18DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 8/20/2024.
Claims 1-20 are cancelled.
Claims 21-40 are added.
Claims 21-40 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on 6/13/2024 and 8/20/2024 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/11/2024 is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,039,588. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the claims of U.S. Patent No. 12,039,588.
Instant application and Patent No. 12,039,588 claim the same invention as follows:
Instant Application
Patent No. 12,039,588
21. (New) A computer implemented method, comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying an online store associated with the physical venue;
determining, with a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information;
in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and
causing the mobile computing device to present the display information.
1. A computer implemented method comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying, a first location of the mobile computing device;
determining an estimated travel time from the first location of the mobile computing device to the physical venue;
comparing the estimated travel time to a user-specific trip travel time threshold that indicates a maximum amount of time that the user of the mobile computing device prefers to travel;
determining that the estimated travel time meets or exceeds the user-specific trip travel time threshold, and in response, generating display information for presentation to the user at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access an online resource that is determined to be associated with the physical venue or that is determined to offer at least one product for sale corresponding to at least one product offered for sale at the physical venue;
causing the mobile computing device to present the display information for the user.
22. (New) The computer implemented method of claim 21, wherein the percentage likelihood is a user-specific percentage, and wherein determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage comprises:
providing, to the machine learning model, the historic user activity information;
obtaining an output from the machine learning model, the output comprising the user-specific percentage associated with a determined likelihood the user of the mobile computing device will purchase the item from the online store;
comparing the user-specific percentage to the threshold percentage; and
determining the user-specific percentage meets or exceeds the threshold percentage.
7. The method of claim 1, further comprising: accessing historic user activity information for the user; determining, using the historic user activity information for the user, an average travel time for trips taken by the user; and setting the determined average travel time as the user-specific trip travel time threshold prior to comparing the estimated travel time to the user-specific trip travel time threshold.
23. (New) The computer implemented method of claim 22, wherein the threshold percentage is at least fifty percent.
10. The method of claim 1, wherein the user-specific trip travel time threshold is determined based on user preference information provided by the user.
24. (New) The computer implemented method of claim 21, wherein the historic user activity information comprises previous visits by the user of the mobile computing device to the physical venue.
9. The method of claim 1, wherein the user-specific trip travel time threshold is determined based on historic user activity information, including information on past trips taken by the user.
25. (New) The computer implemented method of claim 21, wherein the historic user activity information comprises previous purchases by the user of the mobile computing device at the physical venue or the online store associated with the physical venue.
16. The recordable medium of claim of claim 11, wherein the display information includes an indication of one or more products available for purchase at the online resource.
26. (New) The computer implemented method of claim 21, wherein providing the historic user activity information further comprises:
identifying a location of the mobile computing device;
determining an estimated travel metric based on the location of the mobile computing device; and
providing, to the machine learning model, the estimated travel metric and the historic user activity information.
3. the first response from each of the first subset of the plurality of mobile electronic devices comprises one of:
a first response to a first prompt presented in the push notification, wherein the first response is received from one of first portion of the plurality of mobile electronic devices and the single action comprises a selection of an amount to contribute to the group gift; or
a second response to a second prompt presented in the text message, wherein the second response is received from one of second portion of the plurality of mobile electronic devices and the single action comprises a return text message indicating an amount to contribute to the group gift,.
27. (New) The computer implemented method of claim 26, wherein the estimated travel metric comprises one of:
an estimated travel distance to the physical venue from the location of the mobile computing device;
an estimated travel cost for traveling from the location of the mobile computing device to the physical venue; or
an estimated traffic value associated with predicted traffic congestion between the location of the mobile computing device and the physical venue.
1. a first location of the mobile computing device; determining an estimated travel time from the first location of the mobile computing device to the physical venue;
3. The method of claim 1, wherein the display information further includes an indication of an estimated travel cost for traveling from the first location to the physical venue.
28. (New) The computer implemented method of claim 21, wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue.
12. The recordable medium of claim of claim 11, wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue.
29. (New) The computer implemented method of claim 21, wherein the display information comprises an indication of an estimated shipping cost for having the item delivered to the user of the mobile computing device.
14. The recordable medium of claim of claim 13, wherein the display information includes an indication of an estimated shipping cost for having one or more products delivered.
30. (New) The computer implemented method of claim 21, wherein the display information comprises one or more of:
a predicted travel route to the physical venue;
an estimated travel distance to the physical venue;
an estimated travel cost for travelling to the physical venue;
a cost savings for the user of the mobile computing device associated with purchasing the item at the online store relative to the physical venue; or
a uniform resource locator (URL) associated with the online store.
7. The method of claim 1, further comprising: accessing historic user activity information for the user; determining, using the historic user activity information for the user, an average travel time for trips taken by the user; and setting the determined average travel time as the user-specific trip travel time threshold prior to comparing the estimated travel time to the user-specific trip travel time threshold.
31. (New) A tangible, non-transitory recordable medium having recorded thereon instructions, that when executed, cause performance of actions that comprise:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying an online store associated with the physical venue;
determining, by a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information;
in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and
causing the mobile computing device to present the display information.
11. A tangible, non-transitory recordable medium having recorded thereon instructions, that when executed, cause performance of actions that comprise:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying, a first location of the mobile computing device; determining an estimated travel time from the first location of the mobile computing device to the physical venue;
comparing the estimated travel time to a user-specific trip travel time threshold that indicates a maximum amount of time that the user of the mobile computing device prefers to travel;
determining that the estimated travel time meets or exceeds the user-specific trip travel time threshold, and in response, generating display information for presentation to the user at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access an online resource that is determined to be associated with the physical venue or that is determined to offer at least one product for sale corresponding to at least one product offered for sale at the physical venue;
causing the mobile computing device to present the display information for the user.
32. (New) The tangible, non-transitory recordable medium of claim 31, wherein the percentage likelihood is a user-specific percentage, and wherein determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage comprises:
providing, to the machine learning model, the historic user activity information;
obtaining an output from the machine learning model, the output comprising the user-specific percentage associated with a determined likelihood the user of the mobile computing device will purchase the item from the online store;
comparing the user-specific percentage to a threshold percentage; and
determining the user-specific percentage meets or exceeds the threshold percentage.
7. The method of claim 1, further comprising: accessing historic user activity information for the user; determining, using the historic user activity information for the user, an average travel time for trips taken by the user; and setting the determined average travel time as the user-specific trip travel time threshold prior to comparing the estimated travel time to the user-specific trip travel time threshold.
33. (New) The tangible, non-transitory recordable medium of claim 31, wherein the historic user activity information comprises previous visits by the user of the mobile computing device to the physical venue.
19. The recordable medium of claim of claim 11, wherein the user-specific trip travel time threshold is determined based on historic user activity information, including information on past trips taken by the user.
34. (New) The tangible, non-transitory recordable medium of claim 31, wherein the historic user activity information comprises previous purchases by the user of the mobile computing device at the physical venue or the online store associated with the physical venue.
16. The recordable medium of claim of claim 11, wherein the display information includes an indication of one or more products available for purchase at the online resource.
35. (New) The tangible, non-transitory recordable medium of claim 31, wherein providing the historic user activity information further comprises:
identifying a location of the mobile computing device;
determining an estimated travel metric based on the location of the mobile computing device; and
providing, to the machine learning model, the estimated travel metric and the historic user activity information.
3. the first response from each of the first subset of the plurality of mobile electronic devices comprises one of:
a first response to a first prompt presented in the push notification, wherein the first response is received from one of first portion of the plurality of mobile electronic devices and the single action comprises a selection of an amount to contribute to the group gift; or
a second response to a second prompt presented in the text message, wherein the second response is received from one of second portion of the plurality of mobile electronic devices and the single action comprises a return text message indicating an amount to contribute to the group gift,.
36. (New) The tangible, non-transitory recordable medium of claim 35, wherein the estimated travel metric comprises one of:
an estimated travel distance to the physical venue from the location of the mobile computing device;
an estimated travel cost for traveling from the location of the mobile computing device to the physical venue; or
an estimated traffic value associated with predicted traffic congestion between the location of the mobile computing device and the physical venue.
1. a first location of the mobile computing device; determining an estimated travel time from the first location of the mobile computing device to the physical venue;
3. The method of claim 1, wherein the display information further includes an indication of an estimated travel cost for traveling from the first location to the physical venue.
37. (New) The tangible, non-transitory recordable medium of claim 31, wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue.
12. The recordable medium of claim of claim 11, wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue.
38. (New) The tangible, non-transitory recordable medium of claim 31, wherein the display information comprises an indication of an estimated shipping cost for having the item delivered to the user of the mobile computing device.
14. The recordable medium of claim of claim 13, wherein the display information includes an indication of an estimated shipping cost for having one or more products delivered.
39. (New) The tangible, non-transitory recordable medium of claim 31, wherein the display information comprises one or more of:
a predicted travel route to the physical venue;
an estimated travel distance to the physical venue;
an estimated travel cost for travelling to the physical venue;
a cost savings for the user of the mobile computing device associated with purchasing the item at the online store relative to the physical venue; or
a uniform resource locator (URL) associated with the online store.
7. The method of claim 1, further comprising: accessing historic user activity information for the user; determining, using the historic user activity information for the user, an average travel time for trips taken by the user; and setting the determined average travel time as the user-specific trip travel time threshold prior to comparing the estimated travel time to the user-specific trip travel time threshold.
40. (New) A computing system, comprising:
one or more computing devices; and
one or more tangible, non-transitory recordable media having recorded thereon instructions, that when executed, cause the computing system to perform operations, the operations comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying an online store associated with the physical venue;
determining, by a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information;
in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and
causing the mobile computing device to present the display information.
11. A tangible, non-transitory recordable medium having recorded thereon instructions, that when executed, cause performance of actions that comprise:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying, a first location of the mobile computing device; determining an estimated travel time from the first location of the mobile computing device to the physical venue;
comparing the estimated travel time to a user-specific trip travel time threshold that indicates a maximum amount of time that the user of the mobile computing device prefers to travel;
determining that the estimated travel time meets or exceeds the user-specific trip travel time threshold, and in response, generating display information for presentation to the user at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access an online resource that is determined to be associated with the physical venue or that is determined to offer at least one product for sale corresponding to at least one product offered for sale at the physical venue;
causing the mobile computing device to present the display information for the user.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 40 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A computing system, comprising:
one or more computing devices; and
one or more tangible, non-transitory recordable media having recorded thereon instructions, that when executed, cause the computing system to perform operations, the operations comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying an online store associated with the physical venue;
determining, by a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information;
in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and
causing the mobile computing device to present the display information..
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, “receiving, identifying, determining, generating and present” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A computing system, comprising:
one or more computing devices; and
one or more tangible, non-transitory recordable media having recorded thereon instructions, that when executed, cause the computing system to perform operations, the operations comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue;
identifying an online store associated with the physical venue;
determining, by a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information;
in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and
causing the mobile computing device to present the display information..
.
These limitations (deemphasized) are not indicative of integration into a practical application because:
The additional elements of claim 40 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of a mobile computing device, a machine learning model, a selectable control, is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the selectable control causes the device to access an online store, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 40, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 40 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 21 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claim 31 is a tangible, non-transitory recordable medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 22-30, 32-39 are dependencies of claims 21, and 31. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
wherein providing the historic user activity information further comprises: identifying a location of the mobile computing device; determining an estimated travel metric based on the location of the mobile computing device; and providing, to the machine learning model, the estimated travel metric and the historic user activity information. (no details are recited regarding how the identifying is performed or provides integration into a practical application)
wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue.. (only generally links the abstract idea to a technological environment)
wherein the display information comprises one or more of: a predicted travel route to the physical venue; an estimated travel distance to the physical venue; an estimated travel cost for travelling to the physical venue; a cost savings for the user of the mobile computing device associated with purchasing the item at the online store relative to the physical venue; or a uniform resource locator (URL) associated with the online store.. (sales activities or behaviors, only generally links the abstract idea to a technological environment)
the sender or a recipient of an order is not restricted from and may create and share orders with one or more recipients or a group of recipients at the same time or at different times, via the personal virtual shopping cart. (managing personal behavior or interactions between people, transmitting data over a network, advertising marketing or sales activities or behaviors)
the personal virtual shopping cart may be presented within an application or through a web browser on any device which can operate interactively and autonomously, and which is connected to the proprietary platform. (transmitting data over a network, further limiting the device, only generally linking the abstract idea to a technological environment)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
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 of this title, 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 21-22, 24-40 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication No. 2016/0210682 A1 to KANNAN in view of U.S. Patent No. 10546326 B2 to DESOUZA.
Regarding Claim 21, KANNAN and DESOUZA teach a computer implemented method, comprising:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue; ([0024] In addition to the physical location, other attributes, such as direction of motion, velocity, acceleration, etc. are also considered part of the geolocation and can be used in connection with a prediction platform 17 to customize an in-store retail experience. For example, the user may be offered personalized discount offer messages on his smart phone through SMS or a native app, based on items located in the vicinity of the customer.)
identifying an online store associated with the physical venue; ([0031] A nexus between the user location, the user's online or other activities, and stores at or near the user's location is found (106). As an example, assume a user has been browsing online for toys, and his physical location is close to a toy store. )
determining, with a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information; ([0032] a purchase propensity model can be built using statistical and machine learning algorithms such as, Naïve Bayes, decision trees, random forests, support vector machines, and the like. Cross-sell models may also be built using market basket analysis to identify other products and services that may be offered to the customer. [0017] For example, consider the use case of modeling likelihood to purchase in-store versus online, where user-related information includes but is not limited to web pages browsed, operating system, time of site, time spent on individual pages, number of product pages browsed, etc., and these variables are linked with variables that are based on the user's physical location to calculate proximity to nearest store, and are further used as a combined set of variables to model the likelihood to purchase in-store versus online. The data for several consumers can run into several gigabytes, and machine learning techniques such as, logistic regression, support vector machines, decision trees, random forests, Naïve Bayes, etc. may be applied to build the model, and subsequently, execute the model.)
KANNAN does not explicitly disclose in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information.
.
DESOUZA, on the other hand, teaches in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information. ([0009] the offer or sale of goods and/or services itself, such as a sale on various items at a brick-and-mortar store, which items one can also purchase online. [0012] wherein the user, once he has "unlocked" a given online store, can thereafter access that online store and purchase goods and/or services, whether or not the user is at that time in proximity to an event associated with the online store. [0057] 22. Buy now button)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN, the features as taught by DESOUZA, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Regarding Claim 22, KANNAN and DESOUZA teach the method of claim 21.
KANNAN further discloses wherein the percentage likelihood is a user-specific percentage, and wherein determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage comprises: providing, to the machine learning model, the historic user activity information; ([0021] one can identify the recency and frequency of purchases online and at the store from the recorded history of previous transactions of the user. ) obtaining an output from the machine learning model, the output comprising the user-specific percentage associated with a determined likelihood the user of the mobile computing device will purchase the item from the online store; comparing the user-specific percentage to the threshold percentage; ([0017] these variables are linked with variables that are based on the user's physical location to calculate proximity to nearest store, and are further used as a combined set of variables to model the likelihood to purchase in-store versus online. ) and determining the user-specific percentage meets or exceeds the threshold percentage. ([0021] If the customer has interacted over chat or IVR within the past few days, his likely intent for visiting the store may also be known. Based on pages browsed or the previous interaction history, embodiments of the invention can also discover the degree of interest in discounts and offers. Profiling is the process that refers to construction of a profile via the extraction from a set of data.)
Regarding Claim 24, KANNAN and DESOUZA teach the method of claim 21.
KANNAN further discloses wherein the historic user activity information comprises previous visits by the user of the mobile computing device to the physical venue. ([0021] For example, one can identify the recency and frequency of purchases online and at the store from the recorded history of previous transactions of the user.)
Regarding Claim 25, KANNAN and DESOUZA teach the method of claim 21.
KANNAN further discloses wherein the historic user activity information comprises previous purchases by the user of the mobile computing device at the physical venue or the online store associated with the physical venue. ([0021] For example, one can identify the recency and frequency of purchases online and at the store from the recorded history of previous transactions of the user.)
Regarding Claim 26, KANNAN and DESOUZA teach the method of claim 21.
KANNAN further discloses wherein providing the historic user activity information further comprises: identifying a location of the mobile computing device; ([0023] the device is active and, as such, the location of the device is known through use of geolocation techniques. ) determining an estimated travel metric based on the location of the mobile computing device; and providing, to the machine learning model, the estimated travel metric and the historic user activity information. ([0024] In addition to the physical location, other attributes, such as direction of motion, velocity, acceleration, etc. are also considered part of the geolocation and can be used in connection with a prediction platform 17 to customize an in-store retail experience. For example, the user may be offered personalized discount offer messages on his smart phone through SMS or a native app, based on items located in the vicinity of the customer. Alternately, personalized ads can be screened in-store depending on the users buying behavior, and best discount offers on items located in the vicinity of the customer. [0032] a purchase propensity model using various variables such as, demographic information, current and/or historic travel pattern, online web behavior, e.g. pages visited, time on site, time on page, text searches, etc. Such a purchase propensity model can be built using statistical and machine learning algorithms)
Regarding Claim 27, KANNAN and DESOUZA teach the method of claim 26.
KANNAN further discloses wherein the estimated travel metric comprises one of: an estimated travel distance to the physical venue from the location of the mobile computing device; an estimated travel cost for traveling from the location of the mobile computing device to the physical venue; or an estimated traffic value associated with predicted traffic congestion between the location of the mobile computing device and the physical venue. ([0038] The proximity of the customer to the product of interest may be calculated based on the distance between the geolocation and the location of the products available from the store database or through geolocation of the product available from a device attached to the product or the shelf/storage space in the store. When the customer distance is within certain minimum distance from the product, the customer may be presented with a special deal on the cellphone native app. [0005] determine real-time roadway traffic conditions.)
Regarding Claim 28, KANNAN and DESOUZA teach the method of claim 21.
KANNAN further discloses wherein the display information is displayed as part of a map display, the map display including an indication of a location of the physical venue. ([0026] For either geolocating or positioning, the locating engine often uses radio frequency (RF) location methods, for example Time Difference Of Arrival (TDOA) for precision. TDOA systems often use mapping displays or other geographic information systems.)
Regarding Claim 29, KANNAN and DESOUZA teach the method of claim 21.
KANNAN does not explicitly disclose wherein the display information comprises an indication of an estimated shipping cost for having the item delivered to the user of the mobile computing device.
DESOUZA teaches wherein the display information comprises an indication of an estimated shipping cost for having the item delivered to the user of the mobile computing device. ([0061] 26. Shipping information. [0128] The screen shown in FIG. 14 displays Item for purchase 24, Credit card information 25, Shipping Information 26, and Item cost 27. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN, the features as taught by DESOUZA, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Regarding Claim 30, KANNAN and DESOUZA teach the method of claim 21.
KANNAN does not explicitly disclose wherein the display information comprises one or more of: a predicted travel route to the physical venue; an estimated travel distance to the physical venue; an estimated travel cost for travelling to the physical venue; a cost savings for the user of the mobile computing device associated with purchasing the item at the online store relative to the physical venue; or a uniform resource locator (URL) associated with the online store..
DESOUZA teaches wherein the display information comprises one or more of: a predicted travel route to the physical venue; an estimated travel distance to the physical venue; an estimated travel cost for travelling to the physical venue; a cost savings for the user of the mobile computing device associated with purchasing the item at the online store relative to the physical venue; or a uniform resource locator (URL) associated with the online store.. ([0139] the screen shown in FIG. 21 appears. The system can pre-populate a share text display area 37 with text that promotes the offer, for example with an Internet link to the offer,)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN, the features as taught by DESOUZA, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Regarding Claim 31, KANNAN and DESOUZA teach A tangible, non-transitory recordable medium having recorded thereon instructions, that when executed, cause performance of actions that comprise:
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue; ([0024] In addition to the physical location, other attributes, such as direction of motion, velocity, acceleration, etc. are also considered part of the geolocation and can be used in connection with a prediction platform 17 to customize an in-store retail experience. For example, the user may be offered personalized discount offer messages on his smart phone through SMS or a native app, based on items located in the vicinity of the customer.)
identifying an online store associated with the physical venue; ([0031] A nexus between the user location, the user's online or other activities, and stores at or near the user's location is found (106). As an example, assume a user has been browsing online for toys, and his physical location is close to a toy store. )
determining, with a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information; ([0032] a purchase propensity model can be built using statistical and machine learning algorithms such as, Naïve Bayes, decision trees, random forests, support vector machines, and the like. Cross-sell models may also be built using market basket analysis to identify other products and services that may be offered to the customer. [0017] For example, consider the use case of modeling likelihood to purchase in-store versus online, where user-related information includes but is not limited to web pages browsed, operating system, time of site, time spent on individual pages, number of product pages browsed, etc., and these variables are linked with variables that are based on the user's physical location to calculate proximity to nearest store, and are further used as a combined set of variables to model the likelihood to purchase in-store versus online. The data for several consumers can run into several gigabytes, and machine learning techniques such as, logistic regression, support vector machines, decision trees, random forests, Naïve Bayes, etc. may be applied to build the model, and subsequently, execute the model.)
KANNAN does not explicitly disclose in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information.
.
DESOUZA, on the other hand, teaches in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information. ([0009] the offer or sale of goods and/or services itself, such as a sale on various items at a brick-and-mortar store, which items one can also purchase online. [0012] wherein the user, once he has "unlocked" a given online store, can thereafter access that online store and purchase goods and/or services, whether or not the user is at that time in proximity to an event associated with the online store. [0057] 22. Buy now button)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN, the features as taught by DESOUZA, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Claim 32 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 22. The claim is rejected under substantially similar grounds as claim 22.
Claim 33 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 24. The claim is rejected under substantially similar grounds as claim 24.
Claim 34 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 25. The claim is rejected under substantially similar grounds as claim 25.
Claim 35 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 26. The claim is rejected under substantially similar grounds as claim 26.
Claim 36 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 27. The claim is rejected under substantially similar grounds as claim 27.
Claim 37 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 28. The claim is rejected under substantially similar grounds as claim 28.
Claim 38 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 29. The claim is rejected under substantially similar grounds as claim 29.
Regarding Claim 40, KANNAN and DESOUZA teach A computing system, comprising: one or more computing devices; and one or more tangible, non-transitory recordable media having recorded thereon instructions, that when executed, cause the computing system to perform operations, the operations comprising ([0052]):
receiving, from a mobile computing device, information indicating that a user of the mobile computing device intends to travel to a physical venue; ([0024] In addition to the physical location, other attributes, such as direction of motion, velocity, acceleration, etc. are also considered part of the geolocation and can be used in connection with a prediction platform 17 to customize an in-store retail experience. For example, the user may be offered personalized discount offer messages on his smart phone through SMS or a native app, based on items located in the vicinity of the customer.)
identifying an online store associated with the physical venue; ([0031] A nexus between the user location, the user's online or other activities, and stores at or near the user's location is found (106). As an example, assume a user has been browsing online for toys, and his physical location is close to a toy store. )
determining, with a machine learning model, that a percentage likelihood that the user of the mobile computing device will purchase an item from the online store meets or exceeds a threshold percentage, the percentage likelihood being based at least in part on historic user activity information; ([0032] a purchase propensity model can be built using statistical and machine learning algorithms such as, Naïve Bayes, decision trees, random forests, support vector machines, and the like. Cross-sell models may also be built using market basket analysis to identify other products and services that may be offered to the customer. [0017] For example, consider the use case of modeling likelihood to purchase in-store versus online, where user-related information includes but is not limited to web pages browsed, operating system, time of site, time spent on individual pages, number of product pages browsed, etc., and these variables are linked with variables that are based on the user's physical location to calculate proximity to nearest store, and are further used as a combined set of variables to model the likelihood to purchase in-store versus online. The data for several consumers can run into several gigabytes, and machine learning techniques such as, logistic regression, support vector machines, decision trees, random forests, Naïve Bayes, etc. may be applied to build the model, and subsequently, execute the model.)
KANNAN does not explicitly disclose in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information.
DESOUZA, on the other hand, teaches in response to determining that the percentage likelihood that the user of the mobile computing device will purchase the item from the online store meets or exceeds the threshold percentage, generating display information for presentation at the mobile computing device, the display information including a selectable control that, when selected, causes the mobile computing device to access the online store; and causing the mobile computing device to present the display information. ([0009] the offer or sale of goods and/or services itself, such as a sale on various items at a brick-and-mortar store, which items one can also purchase online. [0012] wherein the user, once he has "unlocked" a given online store, can thereafter access that online store and purchase goods and/or services, whether or not the user is at that time in proximity to an event associated with the online store. [0057] 22. Buy now button)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN, the features as taught by DESOUZA, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication No. 2016/0210682 A1 to KANNAN in view of U.S. Patent No. 10546326 B2 to DESOUZA in view of U.S. Patent Application No. 2017/0300948 A1 to CHAUHAN.
Regarding Claim 23, KANNAN and DESOUZA teach the method of claim 22.
But does not explicitly disclose wherein the threshold percentage is at least fifty percent..
CHAUHAN, on the other hand, teaches wherein the threshold percentage is at least fifty percent. ([0044] The predicted value or score Â.sub.c,i(t) is then a rank schema that the likelihood of future purchases can be measured. For example, a consumer with a higher score, for example, 0.8 (or 80% likely to purchase), is considered more likely to purchase than a customer with a lower score, for example, 0.5 (or 50% likely to purchase).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KANNAN AND DESOUZA, the features as taught by CHAUHAN, 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. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KANNAN, to include the teachings of DESOUZA, in order to provide tailored products and services via an online store (DESOUZA, [0008]).
Conclusion
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689