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 .
Status of Claims
Applicant's “Amendment” filed on 5/19/2026 has been considered.
Rejection to Claims 21, 23-25, 28-31, 33-34, 37-40 under 35 USC 101 have not been overcome.
Rejection to Claims 21, 23-25, 28-31, 33-34, 37-40 under nonstatutory double patenting have not been overcome.
Claims 21, 23, 31, 40 are amended.
Claims 1-20, 22, 26-27, 32, 35-36 are cancelled.
Claims 21, 23-25, 28-31, 33-34, 37-40 are currently pending and have been examined.
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, 23-25, 28-31, 33-34, 37-40 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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising 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;
providing, to a machine learning model, historic user activity information and the estimated travel metric;
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user-specific percentage that is based at least in part on the historic user activity information;
determining, based on the output obtained from the machine learning model, that the percentage likelihood meets or exceeds a threshold percentage;
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.
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.
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. The computer implemented method of claim 21, 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. 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. 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.
28. 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. 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. 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. 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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising 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;
providing, to a machine learning model, historic user activity information and the estimated travel metric;
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user-specific percentage that is based at least in part on the historic user activity information;
determining, based on the output obtained from the machine learning model, that the percentage likelihood meets or exceeds a threshold percentage;
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.
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.
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. 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. 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.
37. 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. 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. 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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising 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;
providing, to a machine learning model, historic user activity information and the estimated travel metric;
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user-specific percentage that is based at least in part on the historic user activity information;
determining, based on the output obtained from the machine learning model, that the percentage likelihood meets or exceeds a threshold percentage;
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.
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.
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.
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, 23-25, 28-31, 33-34, 37-40 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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising 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;
providing, to a machine learning model, historic user activity information and the estimated travel metric;
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user- specific percentage that is based at least in part on the historic user activity information;
determining, based on the output obtained from the machine learning model, that the percentage likelihood meets or exceeds a threshold percentage 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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising 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;
providing, to a machine learning model, historic user activity information and the estimated travel metric;
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user- specific percentage that is based at least in part on the historic user activity information;
determining, based on the output obtained from the machine learning model, that the percentage likelihood meets or exceeds a threshold percentage 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 23-25, 28-30, 33-34, 37-39 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, 24-25, 28, 31, 33-34, 37, 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 US 2017/0061480 A1 to Zhou.
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 an estimated travel metric based on a location of the mobile computing device, the estimated travel metric comprising one of:
an estimated travel distance to the physical venue from the location of the mobile computing device; ([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.)
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;
providing, to a machine learning model, historic user activity information and the estimated travel metric; ([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. ([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)
obtaining an output from the machine learning model, the output comprising a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue, the percentage likelihood being a user-specific percentage that is based at least in part on the historic user activity information; ([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 determining, based on the output obtained from the machine learning model, that a percentage likelihood exceeds a threshold percentage; 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.
Zhou, on the other hand, teaches determining, based on the output obtained from the machine learning model, that a percentage likelihood exceeds a threshold percentage; 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. ([0032] Spending features 226 may also include metrics related to the customer's historic and/or projected spending on one or more products [0045] may select a field channel for a customer if the customer's likelihood 216 of converting through the field channel is higher than threshold 232 and the customer's CLV in the field channel is higher than the customer's CLV in other acquisition channels. If likelihood 216 does not exceed threshold 232 and/or the CLV for the field channel is not higher than the CLV for another acquisition channel (e.g., an online channel), the other acquisition channel may be selected [0054] if the customer's likelihood of purchasing the product through a field channel does not exceed the threshold, an online channel may be selected for the user to reserve limited field channel resources for targeting of customers with higher likelihood of purchasing the product through the field channel. [0055] If the likelihood exceeds the threshold, the set of features is used to identify CLVs for the customer(s) through the first and second channels [0056] the selected acquisition channel may be displayed in a user interface with identifying information for the customer and/or stored with data for the customer in a data repository (e.g., data repository 134 of FIG. 1). Additional data that may be displayed and/or stored with the acquisition channel may include the customer's likelihood of purchasing the product through the acquisition channel, the customer's estimated annual spending through the acquisition channel, and/or the customer's CLV through the acquisition channel. [0060] the CLV may be multiplied by the customer's likelihood of purchasing the product through the acquisition channel to obtain an “expected CLV” for the customer.)
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 Zhou, 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 Zhou, in order to select marketing channels for customers (Zhou, [0002]).
Regarding Claim 24, KANNAN and Zhou 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 Zhou 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 28, KANNAN and Zhou 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.)
Claim 31 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 21. The claim is rejected under substantially similar grounds as claim 21.
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 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.
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, ([0052]) the operations comprising substantially similar limitations as claim 21. The claim is rejected under substantially similar grounds as claim 21.
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 US 2017/0061480 A1 to Zhou in view of U.S. Patent Application No. 2017/0300948 A1 to CHAUHAN.
Regarding Claim 23, KANNAN and Zhou teach the method of claim 21.
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 Zhou, 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 CHAUHAN, in order to provide tailored products and services via an online store (CHAUHAN, [0008]).
Claim 29-30, 38-39 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication No. 2016/0210682 A1 to KANNAN in view of US 2017/0061480 A1 to Zhou in view of U.S. Patent No. 10546326 B2 to DESOUZA.
Regarding Claim 29, KANNAN and Zhou 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, on the other hand, 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 Zhou 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]).
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.
Claim 39 recites a tangible, non-transitory recordable medium comprising substantially similar limitations as claim 30. The claim is rejected under substantially similar grounds as claim 30.
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under double patenting have been fully considered but they are not persuasive.
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the amended claims provide a targeted technical solution to a computer resource problem.
Examiner disagrees. The specification at [0009-0010] discuss advantages including saving memory storage resources and network resources by only transmitting customized content when one or more objective criteria have been satisfied. It suggests that this reduces the need to build additional physical communication infrastructure or server farms for storage of customized content to provide service to additional users. It further suggests that allowing users to avoid traveling to physical store locations reduces demand on physical transportation infrastructure leading to reduced traffic congestions and crowding on public transportation systems.
However, allowing users to avoid traveling is not a technically rooted problem.
Regarding only transmitting customized content when criteria are satisfied: If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible (see MPEP 2106.05(a)).
Applicant’s specification provides the following regarding how to save memory storage resources and network resources:
“The computing system that includes the computing device 100 can increase efficiency, reduce use of communication resources (thereby freeing up communication resources for other purposes), reduce memory storage requirements, and reduce processing requirements by determining that the suggestion 114 should only be generated and presented to the user in response to determined criteria being satisfied. For example, by only generating and presenting the suggestion 114 if one or more determined criteria are satisfied (as described above), the total number of suggestions that are generated and communicated to users is decreased. This avoids using processing, memory, and communication resources by only using such resources when the computing system has determined that a particular user is likely to want to view a particular suggestion 114, rather than providing such suggestions to all users of a system and random or indiscriminate points in time. The solution presented here reduces the overall burden on the communications network, memory requirements for the computing system (including memory requirements for the computing device 100) and processing resources thereby freeing up these resources for use by other applications and for other purposes, and also increasing the ability to scale such a system to include a greater number of end user computing devices without increasing the capacity of the system.” (Spec, [0049]) and
“By generating the customized suggestion 114 only at the time that one or more determined criteria has been satisfied, the computing system reduces memory storage requirements. Rather than generating potentially millions of customized suggestions prior to times that the suggestions are to be presented to users, the computing system generates the customized suggestion 114 only at the time that computing system has determined that the customized suggestion 114 should be presented to the user of the computing device 100. This alleviates the need to store large quantities of pre-generated customized suggestions by generating the suggestions "on the fly'' as they are needed for presentation to the user. Thus, the system described herein saves memory storage resources which are then freed up for use by other applications and for other purposes.” (Spec, [0057])
Similar to Affinity Labs, the specification fails to provide details regarding the manner in which the permitting user access is actually accomplished, and there is no indication that generating suggestions “on the fly” improves the functioning of a computer or a technical field. The claim invokes computers or other machinery merely as a tool to perform an existing process. "Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection, necessitated by Applicant’s amendment.
Applicant argues that Kannan fails to disclose determining an estimated travel metric based on a location of the mobile device.
Examiner disagrees. Kannan discloses 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 at [0038]. Kannan also discloses determining real-time roadway traffic conditions.
Applicant further argues that Kannan fails to disclose providing to a machine learning model, historic user activity information and the estimated travel metric,
Examiner disagrees. Kannan discloses one can identify the recency and frequency of purchases online and at the store from the recorded history of previous transactions of the user at [0021]. 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 at [0024]. Kannan further discloses 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 at [0032].
Applicant further argues that Kannan fails to disclose obtaining an output from the machine learning model that includes a percentage likelihood that the user of the mobile computing device will purchase an item from the online store associated with the physical venue.
Examiner disagrees. Kannan discloses 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 at [0017].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 11am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
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