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. 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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer. Claim s 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 5, 2-3 and 6 FILLIN "Pluralize \“Claim\” if necessary, and insert the claim number(s) of the U.S. Patent." of U.S. Patent No. 11, 710, 037 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because see the following table as outlined below . Instant Application U.S. Patent No. 11, 710, 037 B2 1 . A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform operations comprising: automatically customizing, based on a first state of a user, first content for a graphical user interface on an electronic device of the user; monitoring second activities of the user over a time period; identifying a second probability that the user has transitioned from the first state into a second state during the time period; determining when the second probability is above a second probability predefined threshold; and after determining the second probability to be above the second probability predefined threshold, automatically customizing, based on the second state of the user, second content for the graphical user interface on the electronic device of the user. 2 . The system of claim 1, wherein monitoring the second activities of the user over the time period comprises: using a mixed model comprising a mix of a Gaussian model and a second Markov model, and wherein the second state is related to the first state. 3 . The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: monitoring first activities of the user over a first time period; identifying, using a first Markov model, a first probability of the user being in the first state; determining when the first probability is above a first probability predefined threshold. 1. A system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and perform acts of: monitoring first activities of a user over a first time period; based on the first activities of the user over the first time period, identifying, using a Markov model, a first probability of the user being in a first state; determining when the first probability is above a first probability predefined threshold; in response to determining when the first probability is above the first probability predefined threshold, automatically customizing first content on a graphical user interface for the first state to create a first graphical user interface on an electronic device of the user while the user is determined to be in the first state; monitoring second activities of the user over a second time period occurring after the first time period and after the user has been determined to be in the first state and before the user has been determined to be in a second state; based on the second activities of the user over the second time period, identifying, using a mixed model different from the Markov model, a second probability that the user has transitioned from the first state into the second state, wherein the second state is related to the first state; determining when the second probability is above a second probability predefined threshold; and in response to determining when the second probability is above the second probability predefined threshold, automatically customizing a second content on the graphical user interface for the second state to create a second graphical user interface on the electronic device of the user while the user is determined to be in the second state. 4. The system of claim 3, wherein monitoring the first activities of the user over the first time period comprises: gathering information comprising at least one of: views of an item of a category of items; cart adds of the item of the category of items; registry adds of the item of the category of items; transactions involving the item of the category of items; or searches for the item of the category of items. 5. The system of claim 1, wherein monitoring the first activities of the user comprises: gathering information comprising at least one of: views of an item of a category of items; cart adds of the item of the category of items; registry adds of the item of the category of items; transactions involving the item of the category of items; or searches for the item of the category of items. 5 . The system of claim 3, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: training the first Markov model to identify the first probability of the user being in the first state comprises at least one of: identifying one or more binary classifiers, each binary classifier of the one or more binary classifiers independently capable of identifying the first probability of the user being in the first state; or identifying a multi-class classifier capable of assigning a distribution over the first probability of the user being in the first state. 2. The system of claim 1, wherein the computing instructions are further configured to perform acts of: training the Markov model to identify the first probability of the user being in the first state, wherein training the Markov model comprises: identifying one or more binary classifiers, each binary classifier of the one or more binary classifiers independently capable of identifying the first probability of the user being in the first state; or identifying a multi-class classifier capable of assigning a distribution over the first probability of the user being in the first state. 6. The system of claim 5, wherein the first Markov model is trained on a deep neural network. 3. The system of claim 2, wherein the Markov model is trained on a deep neural network. 7 . The system of claim 1, wherein automatically customizing the first content for the graphical user interface comprises: automatically changing one or more images on the graphical user interface to first images related to the first state. 8 . The system of claim 1, wherein automatically customizing the first content for the graphical user interface further comprises: automatically changing text displayed on the graphical user interface to first text related to the first state. 9 . The system of claim 1, wherein automatically customizing the first content for the graphical user interface further comprises: automatically altering a layout of the graphical user interface for the first state. 6. The system of claim 1, wherein automatically customizing the first content on the graphical user interface comprises at least one of: automatically changing one or more images on the graphical user interface to first images related to the first state; automatically changing text displayed on the graphical user interface to first text related to the first state; or automatically altering a layout of the graphical user interface for the first state. 10 . The system of claim 1, wherein the first and second states comprise life events in a sequence of life events of the user. 7. The system of claim 1, wherein the first and second states comprise life events in a sequence of life events of the user. Claims 11-19 and 20 list all similar elements of claims 1-9 and 10, but in method form rather than system form. Therefore, the supporting rationale of the rejection to claims 1 -9 and 10 applies equally as well to claims 11 -19 and 20. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1 , 7-11, 17-19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Modarresi US 2019/0273789. In regarding to claim 1 Modarresi teaches: 1. A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform operations comprising: automatically customizing, based on a first state of a user, first content for a graphical user interface on an electronic device of the user; [0060] Furthermore, the target user identification system 108 can detect additional events associated with the user 118 a . In particular, the target user identification system 108 can detect the user 118 a visiting a webpage, opening another application, accessing a website, clicking a link within a web site, purchasing a product via a web site, or some other type of event . As mentioned, the target user identification system 108 can determine features associated with each event detected for one or more client devices of a given user (e.g., user 118 a ). Accordingly, the target user identification system 108 can gather events, each event including its own event features, corresponding to the user 118 a. Modarresi , 0060-0066, emphasis added. monitoring second activities of the user over a time period; [0066] As mentioned above, in one or more embodiments, the event number threshold is a number of events that causes the user classification model 110 to converge (as described in greater detail below in relation to FIGS. 4A-4C). In some embodiments, however, the target user identification system 108 can operate using an event number threshold different than (e.g., greater than or less than) five events. Indeed, the target user identification system 108 can to receive user input (e.g., from an administrator) to set the event number threshold, or else can automatically determine an event number threshold for a particular user classification model or based on a unique set of users . Modarresi , 0066-0071, emphasis added. identifying a second probability that the user has transitioned from the first state into a second state during the time period; [0117] To elaborate, the target user identification system 108 can provide digital content by customizing an interface of a particular application (e.g., a SAAS application) with features, tools, and other attributes that the target user has previously set up for the given application. In other embodiments, the target user identification system 108 can provide digital content by prioritizing search results in a search engine interface according to previous interests of the target user (e.g., by placing links that the target user is more likely to select higher up in the results). In still other embodiments, the target user identification system 108 can provide digital content by customizing a social networking feed with content associated with other users who are linked with the target user. The target user identification system 108 can still further provide digital content by accessing files that the target user has saved during previous sessions of user activity and either transferring the files or otherwise making the files available for download to the client device associated with the target user. Modarresi , 0117 -0119 , emphasis added. determining when the second probability is above a second probability predefined threshold; [0110] Based on these probability determinations, the user classification model 110 outputs a pred icted corresponding minimum prior event user 504 . For instance, the target user identification system 108 can utilize the user classification model 110 to identify the minimum prior event user with the highest probability as the pred icted corresponding minimum prior event user 504 . To illustrate, the target user identification system 108 can compare the probability corresponding to the first minimum prior event user (e.g., 10%), the probability corresponding to the second prior event user (e.g., 50%), and the probability corresponding to the third minimum prior event user (e.g., 20%) and select the minimum prior event user with the highest probability (i.e., the second minimum prior event user) as the pred icted corresponding minimum prior event user 504 . Modarresi , 0110, emphasis added. and after determining the second probability to be above the second probability predefined threshold, automatically customizing, based on the second state of the user, second content for the graphical user interface on the electronic device of the user. [0118] As mentioned, the target user identification system 108 can provide digital content to a client device associated with a target user. Indeed, FIG. 6 illustrates an example client device 114 a associated with a target user. As shown in FIG. 6, the target user identification system 108 provides digital content to a smartphone. In particular, the target user identification system 108 provides digital content for a customized webpage for the target user. To illustrate, the target user identification system 108 provides digital content including a welcome message (“Welcome back, Bob!”) including the name (or other identifier) of the target user (“Bob”), a link to a news article of interest to the target user, daily stock performance of stocks associated with the target user, access to files that the target user has previously saved, and a link to purchase a product (e.g., glasses) of interest to the target user . Modarresi , 0117-0119, emphasis added. In regarding to claim 7 Modarresi teaches: 7. The system of claim 1, wherein automatically customizing the first content for the graphical user interface comprises: automatically changing one or more images on the graphical user interface to first images related to the first state. Modarresi , 0117-0119 and Fig. 6. In regarding to claim 8 Modarresi teaches: 8. The system of claim 1, wherein automatically customizing the first content for the graphical user interface further comprises: automatically changing text displayed on the graphical user interface to first text related to the first state. Modarresi , 0117-0119 and Fig. 6. In regarding to claim 9 Modarresi teaches: 9. The system of claim 1, wherein automatically customizing the first content for the graphical user interface further comprises: automatically altering a layout of the graphical user interface for the first state. Modarresi , 0117-0119 and Fig. 6. In regarding to claim 10 Modarresi teaches: 10. The system of claim 1, wherein the first and second states comprise life events in a sequence of life events of the user. Modarresi , 0117-0119 and Fig. 6. Claims 11, 17-19 and 20 list all similar elements of claims 1, 7-9 and 10, but in method form rather than system form. Therefore, the supporting rationale of the rejection to claims 1, 7-9 and 10 applies equally as well to claims 11, 17-19 and 20. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Modarresi US 2019/0273789 as applied to claim 1 above, and further in view of Hoffmeister US 9, 159, 319. In regarding to claim 2 Modarresi teaches: 2. The system of claim 1, wherein monitoring the second activities of the user over the time period comprises: [0117] To elaborate, the target user identification system 108 can provide digital content by customizing an interface of a particular application (e.g., a SAAS application) with features, tools, and other attributes that the target user has previously set up for the given application. In other embodiments, the target user identification system 108 can provide digital content by prioritizing search results in a search engine interface according to previous interests of the target user (e.g., by placing links that the target user is more likely to select higher up in the results). In still other embodiments, the target user identification system 108 can provide digital content by customizing a social networking feed with content associated with other users who are linked with the target user. The target user identification system 108 can still further provide digital content by accessing files that the target user has saved during previous sessions of user activity and either transferring the files or otherwise making the files available for download to the client device associated with the target user. Modarresi , 0117-0119, emphasis added. However, Modarresi fails to explicitly teach, but Hoffmeister teaches: using a mixed model comprising a mix of a Gaussian model and a second Markov model, (43) Hidden Markov Model States (44) FIG. 3 illustrates an example sequence 300 of hidden Markov model (HMM) states that may be included in a word model, such as a keyword model, a background model, and/or a competitor model. As illustrated in FIG. 3, the sequence 300 includes six HMM states: S.sub.1 through S.sub.6. While six HMM states are illustrated in FIG. 3, a word model may include any number of HMM states. As discussed above, each HMM state S.sub.1 through S.sub.6 may be represented by Gaussian mixture models (GMMs) that model distributions of feature vectors . Hoffmeister, col. 8 line 60 to col. 9 line 7, emphasis added. Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date to combine the teaching of Hoffmeister with the system of Modarresi in order using a mixed model comprising a mix of a Gaussian model and a second Markov model , as such, t he use of one or more competitor models may improve keyword spotting by creating additional points of comparison and the system reduce the number of times that a word is falsely identified as a keyword ..—Col. 3 lines 41-49 . Furthermore, Modarresi teaches: and wherein the second state is related to the first state. [0117] To elaborate, the target user identification system 108 can provide digital content by customizing an interface of a particular application (e.g., a SAAS application) with features, tools, and other attributes that the target user has previously set up for the given application. In other embodiments, the target user identification system 108 can provide digital content by prioritizing search results in a search engine interface according to previous interests of the target user (e.g., by placing links that the target user is more likely to select higher up in the results). In still other embodiments, the target user identification system 108 can provide digital content by customizing a social networking feed with content associated with other users who are linked with the target user. The target user identification system 108 can still further provide digital content by accessing files that the target user has saved during previous sessions of user activity and either transferring the files or otherwise making the files available for download to the client device associated with the target user. Modarresi , 0117-0119, emphasis added. Claim 12 list all similar elements of claim 2, but in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 2 applies equally as well to claims 12. Note: The motivation that was applied to claim 2 above, applies equally as well to claim 12 as presented blow. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claims 3 -6 and 13 -16 are rejected under 35 U.S.C. 103 as being unpatentable over Modarresi US 2019/0273789 as applied to claim 1 above, and further in view of Hoffmeister Gilman et al. US 2018/0253682. In regarding to claim 3 Modarresi teaches: 3. The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: monitoring first activities of the user over a first time period; [0117] To elaborate, the target user identification system 108 can provide digital content by customizing an interface of a particular application (e.g., a SAAS application) with features, tools, and other attributes that the target user has previously set up for the given application. In other embodiments, the target user identification system 108 can provide digital content by prioritizing search results in a search engine interface according to previous interests of the target user (e.g., by placing links that the target user is more likely to select higher up in the results). In still other embodiments, the target user identification system 108 can provide digital content by customizing a social networking feed with content associated with other users who are linked with the target user. The target user identification system 108 can still further provide digital content by accessing files that the target user has saved during previous sessions of user activity and either transferring the files or otherwise making the files available for download to the client device associated with the target user. Modarresi , 0117-0119, emphasis added. However, Modarresi fails to explicitly teach, but Gilman teaches: identifying, using a first Markov model, a first probability of the user being in the first state; [0060] The customer analytics system 158 may be configured to track and analyze user behavior and attributes. In some implementations, the customer analytics system 158 records user attributes, preferences, order contents, etc., and determines additional user details based on these elements. For instance, t he customer analytics system 158 may track the retail items in past orders of a user and, using past orders of other users and a computer learning algorithm (e.g., a neural network, a Hidden Markov Model, etc.), predict desires and actions of that user relative to other retail items and promotions . The system 100 is beneficial for applying these computer learning methods, because it increases user participation in trackable online interfaces and enables the customer analytics system 158 to provide relevant suggestions and promotions to the user. Gilman, 0060, emphasis added. Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date to combine the teaching of Gilman with the system of Modarresi in order using a mixed model comprising a mix of a Gaussian model and a second Markov model, as such, the system increases user participation in trackable online interfaces and enables the customer analytics system to provide relevant suggestions and promotions to the user.. --0060. Furthermore, Modarresi teaches: determining when the first probability is above a first probability predefined threshold. Modarresi , 0073-0075 and 0117-0119 Note: The motivation that was applied to claim 3 above, applies equally as well to claims 4-6 and 13-16 as presented blow. In regarding to claim 4 Modarresi and Gilman teaches: 4. The system of claim 3, furthermore, Modarresi teaches: wherein monitoring the first activities of the user over the first time period comprises: gathering information comprising at least one of : views of an item of a category of items; cart adds of the item of the category of items; registry adds of the item of the category of items; transactions involving the item of the category of items; or searches for the item of the category of items. Modarresi , 0117-0119 In regarding to claim 5 Modarresi and Gilman teaches: 5. The system of claim 3, furthermore, Gilman teaches: wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: training the first Markov model to identify the first probability of the user being in the first state . Gilman, 0060 furthermore, Modarresi teaches: comprises at least one of : identifying one or more binary classifiers, each binary classifier of the one or more binary classifiers independently capable of identifying the first probability of the user being in the first state; or identifying a multi-class classifier capable of assigning a distribution over the first probability of the user being in the first state. Modarresi , 0073-0075 In regarding to claim 6 Modarresi and Gilman teaches: 6. The system of claim 5, furthermore, Gilman teaches: wherein the first Markov model is trained on a deep neural network. Gilman, 0060, 0064 Claims 13-16 list all similar elements of claims 3-6, but in method form rather than system form. Therefore, the supporting rationale of the rejection to claims 3-6 applies equally as well to claims 13-16. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT DANIEL T TEKLE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1117 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8:00-4:30 ET . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT William Vaughn can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-3922 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DANIEL T TEKLE/ Primary Examiner, Art Unit 2481