DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim(s) 8 is/are objected to because of the following informalities:
Claim 8: “wherein the retrieved at least one candidate estimate presented in the checkout interface is related to of a product presented on the product page” should read “wherein the retrieved at least one candidate estimate presented in the checkout interface is related to [[of]] a product presented on the product page.
Appropriate correction is required.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/13/21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1/13/21 are directed towards a computer system (i.e. machine), a method (i.e. a process) and computer readable medium (i.e. a manufacture), respectively. Thus, each of the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
The claims recite “obtain a geolocation estimate based on an IP address associated; obtain, using a machine learning model, one or more candidate estimates for at least one candidate region overlapping with an accuracy region defined about the geolocation estimate; store the obtained one or more candidate estimates; responsive to receiving input indicating a desired region, retrieve at least one candidate estimate for an identified candidate region matching the desired region; and communicate the at least one retrieved candidate estimate to the user; present the at least one retrieved candidate estimate“
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of predicting a geographical estimate from an IP address which is a method of organizing a human activity and mental process. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and concepts that can be done in the human mind.
This judicial exception is not integrated into a practical application. In particular, the claims recite “a cache, a processing unit, a user device, a machine learning model and a checkout interface” (claim 1); “a cache, a user device, a machine learning model and a checkout interface” (claim 13) and “a cache, a processing unit of a computer system, a user device, a machine learning model and a checkout interface” (claim 21). Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer.
Dependent claims 2-6, 12, 14-17 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 7 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“wherein the geolocation estimate is obtained prior to presentation of the checkout interface on the user device” is recited at a high level of generality which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 8 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“wherein the geolocation estimate is obtained during or prior to presentation of a product page on the user device, wherein the retrieved at least one candidate estimate presented in the checkout interface is related to of a product presented on the product page” is recited at a high level of generality which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 9/18 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1/13 without successfully integrating the exception into a practical application (“obtain the one or more candidate estimates for the at least one candidate region by executing the machine learning system by: inputting to the machine learning system a set of input data including data representing the at least one candidate region; and obtaining a prediction from the machine learning system including the one or more candidate estimates” is recited at a high level of generality which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 10/19 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1/13 without successfully integrating the exception into a practical application (“executing the machine learning system to obtain at least one candidate estimate for the higher priority candidate region prior to obtaining at least one candidate estimate for a remainder of the two or more candidate regions” is recited at a high level of generality which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 11/20 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1/13 without successfully integrating the exception into a practical application (“retrieving, from the cache, the at least one candidate estimate, the retrieved at least one candidate estimate being previously obtained using the machine learning system” is recited at a high level of generality which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 7-9, 11-13, 18-20 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walters (US 2021/0103975) in view of Van Boucq (US 2015/0356606), hereinafter “Van” in further view of Tobkin (US 2024/0119003).
As per claim 1/13/21, Walters discloses a computer system comprising: a processing unit configured to execute computer readable instructions to cause the computer system to: obtain an IP address associated with a user device (paragraph 18, “[0018] Service provider system 104 may be configured to access one or more user parameters associated with one or more of the user devices, e.g., user device 102(1). The one or more user parameters may include an IP address of user device 102(1), cookies stored on user device 102(1), and/or a fingerprint associated with user device 102(1), although any other information associated with user device 102(1) may also be included.”).
Obtain, using a machine learning model, one or more candidate estimates for at least one candidate region (The fingerprint associated with user device 102(1) may enable access to an operating system and/or hardware of user device 102(1), and/or types of applications stored on the user device 102(1), although any other information of the user device 102(1) may also be included. The types of applications stored on user device 102(1) may include a type of web browser, a uniform resource locator (URL) of a webpage accessed via the web browser, and a web browser version. Service provider system 104 may include a machine learning algorithm such as a Gaussian Process Regression (GPR) or a convolution long short-term memory (LSTM) network, however any other type of intelligence and machine learning algorithm may be applied. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). Based on the predicted geolocation of user device 102(1), the machine learning algorithm provides a recommendation for a plurality form fields associated with a checkout form. The plurality of form fields of a checkout form may include: street address field, state field, zipcode field, country field, etc.”, Examiner interprets the checkout autofill recommendations as the ”one or more candidate estimates”);
Store the obtained one or more candidate estimates in a database (“[0037]…. In certain embodiments, memory 306 may store sets of instructions or programs 308 for receiving and storing a list of form fields of a checkout form associated with a plurality of websites, monitoring user interactions with the plurality of websites, predicting geolocation of customers 114(1)-114(n) based on analyzing user parameters associated with customers 114(1)-114(n).”, “[0055]… In step 428, service provider system 104 places the state form field of “Hamburg” in a first list stored in database 106.”, “[0058] Service provider system 104 in step 424 stores an updated version of the first unfilled template of the checkout form in database 106. In this example, the updated version of the first unfilled template of the checkout form includes the street address field of “Friedrich-Ebert-Damm 12345” and the zipcode form field of “20123”, as explained in step 420 above.”);
responsive to receiving, from the user device, input indicating a desired region, retrieve from the database at least one candidate estimate for an identified candidate region matching the desired region ([0045] In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be “Friedrich-Ebert-Damm 12345, 20123 Hamburg, Germany”. Further, based on analysis of the fingerprint associated with user device 102(1) predicts the geolocation to be in zipcode of 20123. [0046] Service provider system 104 in step 412 identifies and scores autofill recommendations for the plurality of form fields determined in step 406 based on the geolocation predicted in step 410. The machine learning algorithm in step 412 provides recommendations for one or more of the pluralities of form fields based on the identified geolocation of user device 102(1). In this example, the plurality of form fields of a checkout form includes: street address.field, state field, zipcode field and country field. Further, based on the prediction of the geolocation made in step 410, the machine learning algorithm provides autofill recommendations for the form fields of street address field, state field, zipcode field and country field based on the predicted geolocation…. [0049] Service provider system 104 in step 416 generates and presents a recommendation to the user while the user is browsing the e-commerce website and before the user reaches the checkout form webpage. The recommendation would be in the form of a pop window on the top of the webpage. The recommendation would correspond to the first one of the plurality of form fields i.e. the address field of “Friedrich-Ebert-Damm 12345”. The prompt presents a question with multiple choices answer for customer 114(1) to select and provide as input. The prompt may be “Do you want this item to be delivered to Friedrich-Ebert-Damm 12345?”, or “Is Friedrich-Ebert-Damm 12345 your shipping street address?”, followed by multiple choices of “Yes” and “No”. Customer 114(1) can provide their input by selecting either Yes or No as an answer….[0052] In this example, service provider system 104 in step 420 receives a selection of “Yes” as an answer in step 418 in response to the recommendation provided in the prompt. Accordingly, service provider system 104 automatically updates the first one of the plurality of form fields in the first unfilled template of the checkout form. In this example, the address field in the first unfilled template of the checkout form is updated to include the address of “Friedrich-Ebert-Damm 12345”.)
communicate the at least one retrieved candidate estimate to the user device, to cause the user device to present the at least one retrieved candidate estimate in a checkout interface ([0052] In this example, service provider system 104 in step 420 receives a selection of “Yes” as an answer in step 418 in response to the recommendation provided in the prompt. Accordingly, service provider system 104 automatically updates the first one of the plurality of form fields in the first unfilled template of the checkout form. In this example, the address field in the first unfilled template of the checkout form is updated to include the address of “Friedrich-Ebert-Damm 12345”.).
However, Walters does not disclose but Van discloses obtain a geolocation estimate based on an IP address associated with a user device (“[0019] In order to meet the objects of the present invention, a method of geolocation is provided, following the invention, for an internet user whose IP address is known, allowing a geolocation point for his IP address to be determined. The method is characterised in that the internet user is assigned a geolocation on a territory corresponding to the geolocation point of his IP address…[0026] Advantageously, in an embodiment of the invention, the assignment of a geolocation to the internet user within the territory corresponding to the geolocation point of his IP address takes account of the internet user's data. These details can come from the internet service provider, such as his IP address, from a form the internet user has previously completed, from a cookie or from another source…0081] This geolocation by IP address can be made for example via a Maxmind® correspondence database (Maxmind of Waltham, Mass., USA). Following this geolocation of the internet user according to his IP address 290, said communication system will directly verify in the database comprising the advertising announcements 230 whether a local advertisement 240 can be displayed according to the geolocation of the internet user.);
At least one candidate region overlapping with an accuracy region defined about the geolocation estimate ([0016] An object of the present invention is the determination, for a point at which IP addresses are geolocated, of a territory that corresponds to it and that covers the Internet users whose IP addresses are geolocated at the said point…[0023] In one particular embodiment of the present invention, the assignment of a geolocation to the internet user within the territory corresponding to the geolocation point of his IP address is made so as to avoid an excessive density of geolocations of internet users in the regions where the territories corresponding to several geolocation points of IP addresses overlap. [0024] This embodiment of the invention resolves a problem that could arise when the territories corresponding to several geolocation points of IP addresses overlap. If the assignment of the geolocation of the internet user does not take into account that the territory corresponding to his IP address and the other internet users whose IP addresses are geolocated at the same point as him, in other words, if the assignment does not take other territories into account, the area where several territories, corresponding to several IP addresses, overlap will have a higher density of Internet users than the areas where a single territory exists. This embodiment of the invention assigns geolocations to internet users so as to avoid and excessive density of internet users…[0028] According to an embodiment of the invention, the territory corresponding to the geolocation point of the internet user's IP address is determined by comparison of geolocation data by IP address and geolocation data from at least one other method of geolocation, said data concerning several other internet users geolocated by their IP address and geolocated by at least one other method of geolocation…[0045] According to an embodiment of the invention, the territory corresponding to a geolocation point for IP addresses has the form of a circle that passes through the two Internet users most distant from one another whose IP address is geolocated at this point and who are geolocated by at least one other method of geolocation. A circular territory such as this one is particularly simple to determine.” This circular territory is defined about the Ip geolocation point (center) and extends outward to encompass the actual geographic spread of users at that IP geolocation point, directly corresponding to an accuracy region defined about the geolocation estimate, the territory’s boundary represents where users with that IP address are actually located relative to the geolocation point.” The circular regions overlap the locations of the IP addresses of the users);
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to include the limitation above as taught by Van in the teaching of Walters, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable in order to take account of the overlap of different territories corresponding to different geolocation points of IP addresses to distribute the geolocations of the internet users harmoniously (please see Van paragraph 124).
However, Walters in view of Van does not disclose but Tobkin discloses a cache and storing the obtained outputs in the cache and retrieving from the cache at least one output ([0081] Thus, the machine learning platform system 102 can apply a prediction template to prediction instances at a variety of times. Indeed, the machine learning platform system 102 can apply a prediction template during model registration, during model implementation, after model prediction, or in extracting predictions from a machine learning data repository and generating a low-latency machine learning prediction cache….[0135]… generating a low-latency machine learning prediction cache by extracting, from the machine learning data repository, a set of current state machine learning model predictions from the plurality of machine learning model predictions according to the machine learning prediction templates; and in response to receiving a query for a machine learning model prediction, providing a current state machine learning model prediction from the set of current state machine learning model predictions of the low-latency machine learning prediction cache.” The ML candidate estimate generated by Walters’s ML model for the candidate regions identified by Van are stored in Tobkin’s low-latency cache for fast retrieval).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to include the limitation above as taught by Tobkin in the teaching of Walters in view of Van, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable in order to improve distribution of current state machine learning predictions across computer networks (please see Tobkin abstract).
As per claim 7, Walters discloses wherein the geolocation estimate is obtained prior to presentation of the checkout interface on the user device (paragraph 45-49, “[0049] Service provider system 104 in step 416 generates and presents a recommendation to the user while the user is browsing the e-commerce website and before the user reaches the checkout form webpage. The recommendation would be in the form of a pop window on the top of the webpage. The recommendation would correspond to the first one of the plurality of form fields i.e. the address field of “Friedrich-Ebert-Damm 12345”. The prompt presents a question with multiple choices answer for customer 114(1) to select and provide as input. The prompt may be “Do you want this item to be delivered to Friedrich-Ebert-Damm 12345?”, or “Is Friedrich-Ebert-Damm 12345 your shipping street address?”, followed by multiple choices of “Yes” and “No”. Customer 114(1) can provide their input by selecting either Yes or No as an answer.”).
As per claim 8, Walters discloses wherein the geolocation estimate is obtained during or prior to presentation of a product page on the user device, wherein the retrieved at least one candidate estimate presented in the checkout interface is related to of a product presented on the product page (paragraph 45-49, “[0049] Service provider system 104 in step 416 generates and presents a recommendation to the user while the user is browsing the e-commerce website and before the user reaches the checkout form webpage. The recommendation would be in the form of a pop window on the top of the webpage. The recommendation would correspond to the first one of the plurality of form fields i.e. the address field of “Friedrich-Ebert-Damm 12345”. The prompt presents a question with multiple choices answer for customer 114(1) to select and provide as input. The prompt may be “Do you want this item to be delivered to Friedrich-Ebert-Damm 12345?”, or “Is Friedrich-Ebert-Damm 12345 your shipping street address?”, followed by multiple choices of “Yes” and “No”. Customer 114(1) can provide their input by selecting either Yes or No as an answer.”).
As per claim 9/18, Walters discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to obtain the one or more candidate estimates for the at least one candidate region by executing the machine learning system by: inputting to the machine learning system a set of input data including data representing the at least one candidate region; and obtaining a prediction from the machine learning system including the one or more candidate estimates ([0018] Service provider system 104 may be configured to access one or more user parameters associated with one or more of the user devices, e.g., user device 102(1). The one or more user parameters may include an IP address of user device 102(1), cookies stored on user device 102(1), and/or a fingerprint associated with user device 102(1), although any other information associated with user device 102(1) may also be included…[0044] Service provider system 104 in step 410 applies a first algorithm to predict a geolocation of the user based on analyzing the determined one or more user parameters. The first algorithm, in this example, is the machine learning algorithm, described above. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). In step 412, based on the predicted geolocation of user device 102(1), the machine learning algorithm provides recommendations for a plurality form fields associated with a checkout form.” Examiner interprets the IP address as data representing the at least one candidate region).
As per claim 11/20, Walters discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to obtain at least one candidate estimate by: retrieving, from the database, the at least one candidate estimate, the retrieved at least one candidate estimate being previously obtained using the machine learning system (paragraph 44-49).
However, Walters does not disclose but Tobkin discloses retrieving, from the cache, the at least one candidate outputs, the retrieved at least one candidate outputs being previously obtained using the machine learning system (paragraph 134-135, [0135] For example, in one or more implementations, the acts 802-808 include defining machine learning model prediction templates corresponding to a plurality of machine learning models from a defined prediction datatype; generating a machine learning data repository comprising a plurality of machine learning model predictions generated from input features utilizing the plurality of machine learning models; generating a low-latency machine learning prediction cache by extracting, from the machine learning data repository, a set of current state machine learning model predictions from the plurality of machine learning model predictions according to the machine learning prediction templates; and in response to receiving a query for a machine learning model prediction, providing a current state machine learning model prediction from the set of current state machine learning model predictions of the low-latency machine learning prediction cache.)(please see claim 1 rejection for combination rationale).
As per claim 12, Walters discloses wherein the one or more candidate estimates are one or more candidate delivery estimates, the at least one candidate region is at least one candidate delivery region, and the desired region is a desired delivery region (“[0019] The machine learning algorithm provides recommendations for one or more of the plurality of form fields based on the identified geolocation of user device 102(1). By way of example, the plurality of form fields of a checkout form may provide recommendations for one or more of the street address field, state field, zipcode field, country field. Further, the machine learning algorithm assigns scores to the recommendations based on analyzing the geolocation of the user device determined based on the one or more user parameters.” Examiner interprets, the street address field, state field, zip code field and country field as delivery estimates, the zip code as candidate region and desired delivery region).
Claim(s) 2-6 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walters (US 2021/0103975) in view of Van and Tobkin (US 2024/0119003), as disclosed in the rejection of claim 1, in further view of Luna, “Using MaxMind’s accuracy radius”, published by Maxmind Blog in 2022, hereinafter “Luna”.
As per claim 2/14, Walters discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to determine the candidate regions by: identifying, as the at least one candidate region, at least one predefined region, from a set of predefined regions ([0044] Service provider system 104 in step 410 applies a first algorithm to predict a geolocation of the user based on analyzing the determined one or more user parameters. The first algorithm, in this example, is the machine learning algorithm, described above. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). In step 412, based on the predicted geolocation of user device 102(1), the machine learning algorithm provides recommendations for a plurality form fields associated with a checkout form. The plurality of form fields of the identified unfilled template of the checkout form may include: street address field, state field, zipcode field, country field. In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be Germany and, based on the analysis of the cookies stored on user device 102(1), also predicts the geolocation of user device 102(1) to be Germany. In addition, based on analysis of the fingerprint associated with user device 102(1), predicts the geolocation to be Germany. [0045] In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be “Friedrich-Ebert-Damm 12345, 20123 Hamburg, Germany”. Further, based on analysis of the fingerprint associated with user device 102(1) predicts the geolocation to be in zipcode of 20123.” Examiner interprets the identifying of the zipcode as candidate region that is also a predefined region).
However, Walters does not disclose but Luna discloses defining the accuracy region about the geolocation estimate by using the geolocation estimate as a center of the accuracy region and an accuracy margin extending from the center of the accuracy region to define a boundary of the accuracy region and the at least one predefined region, as the at least one candidate region, that overlaps with the accuracy region (page 1, “.The mappable geolocation area data included in MaxMind’s geolocation products and services is composed of geolocation coordinates (latitude and longitude), and an accuracy radius (in kilometers). This mappable area is our most precise geolocation data because, as discussed in our previous post on accuracy, IP geolocation isn’t precise enough to put a pin on a map. Some geolocation technology (for example, GPS) may return a point, or a point with such a small accuracy radius that it can easily be treated as a point, but IP geolocation is different. When doing data analysis and building applications, working with geolocation area data is different than working with a geolocation point. If we assume we are locating IP addresses down to a narrow, fixed location, when in fact we are locating them down to a probable area of 80 square kilometers or more, we may build applications that don’t perform their intended task.”, page 2, “One of the most important things to understand about MaxMind’s geolocation data when building applications is that it returns a mappable area, not a point. It’s true that MaxMind products return a point, defined by latitude and longitude, for IP addresses, but the point is only half of the data. We also return an accuracy radius. Together, these three data points define a mappable circle in which we think the IP address is likely to be located.”, page 3, “However, we should remember that the mappable area represents the area in which we believe the IP address is likely to be located.” Luna discloses a map with a circle boundary shaded in blue with a longitude and latitude being the center of that circle boundary and a radius being the accuracy margin. Within the circle boundary lies multiple cities which represents zipcodes as predefined areas).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to include the limitation above as taught by Luna in the teaching of Walters, in order to use mappable geolocation areas rather than geolocation points in applications (please see Luna, page 4).
As per claim 3/15, Walters in view of Van, Tobkin and Luna disclose all the limitation of claim 2. Walters discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to identify the at least one predefined region that overlaps with the accuracy region by: identifying the at least one predefined region, from a set of predefined regions [0044] Service provider system 104 in step 410 applies a first algorithm to predict a geolocation of the user based on analyzing the determined one or more user parameters. The first algorithm, in this example, is the machine learning algorithm, described above. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). In step 412, based on the predicted geolocation of user device 102(1), the machine learning algorithm provides recommendations for a plurality form fields associated with a checkout form. The plurality of form fields of the identified unfilled template of the checkout form may include: street address field, state field, zipcode field, country field. In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be Germany and, based on the analysis of the cookies stored on user device 102(1), also predicts the geolocation of user device 102(1) to be Germany. In addition, based on analysis of the fingerprint associated with user device 102(1), predicts the geolocation to be Germany. [0045] In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be “Friedrich-Ebert-Damm 12345, 20123 Hamburg, Germany”. Further, based on analysis of the fingerprint associated with user device 102(1) predicts the geolocation to be in zipcode of 20123.” Examiner interprets the identifying of the zipcode as candidate region that is also a predefined region).
However, Walter does not explicitly disclose but Luna explicitly discloses that the predefined region, from a set of predefined regions, whose boundary falls within or intersects with the boundary of the accuracy region (page 3-4, the map shows the location of the user to fall within the circle boundary which consequently means that the location of the user is within a zipcode (e.g. predefined area) whose boundary falls within the circle boundary)(please see claim 2 rejection for combination rationale).
As per claim 4/16, Walters in view of Van, Tobkin and Luna disclose all the limitation of claim 2. Walters discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to identify the at least one predefined region that overlaps with the accuracy region by: identifying the at least one predefined region, from a set of predefined regions [0044] Service provider system 104 in step 410 applies a first algorithm to predict a geolocation of the user based on analyzing the determined one or more user parameters. The first algorithm, in this example, is the machine learning algorithm, described above. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). In step 412, based on the predicted geolocation of user device 102(1), the machine learning algorithm provides recommendations for a plurality form fields associated with a checkout form. The plurality of form fields of the identified unfilled template of the checkout form may include: street address field, state field, zipcode field, country field. In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be Germany and, based on the analysis of the cookies stored on user device 102(1), also predicts the geolocation of user device 102(1) to be Germany. In addition, based on analysis of the fingerprint associated with user device 102(1), predicts the geolocation to be Germany. [0045] In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be “Friedrich-Ebert-Damm 12345, 20123 Hamburg, Germany”. Further, based on analysis of the fingerprint associated with user device 102(1) predicts the geolocation to be in zipcode of 20123.” Examiner interprets the identifying of the zipcode as candidate region that is also a predefined region).
However, Walter does not explicitly disclose but Luna explicitly discloses that the at least one predefined region, from a set of predefined regions, whose representative location falls within the accuracy region (page 3-4, the map shows the location of the user to fall within the circle boundary which consequently means that the location of the user is within a zipcode (e.g. predefined area) whose boundary falls within the circle boundary)(please see claim 2 rejection for combination rationale).
As per claim 5/17, Walters in view of Van, Tobkin and Luna disclose all the limitation of claim 2. Walters does not disclose but Van discloses wherein the processing unit is configured to execute the instructions to further cause the computer system to obtain the geolocation estimate by obtaining, from a third-party service provider, the geolocation estimate (paragraph 81, “[0081] This geolocation by IP address can be made for example via a Maxmind® correspondence database (Maxmind of Waltham, Mass., USA).)(please see claim 1 rejection for combination rationale).
However, Walters in view of Van is silent but Luna explicitly discloses that the Maxmind geolocation estimates comes with the accuracy margin assigned by the third-party service provider (page 2, “One of the most important things to understand about MaxMind’s geolocation data when building applications is that it returns a mappable area, not a point. It’s true that MaxMind products return a point, defined by latitude and longitude, for IP addresses, but the point is only half of the data. We also return an accuracy radius. Together, these three data points define a mappable circle in which we think the IP address is likely to be located.”)(please see claim 2 rejection for combination rationale).
As per claim 6, Walters in view of Van, Tobkin and Luna disclose all the limitation of claim 2. Walters does not disclose but Luna further discloses wherein the accuracy margin is representative of a confidence level or accuracy of the geolocation estimate (page 2, “One of the most important things to understand about MaxMind’s geolocation data when building applications is that it returns a mappable area, not a point. It’s true that MaxMind products return a point, defined by latitude and longitude, for IP addresses, but the point is only half of the data. We also return an accuracy radius. Together, these three data points define a mappable circle in which we think the IP address is likely to be located.”, page 3, “However, we should remember that the mappable area represents the area in which we believe the IP address is likely to be located.”).
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walters (US 2021/0103975) in view of Van and Tobkin (US 2024/0119003), as disclosed in the rejection of claim 9/18, in further view of Yong Wang, “Towards Street Level Client Independent IP Geolocation”, published by ACM library in 2011, hereinafter “Wang”.
As per claim 10/19, Walters discloses executing the machine learning system to obtain at least one candidate estimate for the higher priority candidate region (paragraph 18, “webpage accessed via the web browser, and a web browser version. Service provider system 104 may include a machine learning algorithm such as a Gaussian Process Regression (GPR) or a convolution long short-term memory (LSTM) network, however any other type of intelligence and machine learning algorithm may be applied. The machine learning algorithm analyzes the one or more user parameters to predict a geolocation of user device 102(1). Based on the predicted geolocation of user device 102(1), the machine learning algorithm provides a recommendation for a plurality form fields associated with a checkout form. The plurality of form fields of a checkout form may include: street address field, state field, zipcode field, country field, etc. In this example, the machine learning algorithm analyzes the IP address of user device 102(1) by performing a reverse lookup of the IP address of user device 102(1) to identify a device through which the IP address was routed. The device through which the IP address was routed includes a cell tower, a server, a router, etc.”, paragraph 44-45, “In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be Germany and, based on the analysis of the cookies stored on user device 102(1), also predicts the geolocation of user device 102(1) to be Germany. In addition, based on analysis of the fingerprint associated with user device 102(1), predicts the geolocation to be Germany. [0045] In this example, the machine learning algorithm, based on the analysis of the IP address of user device 102(1), predicts the geolocation of user device 102(1) to be “Friedrich-Ebert-Damm 12345, 20123 Hamburg, Germany”. Further, based on analysis of the fingerprint associated with user device 102(1) predicts the geolocation to be in zipcode of 20123.)
Walters discloses that the machine learning model predicts the country and zipcode as an estimate for the IP location of the user device. However, Walters is silent on how the machine learning model determines the country and zipcode when the route of the IP address is traced through servers and routers instead of a cell tower.
Wang discloses such methodology which teaches
wherein the processing unit is configured to execute the instructions to further cause the computer system to obtain candidate estimates for two or more candidate regions by: identifying a higher priority candidate region from the two or more candidate regions; and obtain at least one candidate estimate for the higher priority candidate region prior to obtaining at least one candidate estimate for a remainder of the two or more candidate regions (Wang discloses a 3 tier drill down system to select between a plurality of zipcodes where the target IP address of user device might be located. In tier 1, Wang uses ping and traceroute servers as measurement vantage points (page 2), where each vantage point location represents a potential candidate geographic region where the target IP might be located. Wang measures the delay from each vantage point to the target and converts delays to geographic distances (pages 2-3). Vantage points with minimum measured distance to the target represent higher priority candidate regions because the target is more likely to be located near these points. “In particular, for each vantage point, we draw a ring centered at the vantage point, with a radius of the measured distance between the vantage point and the target. As we show in Section 4, this approach indeed allows us to always find a region that covers the targeted IP.” (page 3). Identifying the locations of these vantage points from among all the available zipcodes constitute the “identifying a higher priority candidate region from the two or more candidate regions”. Wang teaches using multilateration to create an intersection from distance circles around the vantage points (page 3). This intersection area, containing 257 zipcodes (page 4) constitutes the “candidate estimate for the higher priority candidate region” derived from the higher priority vantage points (those closest to the target). In this case, the multilateration will be applied by the machine learning model of Walters after the selection of the vantage points in tier 1. Once the candidate estimate for the higher priority candidate region is obtained in tier 1, the same process is repeated for Tier 2 but this time with landmark discovery before proceeding to Tier 3 where one zipcode is determined as the final location of the IP address (page 4-5, “In this final step, our goal is to complete our geolocation of the targeted IP address. We start from the region constrained in Tier 2, and aim to find all ZIP Codes in this region. To this end, we repeat the sampling procedure deployed in the Tier 2. This time from the center of the Tier 2 constrained intersection area, and at a higher granularity. In particular, we extend the radius distance by 1 km in each step, and apply a rotation angle of 10 degrees… Figure 4 shows the striking accuracy of this approach. We manage to associate the targeted IP location with a landmark which is ’across the street’, i.e., only 0.103 km distant from the target. We analyze this result in more detail below. Here, we provide the general statistics for the Tier 3 geolocation process. In this last step, we discover26 additional ZIP Codes and 203 additional landmarks in the smaller Tier 2 intersection area. We then associate the landmark, which is at ’1776K Street Northwest, Washington, DC’ and has a measured distance of 10.6 km, yet a real geographical distance of 0.103 km, with the target. T)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to include the limitation above as taught by Wang in the teaching of Walters, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable in order to escalate the use of external information and overcome many of the fundamental inaccuracies encountered in the use of absolute delay measurement (Wang, abstract).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628
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