DETAILED ACTION
Status of the Claims
This office action is submitted in response to the amendment filed on 11/6/25.
Examiner notes that this application is a continuation of 16/456830, which was abandoned on 5/14/24.
Examiner further notes Applicant’s priority date of 6/28/19, which stems from the aforementioned parent application.
Claims 4, 7, 14, and 17 have been canceled.
Claims 1, 11, and 20 have been amended.
Therefore, claims 1-3, 5-6, 8-13, 15-16, and 18-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, the claims, as amended, now describe a process for automatically moving the plurality of recommended content items to positions on the GUI based on an order of relevance of the plurality of recommended content items. Examiner notes that the specification does disclose: computing an ordered listing or ranked list of recommended items, and then providing digital advertisements for display on webpages (e.g., online shopping cart pages); and showing ads or lists (e.g., FIGS. 5A–5C show recommended item data, an online cart page with ads, and a digital advertisement list) and sending “item advertisement recommendation messages” to a web server which then “provides the digital advertisement for display” on a web page.
However, there is no teaching that the position or location of recommended items on the GUI is changed as a function of relevance, interaction count, or any other metric. Furthermore, there is no disclosure of automatically “moving” items on the screen, rearranging, reordering, or relocating GUI elements after initial display, nor any rule tying screen position to relevance. The focus is on which items are selected to advertise and in what order they are listed, not on GUI-level rearrangement.
Therefore, because the originally filed disclosure only describes selecting and displaying recommended items (and possibly in a list order), and never describes dynamically moving or repositioning those items on the graphical user interface based on an order of relevance, the claimed “moving the plurality of recommended content items to positions on the graphical user interface based on an order of relevance” lacks written-description support.
Considerations Under 35 USC 101
Independent claims 1, 11, and 20 are directed to the mental/business logic of: collecting user/cart data, determining a relevance or interaction score for items, ranking items, and selecting which items to recommend—i.e., data analysis and targeted recommendation, which clearly discloses a judicial exception in accordance with MPEP 2106.05 (mental processes and organizing human activity).
However, the amendments to the independent claims now describe a process in which recommended items are moved to particular positions on the display based on their relevance. This GUI relocation behavior represents an additional element that is clearly analogous to Example 37 of the Office’s Subject Matter Eligibility examples. In Example 37, the claim was deemed to be eligible because it disclosed an additional functional element that involved automatically moving the most used icons to a position on the GUI closest to the start icon of the computer system based on the determined amount of use.
Thus, the claim as a whole integrates the judicial exception into a practical application. Specifically, the additional elements recite a specific manner of automatically displaying recommendations to the user based on their relevance, which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices. It also denotes a specific way of presenting the results on a computer interface, separate from the underlying ranking logic.
Therefore, for at least these reasons, a rejection under 101 is not appropriate at this time. However, as noted above in the rejection under 35 USC 112(a), the amended language does not have written support in the specification. As such, Applicant should take note that a 101 rejection will likely be required in future office actions when the language is amended to comply with 112.
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.
Claims 1-3, 5-6, 8-13, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jacobi et al. (US 6,317,722 B1) in view of Gramatica (US 10,025,862 B2), and in further view of Aravamudan et al. (US 8,375,069 B2).
Claims 1, 11, and 20: Jacobi discloses a system, method, and computer-readable medium comprising:
receiving, via a graphical user interface of a user device, a user request to display a plurality of recommended content items (FIG. 6; col. 9, ll. 1–11; col. 10, ll. 1–9) Jacobi shows a browser window (FIG. 6 “Instant Recommendations”) where the user selects a hyperlink such as “Instant Book Recommendations” or views the shopping cart page, and in response the system generates and displays a list of recommended items on the same web page as the shopping cart contents.);
determining, by the processor in real-time, the predicted number of user interactions the plurality of recommended content items receive based on in response to receiving the online cart activity data of the user, the activity data comprising a plurality of selected items (FIG. 1; FIG. 2; FIG. 7; col. 4, ll. 24–38; col. 6, ll. 39–67; col. 7, ll. 1–24; col. 9, ll. 29–55. Jacobi identifies items currently in the user’s shopping cart as items of known interest, uses the current and/or recent shopping cart contents as inputs to the recommendation service, and states that in the shopping-cart implementation “the recommendations are generated… in real-time when the user initiates a display of a shopping cart, and are displayed on the same Web page as the shopping cart contents.”); and
the plurality of recommended content items related to at least one of the plurality of selected items. (Abstract; col. 2, ll. 52–67; col. 3, ll. 17–34; FIG. 1; FIG. 3. Jacobi explains that an additional item is selected to be included in the list “based in-part upon whether that item is related to more than one of the items in the user’s shopping cart” and uses a similar-items table mapping items to lists of similar items based on purchase-history correlations.).
While Jacobi bases recommendations on purchase histories, item-to-item commonality indices, and shopping cart contents, it does not explicitly describe a process “wherein the specific criteria is a predicted number of user interactions the plurality of recommended content items receive.”
Gramatica, however, discloses a method wherein the specific criteria is a predicted number of user interactions the plurality of recommended content items receive (e.g., Abstract; FIG. 5; col. 6, ll. 45–67; col. 7, ll. 1–22; col. 13, ll. 10–39. Gramatica describes constructing a weighted information network and analyzing it using a stochastic algorithm implemented as a random walk over a transition/transfer matrix whose entries are probabilities derived from link weights, and using the resulting random-walk scores and distances to rank nodes according to how strongly they are connected to a query or starting node, which corresponds to predicted likelihoods of association/“interaction” between nodes.).
Next, Jacobi calculates commonality indices and combined similarity scores, and sorts the resulting recommendation list from highest to lowest score (e.g., FIG. 2, steps 82, 88; FIG. 7, steps 286–288), but does not describe these scores as “predicted number of user interactions” between each selected item and each recommended item, and does not expressly rank the selected items themselves based on such a measure.
Gramatica, however, discloses a method for determining, by the processor, in real-time the predicted number of user interactions the plurality of recommended content items receive based on in response to receiving the online cart activity data of the user, the activity data comprise a plurality of selected items and the predicted number of user interactions is between (i) each of the plurality of selected items and (ii) each of the plurality of recommended items related to at least one of the plurality of selected items, and the plurality of the selected items are ranked based on a measure. (e.g., Abstract; FIG. 5; col. 6, ll. 45–67; col. 7, ll. 1–22; col. 13, ll. 10–39; col. 14, ll. 1–30. Gramatica teaches defining a weighted graph of information units, building a transition/transfer matrix P whose entries represent transition probabilities between nodes based on dependency measures, executing a random walk on this graph, and using the resulting random-walk distances or scores to rank nodes and paths according to strength of connection to a query node; these probabilistic scores function as measures of how strongly each node (candidate item) is connected to selected nodes and can be used to rank both candidate and selected nodes.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine these features of Gramatica with those of Jacobi. One would have been motivated to do this in order to use known random-walk-based probabilistic scores as the specific criterion for ranking and selecting Jacobi’s recommended items, effectively treating these scores as predicted user interaction measures for the recommended content items.
Finally, while Jacobi describes a process for sorting recommendation lists by score to determine which items to recommend and in what order to present them in a list, it does not explicitly describe a method for moving the plurality of recommended content items to positions on the graphic user interface based on an order of relevance of the plurality of recommended content items.
Aravamudan, however, discloses a method for moving the plurality of recommended content items to positions on the graphic user interface based on an order of relevance of the plurality of recommended content items (Abstract; col. 2, ll. 15–35; col. 7, ll. 35–67; col. 8, ll. 1–31; FIG. 10. Aravamudan describes associating per-user relevance weights with content items, adjusting those weights based on user navigation and selection actions, and “selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights”; it further explains that the navigation hierarchy is “reordered to match the user’s action behavior,” that nodes/items with higher relevance weights are “presented higher in the search results,” and that items can be “brought closer to the user’s locus of relevance” or hoisted to the root, which corresponds to moving items to different positions in the interface based on relevance ordering.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine these features of Aravamudan with those of Jacobi and Gramatica. One would have been motivated to do this in order to improve the presentation of Jacobi’s recommended items by arranging and dynamically repositioning them on the GUI so that more relevant recommendations appear in more prominent positions, thereby reducing user effort to find desired content.
Claims 2 and 12: Jacobi further discloses a method wherein the instructions further cause the processor to: determine that a customer has interacted with an item from the plurality of selected items based on receiving one or more interactions from the user interface; and receive, from a web server, an online cart activity message identifying one or more cart items that have been added to an online digital cart (FIG. 1; col. 5, ll. 32–67; col. 7, ll. 25–42. Jacobi shows a web server 32 and a shopping cart process within external components 40 that “adds and removes items from the users’ personal shopping carts based on the actions of the respective users,” and a user profiles database 38 storing “the current contents of the user’s personal shopping cart(s)” and “recent shopping cart contents,” which necessarily occurs in response to user interactions on the site and corresponds to receiving cart-activity data indicating items added to the cart.).
Claims 3 and 13: Jacobi further discloses a method wherein the instructions further cause the processor to: receive, from a web server, a subsequent online cart activity message identifying one or more cart items that have been removed from the online digital cart (FIG. 1; col. 5, ll. 54–67; col. 7, ll. 25–42. Jacobi explains that the shopping cart feature “allows users to add and remove items to/from a personal shopping cart” and that the shopping cart process “adds and removes items from the users’ personal shopping carts” and maintains “recent shopping cart contents,” including items removed without purchase, which corresponds to subsequent online cart activity messages indicating items removed from the cart.).
Claims 5 and 15: Jacobi further discloses a method wherein the instructions further cause the processor to: generate an item advertisement recommendation message identifying recommended items to be included in one of the subset of recommended items (FIG. 1; FIG. 2; FIG. 6; FIG. 7; col. 4, ll. 24–38; col. 6, ll. 39–67; col. 9, ll. 1–11. Jacobi describes recommendation process 52 generating a list of recommended items (a “recommendations list”) and the web server 32 incorporating these recommendations into personalized web pages (e.g., FIG. 6 “Instant Recommendations” and the shopping cart page with recommendations), which requires producing a data structure/message that identifies recommended items for inclusion in a displayed recommendation/ad subset.).
Claims 6 and 16: The Jacobi/Gramatica/Aravamudan combination discloses those limitations cited above. Gramatica, however, further discloses a method wherein executing the stochastic process comprises executing a random walk algorithm based on the transition matrix probabilities (Abstract; FIG. 5; col. 6, ll. 45–67; col. 7, ll. 1–22; col. 13, ll. 10–39; col. 14, ll. 1–30. Gramatica describes analyzing a weighted information network using “a stochastic algorithm that utilizes a probabilistic crawler defined as ‘random walker’ which navigate the whole weighted graph,” defines a transfer/transition matrix P of probabilities derived from edge weights, and executes a random walk over this matrix to compute distances and rankings.).
The rationale for combining Gramatica with Jacobi and Aravamudan is articulated above and reincorporated herein.
Claim 8: Jacobi further discloses a method wherein the plurality of selected items includes a first item and a second item, and the instructions further cause the processor to generate a first set of digital advertisements for the first item and a second set of digital advertisements for the second item, the first item being different than the second item. (FIG. 1; FIG. 2; FIG. 3; FIG. 7; col. 3, ll. 17–34; col. 4, ll. 24–38; col. 8, ll. 1–24. Jacobi retrieves a similar-items list from the table for “each shopping cart item that is a popular item” (FIG. 7, step 282) and uses these per-item lists to generate recommendations; these lists correspond to separate sets of recommended items for each selected/cart item, which, when displayed on web pages, function as per-item digital ad/recommendation sets for different items.).
Claims 9 and 18: Jacobi further discloses a method wherein the instructions further cause the processor to: determine a maximum number of recommended items for each item of the plurality of selected items. (FIG. 3; FIG. 2; FIG. 7; col. 4, ll. 24–38; col. 8, ll. 1–24. Jacobi describes that each similar-items list 64 is truncated (“TRUNCATE OTHER_ITEMS LISTS AND STORE IN TABLE,” FIG. 3, step 116) and that the recommendation process “RECOMMEND TOP M ITEMS FROM LIST” (FIG. 2, step 94; FIG. 7, step 294), which establishes and applies maximum counts (N, M) for recommended items per base item/list.).
Claims 10 and 19: Jacobi further discloses a method wherein the instructions further cause the processor to: generate a first set of recommended items for a first item of the plurality of selected items, the first set including a predetermined number of recommended items; generate a second set of recommended items for the first item of the plurality of selected items, the second set including the predetermined number of recommended items; cause the user device to display the first set of recommended items in response to execution of the stochastic process; and cause the user device to display the second set of recommended items in response to completion of displaying the first set of recommended items and in response to an indication of the customer's input on the user interface (FIG. 2; FIG. 5; FIG. 6; FIG. 7; col. 6, ll. 39–67; col. 9, ll. 1–11. Jacobi teaches generating a list of recommended items (e.g., “RECOMMEND TOP M ITEMS FROM LIST” in FIG. 2 and FIG. 7) and displaying those recommended items on a web page (e.g., FIG. 6 “Instant Recommendations” and the shopping cart page with recommendations), where the list contains a predetermined number M of recommended items shown to the user.).
Jacobi does not explicitly disclose “generating a second set of recommended items for the first item of the plurality of selected items, the second set including the predetermined number of recommended items; cause the user device to display the first set of recommended items in response to execution of the stochastic process; and cause the user device to display the second set of recommended items in response to completion of displaying the first set of recommended items and in response to an indication of the customer's input on the user interface.”
Aravamudan, however, discloses a method for generating a second set of recommended items for the first item of the plurality of selected items, the second set including the predetermined number of recommended items; cause the user device to display the first set of recommended items in response to execution of the stochastic process; and cause the user device to display the second set of recommended items in response to completion of displaying the first set of recommended items and in response to an indication of the customer's input on the user interface (Abstract; col. 2, ll. 15–35; col. 4, ll. 1–30; col. 7, ll. 35–67; FIG. 10. Aravamudan describes presenting a subset of items ordered by relevance weights, updating those weights based on user navigation and selection actions, and then selecting and presenting new subsets of items (e.g., different items or reordered items) in response to continued user input; this corresponds to displaying subsequent sets of items after an initial display when the user continues to interact with the interface.).
The rationale for combining Aravamudan with Jacobi and Gramatica is articulated above and reincorporated herein.
Other Relevant Prior Art
The following references are not cited above, but are nevertheless deemed to be relevant to Applicant’s disclosures:
Agarwal et al. (20240029139), directed to a method and apparatus for item selection.
Joshi et al. (8417559), directed to assortment planning based on demand transfer between products.
Chen et al. (20110184806), directed to probabilistic recommendation of an item.
Das et al. (20210263939), directed to transition regularized matrix factorization for sequential recommendation.
Gunawardana et al. (20090006290), directed to training random walks over absorbing graphs.
Response to Arguments
Applicant’s arguments have been fully considered, but are rendered moot in view of the modified grounds of rejection cited above, which were necessitated by the amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BUSCH whose telephone number is (571)270-7953. The examiner can normally be reached M-F 10-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621