently visits seafood restaurants and jazz clubs in their home town, so the techniques of the present disclosure may generate an experience recommendation featuring a seafood restaurant and a jazz club in the new city for the user to experience.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Bahnsen and Koukoumidis before them, to include Bahnsen’s entity categories and user behavior, which would allow PAO, Otillar and Koukoumidis’s model to incorporate broader suggestion groups and user’s response to locations into its predictions. One would have been motivated to make such a combination in order to improve use experience focused navigation and predictions, as suggested by Bahnsen (US 20240110806 A1) (0006)
Regarding claim 12, PAO, Otillar and Bahnsen teach the method of claim 11
PAO, Otillar and Bahnsen do not teach wherein the content is an image or a video, and wherein processing the content comprises performing image recognition on the image or the video to determine the entity category associated with the location.
Koukoumidis teaches wherein the content is an image or a video, and wherein processing the content comprises performing image recognition on the image or the video to determine the entity category associated with the location. (See e.g. [C11:L26 – 30], If the user input is based on an image or video modality, the assistant system 140 may process it using optical character recognition techniques within the messaging platform 205 to convert the user input into text.) (See e.g. [C31:L3 – 8], if the assistant system 140 receives the trigger action 602 from a client system 130, then the assistant system 140 may determine the geographic location of the client system 130, a date and time of when the trigger action 602 was received, and other parameters that may be associated with the trigger action 602.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Bahnsen and Koukoumidis before them, to include Koukoumidis’s method to collect metadata/data from user’s posts, which would allow PAO, Otillar and Bahnsen’s model to incorporate users posted to gather addition accurate data on the user for its predictions. One would have been motivated to make such a combination in order to improve classification and content extracting information from the user post, as suggested by Koukoumidis (US 11038974 B1) (C18:L38 – 47)
Regarding claim 13, PAO, Otillar and Bahnsen teach the method of claim 11
PAO, Otillar and Bahnsen do not teach wherein the content is text, and wherein processing the text comprises performing natural language processing on the text to determine the entity category associated with the location.
Koukoumidis teaches wherein the content is text, and wherein processing the text comprises performing natural language processing on the text to determine the entity category associated with the location. (See e.g. [C10:L53 – 55], the assistant system 140 may analyze the user input using natural-language understanding.) (See e.g. [C31:L3 – 8], if the assistant system 140 receives the trigger action 602 from a client system 130, then the assistant system 140 may determine the geographic location of the client system 130, a date and time of when the trigger action 602 was received, and other parameters that may be associated with the trigger action 602.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Bahnsen and Koukoumidis before them, to include Koukoumidis’s method to collect metadata/data from user’s posts, which would allow PAO, Otillar and Bahnsen’s model to incorporate users posted to gather addition accurate data on the user for its predictions. One would have been motivated to make such a combination in order to improve classification and content extracting information from the user post, as suggested by Koukoumidis (US 11038974 B1) (C18:L38 – 47)
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over PAO (US 20190171943 A1) in view of Otillar (US 20180276572 A1) further in view of Cella (US 20210342836 A1) (0017) further in view of Bahnsen (US 20240110806 A1)
Regarding claim 16, PAO, Otillar and Bahnsen teach the non-transitory computer-readable medium of claim 9. PAO further teaches wherein the one or more instructions, when executed by the one or more processors, further cause the device to: (See e.g. [0086], Data in the data caches may be copies of data in memory 804 or storage 806 for instructions executing at processor 802 to operate on; the results of previous instructions executed at processor 802 for access by subsequent instructions executing at processor 802 or for writing to memory 804 or storage 806)
PAO, Otillar and Bahnsen do not teach transmit, to a device associated with the lodging unit, an indication to secure the lodging unit for the user.
Cella teaches transmit, to a device associated with the lodging unit, an indication to secure the lodging unit for the user. (See e.g. [1186], In embodiments, accommodations may be provided with configured forward market parameters 4208 (including conditional parameters) apart from access tokens 4008 to events, such as where a hotel room or other accommodation is booked in advance upon meeting a certain condition (such as one relating to a price within a given time window)…Thus, demand for accommodations can be aggregated in advance and conveniently fulfilled by automatic recognition (such as by monitoring systems 3306) of conditions that satisfy pre-configured commitments represented on a blockchain (e.g., distributed ledger) and automatic initiation (optionally including by smart contract execution) of settlement or fulfillment of the demand (such as by automated booking of a room or other accommodations).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of PAO, Otillar, Cella and Bahnsen before them, to include Cella’s automated action based on threshold trigger PAO, Otillar and Bahnsen’s model to automatically book lodging when a satisfying condition is met. One would have been motivated to make such a combination in order to increase the speed and reliability of the model for users to make plans, as suggested by Cella (US 20210342836 A1) (0017)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ALLMAN THOMPSON whose telephone number is (571)272-3671. The examiner can normally be reached Monday - Thursday, 6 a.m. - 3 p.m. ET..
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/K.A.T./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125