DETAILED ACTION
This final Office action is responsive to amendments filed March 2nd, 2026. Claims 1, 7, 10, 16, and 19 have been amended. Claims 1-20 are presented for examination.
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 .
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 03/02/26 have been fully considered but they are not persuasive.
On pages 16-18 of the provided remarks, Applicant argues that the amended claims present statutory subject matter. Specifically, on page 17 of the provided remarks, Applicant compares the amended claim limitations to analysis of Example 37 of 2019 Subject Matter Eligibility Examples (SMEE) and argues “information about how frequently the user interacted with content candidates (i.e., icons) from each subset (i.e., from each content category) is used to place each subset of content candidates (i.e., each subset of icons) at specific positions of the user interface such that the first subset of content candidates (i.e., the first subset of icons) with which the user interacted the most frequently is placed at a first row (i.e., top position) of the user interface. This provides for improved user's interaction with the user interface as more frequently used items (i.e., icons) are more prominently displayed, which results in an improved user interface for electronic devices.” Examiner respectfully disagrees and asserts that the claimed display of content in a user interface is not analogous to the rearrangement of icons in Example 37. Per the 2019 SMEE, “the additional elements recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems”. The usage determination involved the processor “tracks the number of times each icon is selected or how much memory has been allocated to the individual processes associated with each icon over a period of time (e.g., day, week, month, etc.)”. This determination provided a “non-typical” arrangement of icons which varied from the standard “alphabetically, by file size, and by file type”. This technical problem of Example 37 is not analogous to the as-filed Specification recitation of “a retailer may select to place content associated with certain items available from the retailer more prominently due to promotional incentives available to the retailer”. This identified problem is not technical in comparison to the improvement of Example 37. Therefore, the 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 18-20, filed 03/02/26, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Battisti (U.S 9,911,130 B1) in view of Ramer (U.S 2007/0094042 A1) in view of Tan (U.S 2011/013077 A1) in view of Tsoy (U.S 2022/0327134 A1).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (method), 10 (non-transitory computer-readable medium), and 19 (system) and dependent claims 2-9, 11-18, and 20, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process), claim 10 is directed to a non-transitory computer-readable medium (i.e. manufacture), and claim 20 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claims recite receiving, via a network and from a device associated with a user of the computer system, a request for generation of a user interface of the computer system; responsive to receiving the request, obtaining retailer-specified preferences for placements of a plurality of content candidates in a plurality of placement positions of the user interface, the retailer-specified preferences corresponding to a preconfigured version of the user interface; responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indicated about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives from a supplier of the items; applying a machine learning model of the computer system to the retailer-specified preferences and the contextual data including the indication about the current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, the sale status of the items, and the promotional incentives to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions, wherein the machine learning model is trained on historical data of the computer system to predict the ranking score for each of the plurality of content candidates, the ranking score indicative of a respective value of a performance metric associated with operation of the computer system, the respective value of the performance metric achieved when each of the plurality of content candidates is selected by the user; generating, using the ranking score for each of the plurality of content candidates, the respective placement position of the plurality of placement positions for each of the plurality of content candidates in the user interface, wherein the plurality of placement positions differ from the retailer-specified preferences, and wherein the user interface differs from the preconfigured version of the user interface; sending, via the network, the user interface to the device associated with the user, wherein sending the user interface causes the device associated with the user to display the user interface with each of the plurality of content candidates at the respective placement position in the user interface, wherein displaying the user interface different from the preconfigured version of the user interface comprises: displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface, the first subset of content candidates including content candidates that correspond to a first content category, and each content candidate of the first subset of content candidates having a respective ranking score that is higher than any ranking score of a content candidate of the plurality of content candidates excluding the first subset of content candidates, and each content candidate of the first subset of content candidates representing an item with which the user interacted more frequently than with any of the plurality of content candidates excluding the first subset of content candidates, and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row, the second subset of content candidates including content candidates that correspond to a second content category, and each content candidate from the second subset of content candidates having a respective ranking score that is less than a ranking score of each content candidate from the first subset of content candidates; receiving, via the network and from the device associated with the user, information about interactions of the user with the plurality of content candidates placed at the plurality of placement positions of the user interface and retraining the machine learning model by updating a set of parameters of the machine learning model using the information about the interactions (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are using retailer-specified preferences for placements of a plurality of content candidates in placement positions of the user interface to generate, based on the ranking scores, respective placements of the candidate content in the user interface, which is a commercial interaction in the form of marketing. The Applicant’s claimed limitations are generating, based on the ranking scores, respective placements of the candidate content in the user interface, which recite the abstract idea of Certain Methods of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are using retailer-specified preferences for a plurality of content candidates in placement positions of the user interface to generate a ranking score for each of the candidate content for the placement positions; predicting the ranking score for each of the plurality of content candidates, the ranking score indicative of a respective value of a performance metric associated with operation of the computer system and generating, using the ranking score for each of the plurality of content candidates, the respective placement position for each of the plurality of content candidates in the user interface, which are functions of the human mind in the form of observation, judgment, and evaluation. The Applicant’s claimed limitations are using retailer-specified preferences for a plurality of content candidates in placement positions of the user interface to generate a ranking score for each of the candidate content for the placement positions; predicting the ranking score for each of the plurality of content candidates, the ranking score indicative of a respective value of a performance metric associated with operation of the computer system and generating, using the ranking score for each of the plurality of content candidates, the respective placement position for each of the plurality of content candidates in the user interface, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-9, 11-18, and 20 further narrow the abstract idea and recite further defining the candidate content including retailer-curated content specific to the retailer; adding items available from the retailer to an order for a user; promotional data; product carousels that include a set of item elements; deriving the performance metric; facilitating the processing of an order to procure one or more items by assigning the order to a picker that is a fully-autonomous robot; and the placements. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as marketing and sales activity as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “receiving, via a network and from a device associated with a user of the computer system, a request for generation of a user interface of the computer system; responsive to receiving the request, obtaining retailer-specified preferences for placements of a plurality of content candidates in a plurality of placement positions of the user interface, the retailer-specified preferences corresponding to a preconfigured version of the user interface; responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, the sale status of the items, and the promotional incentives from a supplier of the items; sending the user interface to the device associated with the user, wherein sending the user interface causes the device associated with the user to display the user interface with each of the plurality of content candidates at the respective placement position in the user interface, wherein displaying the user interface different from the preconfigured version of the user interface comprises: displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface, the first subset of content candidates including content candidates that correspond to a first content category, and each content candidate of the first subset of content candidates having a respective ranking score that is higher than any ranking score of a content candidate of the plurality of content candidates excluding the first subset of content candidates, and each content candidate of the first subset of content candidates representing an item with which the user interacted more frequently than with any of the plurality of content candidates excluding the first subset of content candidates, and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row, the second subset of content candidates including content candidates that correspond to a second content category, and each content candidate from the second subset of content candidates having a respective ranking score that is less than a ranking score of each content candidate from the first subset of content candidates; receiving, via the network and from the device associated with the user, information about interactions of the user with the plurality of content candidates placed at the plurality of placement positions of the user interface” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “a computer system comprising a processor and a computer-readable medium; a network; a device associated with a user of the computer system; a user interface; a fully-autonomous robot; A non-transitory computer-readable storage medium storing instructions executable by a processor for performing steps; A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions executable by the processor for performing step” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 1, 10, and 19 recite the following limitation, “applying a machine learning model”; “the machine learning model is trained on historical data of the computer system”; and “retraining the machine learning model by updating a set of parameters of the machine learning model using the information about the interactions”. The “the machine learning model is trained on historical data of the computer system” and “retraining the machine learning model by updating a set of parameters of the machine learning model using the information about the interactions” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-9, 11-18, and 20 further narrow the abstract idea and dependent claims 2-4, 7, 9, 11-13, 16, 18, and 20 additionally recite “obtaining a combination of retailer-curated content specified by a retailer and system-curated content generated automatically without input from the retailer”; “obtaining one or more digital banners that include links to respective landing pages”; “obtaining one or more product carousels”; “receiving, via the user interface, a selection of one or more items for adding to an order of the user operating the device”; “obtaining at least one of a profile of the user operating the device, a season” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “the user interface” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “a computer system comprising a processor and a computer-readable medium; a network; a device associated with a user of the computer system; a user interface; a fully-autonomous robot; A non-transitory computer-readable storage medium storing instructions executable by a processor for performing steps; A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions executable by the processor for performing step” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1-9; non-transitory computer-readable medium claims 10-18; and System claims 19-20 recite “a computer system comprising a processor and a computer-readable medium; a network; a device associated with a user of the computer system; a user interface; A non-transitory computer-readable storage medium storing instructions executable by a processor for performing steps; A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions executable by the processor for performing step”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0082 and Figures 1, 2, & 4. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, via a network and from a device associated with a user of the computer system, a request for generation of a user interface of the computer system; responsive to receiving the request, obtaining retailer-specified preferences for placements of a plurality of content candidates in a plurality of placement positions of the user interface, the retailer-specified preferences corresponding to a preconfigured version of the user interface; responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, the sale status of the items, and the promotional incentives from a supplier of the items; sending the user interface to the device associated with the user, wherein sending the user interface causes the device associated with the user to display the user interface with each of the plurality of content candidates at the respective placement position in the user interface, wherein displaying the user interface different from the preconfigured version of the user interface comprises: displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface, the first subset of content candidates including content candidates that correspond to a first content category, and each content candidate of the first subset of content candidates having a respective ranking score that is higher than any ranking score of a content candidate of the plurality of content candidates excluding the first subset of content candidates, and each content candidate of the first subset of content candidates representing an item with which the user interacted more frequently than with any of the plurality of content candidates excluding the first subset of content candidates, and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row, the second subset of content candidates including content candidates that correspond to a second content category, and each content candidate from the second subset of content candidates having a respective ranking score that is less than a ranking score of each content candidate from the first subset of content candidates; receiving, via the network and from the device associated with the user, information about interactions of the user with the plurality of content candidates placed at the plurality of placement positions of the user interface” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a ranking score does not add significantly more to the claim.
In addition, claims 2-9, 11-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2-4, 7, 9, 11-13, 16, 18, and 20 additionally recite “obtaining a combination of retailer-curated content specified by a retailer and system-curated content generated automatically without input from the retailer”; “obtaining one or more digital banners that include links to respective landing pages”; “obtaining one or more product carousels”; “receiving, via the user interface, a selection of one or more items for adding to an order of the user operating the device”; “obtaining at least one of a profile of the user operating the device, a season” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “user interface” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 6, 8-12, 15, and 17-20 is/are rejected under 35 U.S.C. 103 is/are rejected under 35 U.S.C. 103 as being unpatentable over Battisti (U.S 9,911,130 B1) in view of Ramer (U.S 2007/0094042 A1) in view of Tan (U.S 2011/0131077 A1) in view of Tsoy (U.S 2022/0327134 A1).
Claims 1, 10, and 19
Regarding Claim 1, Battisti recites the following:
A method, performed at a computer system comprising a processor and a computer-readable medium, the method comprising [see at least Col 5 lines 19-25 for reference to the stream processing service being a computer system comprising one or more computing devices configured such that the system collects, organizes, stores, and makes available to one or more other systems or services of the online retailer data corresponding to customers’ interaction with the online retailer; Col 12 lines 5-8 for reference to the process for optimizing content on an electronic commerce website based at least in part on attributed values calculated by one or more services of the online retailer; Col 15 lines 38-44 for reference to the server including a computer-readable storage medium storing instructions that, when executed by the processor of the server, allow the server to perform its intended functions; Figure 3 and related text regarding the environment allowing customers to connect to one or more servers operated by an online retailer; Figure 10 and related text regarding the process for generating regression models used to calculate the attributed value for one or more eligible hits; Figure 12 and related text regarding the process for optimizing content on an electronic commerce website based at least in part on attributed values calculated by one or more services of the online retailer]
receiving, via a network from a device associated with a user of the computer system, a request for generation of a user interface of the computer system [see at least Col 7 lines 20-22 for reference to the content delivery service receiving API requests from one or more servers requesting content to be used for the webpage; Col 12 lines 30-32 for reference to the content delivery service receiving a request for content to be displayed on the electronic commerce website; Col 12 lines 32-39 for reference to a customer navigating to the electronic commerce website and one or more servers of the electronic commerce website receiving an appropriately configured HTTP request]
responsive to receiving the request, obtaining retailer-specified preferences for placements of a plurality of content candidates in a plurality of placement positions of the user interface, the retailer-specified preferences corresponding to a preconfigured version of the user interface [see at least Col 3 lines 33-36 for reference to the online retailer operating one or more other services in order to optimize and select content to be displayed on their website; ; Figure 2 and related text regarding item 200 ‘website’; Examiner notes ‘website’ as analogous to the ‘preconfigured version of the user interface’; Figure 4 and related text regarding item 404 ‘online retailer’]
responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface [see at least Col 11 lines 8-10 for reference to collected customer data including data corresponding to a customer’s profiled or account with the online retailer; Col 12 lines 11-20 for reference to the system receiving attribute values associated with a piece of content; Col 12 lines 55-66 for reference to the system collecting navigational data including clickstream data or other data representing a customers’ interaction with the electronic marketplace; Figure 10 and related text regarding item 1002 ‘Collect Customer Data’; Figure 12 and related text regarding item 1202 ‘Receive Attribution Values’; Figure 13 and related text regarding item 1302 ‘Collect Navigational Data Over a Period of Time’]
applying a machine learning model of the computer system to the retailer-specified preferences and the contextual data to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions, wherein the machine learning model is trained on historical data of the computer system to predict the ranking score for each of the plurality of content candidates, the ranking score indicative of a respective value of a performance metric associated with operation of the computer system, the respective value of the performance metric achieved when each of the plurality of content candidates is selected by the user [see at least Col 2 lines 56-67 for reference to one or more regression models being used to assign attribute values to content which track the performance of content and/or determine the effectiveness of content; Col 11 lines 11-25 for reference to the regression models being generated based on the determined success events and eligible hits and adjusted further utilizing business logic; Col 11 lines 44-50 for reference to the regression model being used to calculate the probability of a particular event given a particular interaction, for example, the probability of a cart-add based on a customer clicking on or otherwise selecting a particular piece of content on the electronic website; Col 12 lines 23-29 for reference to the content delivery service generating content rankings utilizing one or more algorithms; Col 13 lines 8-11 for reference to navigational data for a particular period being transmitted to the attribution service and used to generate one or more regression models; Col 13 lines 14-18 for reference to the attribution service determining one or more attribution values for content based at least in part on the one or more regression models; Figure 10 and related text regarding item 1006 ‘Generate Regression Models’ and item 1008 ‘Adjust Regression Models’; Figure 11 and related text regarding item 1102 ‘Generate Regression Models to Estimate the Probability of a Particular Event’; Figure 12 and related text regarding item 1204 ‘Generate Content Rankings Based at Least In Part on the Received Attribution Values’ and item 1208 ‘Match Content According to Ranking and Placement Value’; Figure 13 and related text regarding item 1304 ‘Generate Regression Models Based at Least in Part on the Collected Navigational Data’ and item 1306 ‘Determine Attribution Values Based at Least in Part on Regression Models’]
generating, using the ranking score for each of the plurality of content candidates, the respective placement position of the plurality of placement positions for each of the plurality of content candidates in the user interface, wherein the user interface differs from the preconfigured version of the user interface [see at least Col 5 lines 52-55 and related text regarding the content delivery service using one or more algorithms to determine placement of content on the electronic commerce website; Col 6 lines 62-65 for reference to the content provider removing under-performing content from the electronic commerce website or increasing the use of content which has a high attributed value; Col 12 lines 45-48 for reference to the content delivery service matching the highest ranking content with the most prominent placement on the electronic commerce website; Col 13 lines 32-34 for reference to the content rating being used to determine the content and placement of content on the electronic commerce marketplace; Figure 12 and related text regarding item 1204 ‘Generate Content Rankings Based at Least In Part on the Received Attribution Values’ and item 1208 ‘Match Content According to Ranking and Placement Value’]
sending, via the network, the user interface to the device associated with the user, wherein sending the user interface causes the device associated with the user to display the user interface with each of the plurality of content candidates at the respective placement position in the user interface [see at least Col 2 lines 23-26 for reference to a website including other means of displaying content on a computing device of the user such as a mobile application; Col 3 lines 55-57 for reference to the webpage including various graphical user interface elements that enable navigation throughout a website of which the webpage is a part; Col 6 lines 43-45 for reference to the content delivery service rendering and delivering content for the online retailer’s website; Col 12 lines 35-39 for reference to content being rendered and displayed to the customer navigating the electronic commerce website; Figure 2 and related text regarding item 200 ‘webpage’]
wherein displaying the user interface different from the preconfigured version of the user interface comprises: displaying a first subset of content candidates of the plurality of content candidates, the first subset of content candidates including content candidates that correspond to a first content category, and each content candidate of the first subset of content candidates having a respective ranking score that is higher than any ranking score of a content candidate of the plurality of content candidates excluding the first subset of content candidates, and each content candidate of the first subset of content candidates representing an item with which the user interacted more frequently than with any of the plurality of content candidates excluding the first subset of content candidates [see at least Col 2 line 67 and Col 3 lines 1-5 for reference to attribution values being calculated from clickstream data collected as the user interacts with content is part of the online retailer’s website and used as input data for one or more regression models used to assign attribution values to content; Col 3 lines 36-51 for reference to the online retailer collecting users’ interactions with the website and content by capturing clickstream corresponding to users’ inputs’; Col 4 lines 49-53 for reference to content with a higher attribution value being placed by the content delivery service in a position of prominence on the webpage more often than content with a lower attribution value; Col 5 lines 48-52 for reference to the content delivery service displaying content with a higher attribute value more prominently on the electronic commerce website than content with a lower attributed value; Col 12 lines 45-48 for reference to the content delivery service matching the highest ranking content with the most prominent placement on the electronic commerce website; Figure 2 and related text regarding item 200 ‘website’; Examiner notes ‘website’ as analogous to the ‘preconfigured version of the user interface’]
displaying a second subset of content candidates of the plurality of content candidates, the second subset of content candidates including content candidates that correspond to a second content category, and each content candidate from the second subset of content candidates having a respective ranking score that is less than a ranking score of each content candidate from the first subset of content candidates [see at least Col 4 lines 49-53 for reference to content with a higher attribution value being placed by the content delivery service in a position of prominence on the webpage more often than content with a lower attribution value; Col 5 lines 48-52 for reference to the content delivery service displaying content with a higher attribute value more prominently on the electronic commerce website than content with a lower attributed value; Col 12 lines 45-48 for reference to the content delivery service matching the highest ranking content with the most prominent placement on the electronic commerce website; Figure 2 and related text regarding item 200 ‘website’; Examiner notes ‘website’ as analogous to the ‘preconfigured version of the user interface’]
receiving, via the network and from the device associated with the user, information about interactions of the user with the plurality of content candidates placed at the plurality of placement positions of the user interface [see at least Col 3 lines 47-54 for reference to users’ interaction being tracked to determine the attribution values for various content displayed in the website and stored and used to update one or more other services such as content delivery; Col 13 lines 36-42 for reference to the process being repeated periodically and navigational data being collected every 30 days and used as an input to one or more regression models; Figure 13 and related text regarding item 1300 and item 1302 ‘Collect Navigational Data Over a period of Time’]
retraining the machine learning model by updating a set of parameters of the machine learning model using the information about the interactions [see at least Col 13 lines 36-42 for reference to the process being repeated periodically and navigational data being collected every 30 days and used as an input to one or more regression models; Col 13 lines 44-54 for reference to the collected navigational data being used an input into the regression models to calculate new attribution values to the content delivery service; Figure 13 and related text regarding item 1300 and item 1308 ‘Update Content Ratings Based at Least in Part on the Attribution Values’]
While Battisti discloses the limitations above, it does not disclose responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives from a supplier of the items; applying a machine learning model to the retailer-specified preferences and the contextual data including the indication about the current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions; displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface; and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row.
However, Ramer discloses the following:
responsive to receiving the request, obtaining retailer-specified preferences for placements of a plurality of content candidates in a plurality of placement positions of the user interface, the retailer-specified preferences corresponding to a preconfigured version of the user interface [see at least Paragraph 0445 for reference to the determination of presentation of mobile content being based in part on the mobile content owner as it may be provided in the mobile content or the mobile content metadata]
responsive to receiving the request, obtaining contextual data associated with a presentation of user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, and information about inventories of the items [see at least Paragraph 0058 for reference to the system updating usage patterns including time of day, transaction history, content viewing history, etc.; Paragraph 0107 for reference to the search box being adapted to target results based on time of day; Paragraph 0160 for reference to the user’s time of day being used to predict what the user is more interested in; Paragraph 0385 for reference to the user may enter a search query with the name of the office building, and the name of the office building may be combined with the user's location and time of day to better target search results for the user; Paragraph 0460 for reference to the list of ordered merchants being resorted according to price, inventory, proximity or some other characteristic; Paragraph 0464 for reference to information provided by a third-party that is used to order a list of merchants including information cost of products, available information about their prices or their product inventory (online or offline)]
applying a machine learning model to the retailer-specified preferences and the contextual data including the indication about the a current time of day, information about a purchase history of the user, costs of items associated with the presentation, and information about inventories of the items to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions [see at least Paragraph 0138 for reference to suggestions being made to users by ranking content based on various metrics; Paragraph 0248 for reference to the algorithm facility analyzing usage patterns based on time of day and patterns surrounding transactions; Paragraph 0248 for reference to usage patterns may be analyzed using various predictive algorithms, such as regression techniques (least squares and the like), neural net algorithms, learning engines, random walks, Monte Carlo simulations, and others; Paragraph 0281 for reference to a user of a mobile communication facility may identify a policy or preference associated with determining which mobile content may be presented to a mobile communication facility; Paragraph 0522 for reference to the yield optimization algorithm considers several variables to determine placement and rank simultaneously, including, relevancy, geography, click-through/call-through rate, and bid price; Paragraph 1148 for reference to interaction information relating to a mobile communication facility may be used to weight content, and the content may be ordered for presentation on a mobile communication facility; Paragraph 1155 for reference to behavioral metrics, such as clickthrough volume, and conversion volume may be used to predict future consumer interactions with mobile content]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the contextual information of Battisti to include the current time of day, information about a purchase history of the user, costs of items associated with the presentation, and information about inventories of the item information of Ramer. Doing so would ensure that this advantageous content is given priority over search results that are equally relevant, as stated by Ramer (Paragraph 0151).
While Battisti and Ramer disclose the limitations above, they do not disclose responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives from a supplier of the items; applying a machine learning model to the retailer-specified preferences and the contextual data including the indication about the current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions; displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface; and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row.
However, Tan discloses the following:
responsive to receiving the request, obtaining contextual data associated with a presentation of the user interface including an indication about a current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives from a supplier of the items [see at least Paragraph 0033 for reference to the item corresponding to a coupon; Paragraph 0034 for reference to context information refers to at least one environmental factor which has a bearing on the relevancy of recommended items in an identified setting with respect to a particular user; Paragraph 0036 for reference to context information identifies temporal information, such as time information and/or date information; Paragraph 0044 for reference to content-based source information providing information that pertains to the item including its various physical properties, its performance, its cost, etc.; Paragraph 0045 for reference to collaboration-based sources of information can conclude that the user has expressed an interest in a particular item because: i) the user has repeatedly clicked-on (or otherwise consumed) information regarding that item; or ii) the user has purchased or otherwise acquired the item; or iii) the user has designated this item as a favorite, and so on; Figure 1 and related text regarding item 106 ‘context information’; Figure 6 and related text regarding item 602 ‘RECEIVE CONTEXT INFORMATION’]
applying a machine learning model to the retailer-specified preferences and the contextual data including the indication about the current time of day, information about a purchase history of the user, costs of items associated with the presentation, information about inventories of the items, sale status of the items, and promotional incentives to generate a ranking score for each of the plurality of content candidates for a respective placement position of the plurality of placement positions [see at least Paragraph 0043 for reference to the recommendation model, in turn, refers to an algorithm used by the recommendation module to assess the relevancy of items with respect to a particular recipient user; Paragraph 0044 for reference to content-based source information providing information that pertains to the item including its various physical properties, its performance, its cost, etc.; Paragraph 0045 for reference to collaboration-based sources of information can conclude that the user has expressed an interest in a particular item because: i) the user has repeatedly clicked-on (or otherwise consumed) information regarding that item; or ii) the user has purchased or otherwise acquired the item; or iii) the user has designated this item as a favorite, and so on; Paragraph 0049 for reference to the model updater module can periodically obtain model information from one or more sources of such information, and then provide this information to the recommendation module; Paragraph 0062 for reference to a single-model technique, the ranking module generates a set of ranked items on the basis of a single recommendation model, such as a content-based model, a collaboration-based model, a friends-based model, or some other model; Paragraph 0100 for reference to the subset of ranked set of items includes coupon associated therewith]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the contextual information of Battisti to include the sale status information and promotional incentives of Tan. Doing so can improve the relevance of its recommended items, especially in those environments in which users interact with a network using mobile devices (although the recommendation module can be used in any environment), as stated by Tan (Paragraph 0005).
While the combination of Battisti, Ramer, and Tan disclose the limitations above, they do not recite displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface; and displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row.
However, Tsoy discloses the following:
displaying a first subset of content candidates of the plurality of content candidates at a first row of the user interface corresponding to a top row of the user interface, the first subset of content candidates including content candidates that correspond to a first content category, and each content candidate of the first subset of content candidates having a respective ranking score that is higher than any ranking score of a content candidate of the plurality of content candidates excluding the first subset of content candidates [see at least Paragraph 0098 for reference to content elements may be displayed in any arrangement, such as a horizontal arrangement and/or a nested arrangement wherein content elements may be displayed in a vertical feed having rows with multiple content elements; Paragraph 0099 for reference to content elements being displayed in ranked order determined by a predicted relevance score; Paragraph 0099 for reference to content element 301 being the top ranked element with the highest predicted relevance score; Paragraph 0147 for reference to the interface displaying the content elements in their ranked order in which highest-ranked content is displayed at the top of the interface; Figure 3 and related text regarding the ‘Content Feed’ of ranked content; Figure 6 and related text regarding item 620 ‘Output an interface displaying the content elements in their ranked positions’]
displaying a second subset of content candidates of the plurality of content candidates at a second row of the user interface immediately below the first row, the second subset of content candidates including content candidates that correspond to a second content category, and each content candidate from the second subset of content candidates having a respective ranking score that is less than a ranking score of each content candidate from the first subset of content candidates [see at least Paragraph 0098 for reference to content elements may be displayed in any arrangement, such as a horizontal arrangement and/or a nested arrangement wherein content elements may be displayed in a vertical feed having rows with multiple content elements; Paragraph 0099 for reference to content elements being displayed in ranked order determined by a predicted relevance score; Paragraph 0099 for reference to content element 301 being the top ranked element with the highest predicted relevance score; Paragraph 0147 for reference to the interface displaying the content elements in their ranked order in which highest-ranked content is displayed at the top of the interface; Paragraph 0147 for reference to as the user scrolls through the interface additional content elements may be displayed; Figure 3 and related text regarding the ‘Content Feed’ of ranked content; Figure 6 and related text regarding item 620 ‘Output an interface displaying the content elements in their ranked positions’]
receiving, via the network and from the device associated with the user, information about interactions of the user with the plurality of content candidates placed at the plurality of placement positions of the user interface [see at least Paragraph 0137 for reference to additional datasets being collected of user interactions; Paragraph 0147 for reference to the user interaction with the interface being recorded]
retraining the machine learning model by updating a set of parameters of the machine learning model using the information about the interactions [see at least Paragraph 0089 for reference to user interactions being used to train an MLA to predict relevance scores; Paragraph 0137 for reference to the MLA being regularly re-trained using additional datasets such as newly recorded data regarding user interactions with interfaces; Paragraph 0160 for reference to the MLA being re-trained based on the training dataset as well as periodically when a threshold number of datasets have been collected; Figure 6 and related text regarding item 650 ‘Re-Train the MLA’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the content placement method of Battisti to include the specific row placement of Tsoy. Doing so would provide a system that is updated to respond to changing user interests, as stated by Tsoy (Paragraph 0137).
Regarding claims 10 and 19, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 10, Battisti teaches a non-transitory computer-readable storage medium storing instructions executable by a processor [Col 15 lines 38-44]. Regarding claim 19, Battisti teaches a computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions executable by the processor [Col 5 lines 19-25]. Therefore, claims 10 and 19 are rejected as being unpatentable in view of Battisti, Ramer, Tan, and Tsoy.
Claims 2 and 11
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 2, Battisti discloses the following:
obtaining the plurality of content candidates by obtaining a combination of retailer-curated content specified by a retailer and system-curated content generated automatically without input from the retailer [see at least Col 7 lines 11-22 or reference to the content delivery service selecting content for an electronic commerce website; Col 12 lines 35-39 for reference to content being rendered and displayed to the customer navigating the electronic commerce website; Figure 4 and related text regarding item 406 ‘Content Delivery Service’]
Regarding claim 11, the claim recites limitations already addressed by the rejection of claim 2.
Claims 3 and 12
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 3, Battisti discloses the following:
obtaining the plurality of content candidates by obtaining one or more digital banners that include links to respective landing pages that enable adding of items available from a retailer to an order for the user operating the device [see at least Col 2 lines 18-25 for reference to website content or website features including navigation links, audio, video, advertisements, menus, images, words, dialog boxes, pop-up windows or any other information that may be received by a web browser; Col 3 lines 62-66 for reference to the webpage including various links to one or more webpages that contain additional content corresponding to department pages and/or category pages; Figure 2 and related text regarding item 210 ‘links’]
Regarding claim 12, the claim recites limitations already addressed by the rejection of claim 3.
Claims 6 and 15
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 6, Battisti discloses the following:
deriving the performance metric from at least one of a click-through-rate (CTR), a gross transaction value (GTV), or a gross merchandise value (GMV) associated with the user interface [see at least Col 3 lines 1-5 for reference to clickstream data being collected as the user interacts with content; Col 3 lines 36-51 for reference to the attribution service analyzing clickstream data over a period of time; Col 6 lines 4-7 for reference to the attribution service determining based on clickstream data a set of success events and associated eligible hits]
Regarding claim 15, the claim recites limitations already addressed by the rejection of claim 6.
Claims 8 and 17
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 8, Battisti discloses the following:
wherein in at least one instance, the plurality of placement positions determined based on the ranking score for each of the plurality of content candidates vary from the retailer-specified preferences [see at least Co 12 lines 23-29 for reference to the content delivery service generating content rankings utilizing one or more algorithms; Col 13 lines 14-18 for reference to the attribution service determining one or more attribution values for content based at least in part on the one or more regression models; Figure 12 and related text regarding item 1204 ‘Generate Content Rankings Based at Least In Part on the Received Attribution Values’ and item 1208 ‘Match Content According to Ranking and Placement Value’]
Regarding claim 17, the claim recites limitations already addressed by the rejection of claim 8.
Claims 9, 18, and 20
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 9, Battisti discloses the following:
obtaining the contextual data further comprises obtaining at least one of a profile of the user operating the user client device, a season, or promotional data associated with items available from a retailer [see at least Col 11 lines 8-10 for reference to collected customer data including data corresponding to a customer’s profiled or account with the online retailer; Figure 10 and related text regarding item 1002 ‘Collect Customer Data’]
Regarding claims 18 and 20, the claims recite limitations already addressed by the rejection of claim 9.
Claim(s) 4-5 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Battisti (U.S 9,911,130 B1) in view of Ramer (U.S 2007/0094042 A1) in view of Tan (U.S 2011/013077 A1) in view of Tsoy (U.S 2022/0327134 A1), as applied in claims 1 and 10, in view of Mantha (U.S 11,308,543 B1).
Claims 4 and 13
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 4, Battisti discloses the following:
obtaining the plurality of content candidates by obtaining one or more product that include a set of item elements for different items available from a retailer and respective controls for adding one or more selected items to an order for the user operating the device [see at least Col 4 lines 7-16 for reference to the “add to cart” selection by an input device causing information corresponding to the item offered for sale on the particular webpage to be placed in the user’s electronic shopping cart; Col 4 lines 25-32 for reference to the saved items list being a list of items maintained by the online retailer which may be accessed by others so that the items on the list may be purchased for the user responsible for creating the saved items list; Figure 2 and related text regarding item 204 ‘add to cart button’]
While Battisti discloses the limitations above, it does not disclose obtaining one or more product carousels that include a set of item elements for different items available from a retailer.
However, Mantha discloses the following:
obtaining the candidate content by obtaining one or more product carousels that include a set of item elements for different items available from a retailer [see at least Col 5 lines 47-52 for reference to the carousel including items that a customer may be interested in; Col 14 lines 62-64 for reference to carousels being associated with carousel item data identifying and characterizing items in each carousel; Figure 3 and related text regarding item 370 ‘Carousels’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the item identification method of Battisti to include the product carousel of Mantha. Doing so increases the chances that a person will purchase the recommended items, as stated by Mantha (Col 2 lines 7-9).
Regarding claim 13, the claim recites limitations already addressed by the rejection of claim 4.
Claims 5 and 14
While the combination of Battisti, Ramer, Tan, Tsoy, and Mantha disclose the limitations above, Battisti does not disclose wherein the one or more product carousels are associated with corresponding product categories, and wherein the one of more product carousels have different selection and ranking rules for selecting and ranking the items in the corresponding product categories.
Regarding Claim 5, Mantha discloses the following:
wherein the one or more product carousels are associated with corresponding product categories [see at least Col 14 lines 64-67 and Col 15 lines 1-5 for reference to each carousel being associated with item category data identifying and characterizing a category (e.g., car, animals, fruits, vegetables, drinks, etc.) associated with each item in the carousel; Figure 3 and related text regarding item 370 ‘carousel’ and item 390 ‘carousel item data’]
wherein the one or more product carousels have different selection and ranking rules for selecting and ranking the items in the corresponding product categories [see at least Col 7 lines 1-5 for reference to the ranking of the potential carousels for the user based on carousel scores; Col 14 lines 47-52 for reference to the use of user engagement data, transaction data to determine carousel ranks; Figure 3 and related text regarding item 308 ‘carousel ranks’; Figure 4 and related text regarding item 402 ‘Carousel Ranking Engine’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the item identification method of Battisti to include the product carousel selection and ranking of Mantha. Doing so a user may be presented with carousels based on identifying whether the user prefers to explore new items or prefers to interact or buy previously bought and/or seen items, as stated by Mantha (Col 7 lines 5-8).
Regarding claim 14, the claim recites limitations already addressed by the rejection of claim 5.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Battisti (U.S 9,911,130 B1) in view of Ramer (U.S 2007/0094042 A1) in view of Tan (U.S 2011/013077 A1) in view of Tsoy (U.S 2022/0327134 A1), as applied in claims 1 and 10, in view of Elazary (U.S 9,120,622 B1).
Claims 7 and 16
While the combination of Battisti, Ramer, Tan, and Tsoy disclose the limitations above, regarding Claim 7, Battisti discloses the following:
receiving, via the user interface, a selection of one or more items for adding to an order of the user operating the device [see at least Col 4 lines 7-16 for reference to the “add to cart” selection by an input device causing information corresponding to the item offered for sale on the particular webpage to be placed in the user’s electronic shopping cart; Figure 2 and related text regarding item 204 ‘add to cart button’]
facilitating, by the computing system, processing of the order to procure the one or more items and deliver the one or more items to the user [see at least Col 6 lines 21-25 for reference to the customer purchasing the item added to their cart; Figure 3 and related text regarding the customer environment operating an online retailer webpage]
While Battisti discloses the limitations above, it does not disclose wherein facilitating processing of the order comprises: assigning the order to picker that is a fully-autonomous robot, upon assigning the order, instructing, via collection instructions stored at the computer-readable medium and executed by the processor, the fully- autonomous robot to collect the one or more items of the order in a retailer location, and upon collecting the items in the retailer location, controlling, via navigation instructions stored at the computer-readable medium and executed by the processor, a movement of the fully-autonomous robot from the retailer location to a delivery location associated with the user.
However, Elazary discloses the following:
wherein facilitating processing of the order comprises: assigning the order to picker that is a fully-autonomous robot [see at least Col 3 lines 18-22 for reference to a robot performing a fully autonomous order fulfillment, inventory management, and site optimization; Col 8 lines 3-5 for reference to the first set of robots depositing any retrieved items in container located nearby the shelves per the robot assignment]
upon assigning the order, instructing, via collection instructions stored at the computer-readable medium and executed by the processor, the fully- autonomous robot to collect the one or more items of the order in a retailer location [see at least Col 8 lines 52-56 for reference to the control center instructing each and every robot operating within the distribution site of which orders or order items the robots are to retrieve; Col 11 lines 60-67 for reference to the instructions sent by the control center including an identifier identifying a location of the items awaiting restock; Figure 8 and related text regarding item 810 ‘Control center configures robot with the task of fulfilling a customer order’]
upon collecting the items in the retailer location, controlling, via navigation instructions stored at the computer-readable medium and executed by the processor, a movement of the fully-autonomous robot from the retailer location to a delivery location associated with the user [see at least Col 6 lines 39-41 for reference to a robot being used to deliver a package containing all items of a customer order to a shipping drop off location within the distribution site; Col 7 lines 34-40 for reference to the robot navigating (following instructions from the control center) intelligently through the distribution site without collision to arrive at the nearest shelf containing a customer order; Col 8 lines 52-56 for reference to the control center instructing each and every robot operating within the distribution site of which orders or order items the robots are to retrieve; Col 11 lines 60-67 for reference to the instructions sent by the control center including an identifier identifying a location of the items awaiting restock; Figure 8 and related text regarding item 810 ‘Control center configures robot with the task of fulfilling a customer order’ and item 840 ‘Robot navigates the distribution site without collisions to arrive at the nearest shelf containing a customer order item’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the order processing of Battisti to include the automatic robot fulfillment of Elazary. Doing so allows the robots to retrieve individual items or bins from the distribution site shelves and, optionally dispense items of the customer order in order to better automate order fulfillment, as stated by Elazary (Col 3 lines 14-16).
Regarding claim 16, the claim recites limitations already addressed by the rejection of claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tan, Hua-Zhe, Wei Zhao, and Hai-Hua Shen. "Adaptive user interface optimization for multi-screen based on machine learning." 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD)). IEEE, 2018.
DOCUMENT ID
INVENTOR(S)
TITLE
US2022/0044299 A1
Tate et al.
DETERMINING GENERIC ONLINE ITEMS FOR ORDERS ON AN ONLINE CONCIERGE SYSTEM
KR20170027323A
Choi Jong Hwan
SYSTEM FOR MANAGING ONLINE GOODS ORDER
THIS ACTION IS MADE FINAL. 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 KRISTIN ELIZABETH GAVIN whose telephone number is (571)270-7019. The examiner can normally be reached M-F 7:30-4:30 PM EST.
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, Jerry O'Connor can be reached at 571-272-6787. 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.
/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
Prosecution Insights