DETAILED ACTION
This office action is responsive to RCE filed on 04/28/2026. Claims 1, 11 and 20 are amended; claims 1-20 are pending.
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
Claims 1-20 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. Claims 1, 11, and 20 recites “ranking the set of items to determine a position of images in the user interface according to the inventory matching score, wherein a first score causes a first image to be in a first position and a second score causes a second image different than the first image to be in a second position different than the first position.
identifying the first position and the second position in the user interface to display the first image and the second image.”
The specification is completely silent with regard to “a first score causes a first image to be in a first position and a second score causes a second image different than the first image to be in a second position different than the first position.” In fact, the specification does not even discuss anything related to “position”; Fig. 5 seems to be the closest description; however, the Fig. 5 teaches:
“[00104] The online system 140 may then rank (e.g., using the ranking module 213) items included among the inventory at the retailer location based on a score for each item. In some embodiments, the online system 140 ranks the items based on an inventory matching score computed 335 for each item. FIG. 5 illustrates examples of inventory matching scores for items included among an inventory at a retailer location, in accordance with one or more embodiments. As shown in FIG. 5, the online system 140 may rank items 505 (e.g., items 505A-N) from highest to lowest based on an inventory matching score 500 for each item 505, in which an item 505A associated with a highest inventory matching score 500 is ranked the highest and an item 505N associated with a lowest inventory matching score 500 is ranked the lowest. Alternatively, in the above example, if the online system 140 scores each item 505 based on other types of information (e.g., a likelihood that the user will order the item 505, a relatedness of the item 505 to a search query, or a predicted availability of the item 505), the online system 140 may rank the items 505 based on the score for each item 505. In some embodiments, the online system 140 may boost (e.g., using the ranking module 213) the ranking for one or more items 505 included in the item category. In the above example, suppose that the inventory matching score 500 for an item 505D corresponding to skirt steak is less than a threshold score 510 and that a search query is received from the user client device 100 for skirt steak. In this example, if other items 505 A–C (e.g., ribeye steak, sirloin steak, and flat iron steak,) included in the same “steak” item category are associated with higher inventory matching scores 500, the online system 140 may boost the ranking for these other items 505A–C, increasing the likelihood that the online system 140 will select 345 them for display (e.g., in a set of search results of the ordering interface), as described below.” This disclosure has nothing to do with “wherein a first score causes a first image to be in a first position and a second score causes a second image different than the first image to be in a second position different than the first position”, rather it only teaches ranking the items based on score; it does not discuss the position aspect in the user’s interface.
Dependent claims are rejected based on rejected base claim.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 20 as representative:
Step 1: The claim recites a system, therefore, is a machine.
Step 2A, Prong One: The invention as claimed comprises:
A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising: retrieving a set of user data for a user of an online system; accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by: receiving user data for a plurality of users of the online system, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with the item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users; applying the machine-learning model to predict the measure of preference of the user associated with the item category based at least in part on the set of user data for the user; for an item included in the item category, receiving information describing an inventory of the item at a retailer location, the information comprising a set of images of the item captured at the retailer location; receiving a request from a user client device associated with the user to access a user interface comprising information describing a set of items included among the inventory at the retailer location; determining a measure of similarity between the information describing the inventory of the item at the retailer location and the predicted measure of preference of the user associated with the item category, wherein determining the measure of similarity comprises: comparing the predicted measure of preference of the user to a plurality of content item embeddings of the inventory to obtain the measure of similarity; generating an inventory matching score for the item based at least in part on the measure of similarity, wherein the inventory matching score indicates whether the inventory of the item at the retailer location is consistent with the predicted measure of preference of the user associated with the item category; selecting the set of items included among the inventory at the retailer location to include in the user interface based at least in part on the inventory matching score; ranking the set of items to determine a position of images in the user interface according to the inventory matching score, wherein a first score causes a first image to be in a first position and a second score causes a second image different than the first image to be in a second position different than the first position; generating the user interface comprising a set of information describing the selected set of items, wherein generating the user interface further comprises: selecting the first image and the second image from a plurality of candidate images based on the inventory matching score; and identifying the first position and the second position in the user interface to display the first image and the second image; and sending the user interface to the user client device associated with the user, wherein sending the user interface causes the user client device to display the user interface.
Claim as drafted, is a process that under its broadest reasonable interpretation, covers certain methods of organizing human activity such as marketing or sales activities.
That is, other than reciting computer, processor, training machine learning model, retailer location, client device, and interface, nothing in the claim element precludes the step from practically being sales activity. It is no different than checking which location have most inventory matching with buyers’ preference.
Step 2A Prong Two: The claim recites additional element of “computer, processor, training machine learning model, retailer location, client device, and interface”.
The additional elements computer, processor, training machine learning model, retailer location, client device, and interface are no more than mere instructions to apply the exception using a generic computer component (computer). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Using the trained model is nothing more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) In addition, machine learning model can be interpreted as mathematical concept. The process of generating and presenting the interface is applying/linking internet browser technology, that renders the recommended content on users’ device for display.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component or generally linking the use of the judicial exception to a particular technological environment.
The same conclusion is reached in 2B, i.e., mere instructions to apply an exception on a generic computer or generally linking the use of the judicial exception to a particular technological environment cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1, and 11 are rejected based on same rational as claim 1 above, as these claims represent corresponding method, and a computer program product to the system of claim 20.
Claims 2-10, and 12-19 further narrow the recited abstract idea above, and are rejected under same rational as claim 1. Claim 5 recites additional element of memory, which is treated in same manner as device/processor in claim 1 above. In addition, “picker client device” as recited in claim 10 is treated same as client device with regard to claim 20.
Allowable Subject Matter
Claims 1-20 are allowed over the prior art. Liu et al. (US 20230244727 A1) reference teaches the concept of predicting the user preference for the product or product category (Brand) using machine learning and ranking.
“ [0054] The machine learning architecture 350 can comprise one or more learning models that are configured to optimize the ranking of items 310 included in the search results 380. In certain embodiments, the one or more learning models can be trained to personalize the ranking of items 310 for each user based, at least in part, on predicted user preferences. In many cases, the one or more learning models can be trained to personalize the search results 380 on an individual user basis (e.g., specifically for each user).”
Garner (US 20200273013 A1) teaches inventory level of products of interest at different retail locations.
“[0084] ….. Further, one or more rules may be applied that access product preference information for a particular customer, identify products that correspond to the product preference information, and include those products into the listing of products 634. ….. Additionally, or alternatively, the applied set of rules result in filtering the products identified in the product database based on one or more of a location of one or more retail stores, rates of sales of one or more of the products at one or more retail stores, inventory levels and/or on-hand inventory of one or more of the products at one or more of the retail stores, other such factors, or a combination of two or more of such factors. For example, one or more rules may cause a confirmation that a particular store has a predefined threshold on-hand quantity, an on-hand quantity based on a predicted quantity a customer is expected to purchase, or other such on-hand quantity prior to incorporating the product into the resulting listing of products. Still further, some embodiments may apply one or more rules that evaluate an on-hand quantity relative to a current and/or predicted rate of sale, and determine whether a threshold quantity will be available at a store at one or more times in the future prior to including the product into the listing. Other rules may apply factors such as a store that a customer is visiting and/or expected to visit, on-hand inventory of products at that store, featured and/or on-sale products at that store, frequency of a sales of products at that store, product demand at that store, and/or other such factors. For example, a product may be excluded from a listing of products 634 when an on-hand quantity is less than a threshold.”
As per independent claims 1, 11, and 20, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest the specific combination of claim limitations/elements presently recited in the claims. While each of the individual features may have been known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features in the specific manner claimed other than with impermissible hindsight.
Response to Arguments
Applicant's arguments filed on 04/28/2026 have been fully considered but they are not persuasive.
Applicant argues (Pg. 16): “Specifically, the additional elements recite a specific manner of displaying images in a user interface to the user based on usage which provides a specific improvement over prior systems of aligning images for a given item to a user's preferences, resulting in an improved user interface for electronic devices.”
Please see 35 U.S.C 112(a) rejection with regard to the argued claim limitation. The disclosure discusses the ranking aspect; furthermore, ranking items in certain arrangement based on score is abstract idea itself. One ordinary in skill, can rank the items based on score or preference using paper and pen. This ranking does not involve any improvement to the display technology, neither the position at which the item is being displayed. Note, the claim limitations are different than example 37, in example 37 “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, resulting in an improved user interface for electronic devices.” Instant claims, or specification doesn’t provide any detail regarding improvement into the display technology, rather it uses the generic display at apply it level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See prior arts discussed in allowable subject matter section above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASEEM ASHRAF whose telephone number is (571)270-3948. The examiner can normally be reached Monday-Friday 09:30 A.M-06:00 P.M.
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, Tariq Hafiz can be reached at 571-272-5350. 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.
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
Prosecution Insights