DETAILED ACTION
The following is a non-final, first office action in response to the application filed May 28, 2024. Claims 1-20 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 § 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 (abstract idea) without significantly more.
Step 1: Statutory Category
(MPEP § 2106)
Claims 1-20 are directed towards a method, computer-readable medium, and system. The claims are directed to a statutory category: a process, an article of manufacture, and a machine as defined under 35 U.S.C. § 101.
Regarding Claim 1:
Step 2A, Prong One: Judicial Exception – Abstract Idea
(MPEP § 2106.04)
Claim 1 recites an abstract idea in the form of certain methods of organizing human activity and mental processes.
The limitations reciting:
generating a prompt for a machine-learned language model to output a list of item categories predicted to be in-season for a time period and geographical location;
receiving a response including the predicted list of item categories;
retrieving historical user engagement data corresponding to the item categories;
correlating an increase in engagement level during a first time period relative to other time periods;
validating item categories as being in season based on the correlation;
updating an item catalog by tagging corresponding items with an in-season badge; and
generating an ordering interface including the tagged items
collectively describe the commercial practice of identifying products likely to be desirable during a particular season and promoting those products to users. Such activities constitute advertising, marketing, sales activities, and managing commercial interactions, which are certain methods of organizing human activity.
Additionally, the limitations of evaluating engagement information, correlating engagement levels across time periods, determining whether categories are in season, and validating categories based on the correlation recite observations, evaluations, judgments, and opinions that can be performed in the human mind or with the aid of pen and paper, and therefore constitute mental processes.
Step 2A, Prong Two: Integration into a Practical Application
(MPEP § 2106.04(d))
The additional elements recited beyond the abstract idea include:
an online system;
a requesting user client device;
a model serving system;
a machine-learned language model;
an item catalog;
an in-season badge; and
an ordering interface.
The additional elements do not integrate the judicial exception into a practical application. The claimed machine-learned language model is merely used as a tool to generate predicted item categories. The claim does not improve the functioning of the machine-learned language model, improve model training, improve inference operations, reduce computational resource usage, or otherwise improve computer technology. Rather, the machine-learned language model is used to perform the abstract task of generating recommendations.
Likewise, retrieving engagement data, validating categories based on correlations, updating catalog information with badges, and displaying the resulting items within an ordering interface merely apply the abstract idea in a computerized environment and present the results of the analysis to a user. The claim does not recite an improvement to the operation of the client device, online system, catalog database, graphical user interface technology, or any other technology or technical field. Instead, the additional elements serve only to collect information, analyze information, and display the results of that analysis.
Step 2B: Inventive Concept
(MPEP § 2106.05)
The claim does not include additional elements that amount to significantly more than the judicial exception.
The online system, user client device, model serving system, machine-learned language model, item catalog, and ordering interface are recited at a high level of generality and perform generic computer functions including receiving information, processing information, storing information, updating records, and displaying results. These functions are well-understood, routine, and conventional computer activities.
Considering the claim elements individually and as an ordered combination, the claim merely automates the abstract business practice of determining seasonally relevant product categories, validating those categories using historical engagement information, and presenting corresponding products to users. The ordered combination does not provide an unconventional technical solution or improve computer functionality. Instead, the claim uses generic computer components as tools to implement the abstract idea.
Therefore, the claim is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding Claim 11 and 20
Independent claims 11 and 20 are parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-10 and 12-19
Dependent claims 2-10 and 12-19merely set forth further embellishments to the abstract idea, and therefore do not confer eligibility on the claimed invention and are ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan et al (US 2022/0245702 A1) in view of Nelson et al (US 2020/0401976 A1).
Regarding claims 1, 11, and 20, Sundaresan discloses method executed by an online system, the method comprising:
generating a prompt for a machine-learned language model to output a list of item categories, wherein the item categories in the list are predicted to be in-season for a first time period and a geographical location of a requesting user client device (Sundaresan: paragraph [0087] - For example, filtering can be performed for certain geographic regions, for certain characteristics of purchases (e.g., in store pick-up, delivery, etc.) or for other characteristics.);
providing the prompt to a model serving system for execution by the machine-learned language model (Sundaresan: paragraph [0004] - A seasonality model can be used in which the user seasonality embeddings, the final item seasonality embeddings as well as non-seasonal item embeddings and non-seasonal user embeddings can be input into a machine learning model to determine final user-item scores that can indicate a seasonality of each item in a catalog of items that are available on the ecommerce marketplace);
receiving, from the model serving system, a response generated by the machine-learned language model including the list of item categories, wherein the item categories in the list are predicted to be in-season for the first time period and the geographical location (Sundaresan: Figure 9 - provide seasonal item recommendations 920);
validating that each item category in the list of item categories is in season for the first time period by: retrieving historical user engagement data by requesting users of the online system with items in an item catalog corresponding to the item category, correlating an increase in engagement level during the first time period relative to other time periods, and validating the item category as in season for the first time period based on the correlation (Sundaresan: Figure 9 - aggregate category buyers data 914).
Sundaresan does not expressly disclose updating the item catalog by tagging, with an in-season badge, one or more items corresponding to the validated list of item categories; and generating an ordering interface that includes the one or more items tagged with the in-season badge for display on the requesting user client device, wherein the generating causes the requesting user client device to display the ordering interface. Nelson discloses:
updating the item catalog by tagging, with an in-season badge, one or more items corresponding to the validated list of item categories; and generating an ordering interface that includes the one or more items tagged with the in-season badge for display on the requesting user client device, wherein the generating causes the requesting user client device to display the ordering interface (Nelson: paragraph [0034] - The seasonality indicator (seasonal/not seasonal for each season) may be defined for each subcategory.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and apparatus of Sundaresan to have included updating the item catalog by tagging, with an in-season badge, one or more items corresponding to the validated list of item categories; and generating an ordering interface that includes the one or more items tagged with the in-season badge for display on the requesting user client device, wherein the generating causes the requesting user client device to display the ordering interface, as taught by Nelson because it would provide product indicators for sales improvement (Nelson: paragraph [0003]).
Regarding claims 2 and 12, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. Sundaresean further discloses wherein the first time period is a portion of a year (Sundaresean: paragraph [0087] -In other examples, other filtering can be performed such as retaining data for other periods of time such as for the last one year, for the last three years, for the last five years or for other periods of time).
Regarding claims 3 and 13, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. Sundaresean further discloses identifying the geographical location based on where the requesting user is located (Sundaresean: paragraph [0087] - For example, filtering can be performed for certain geographic regions, for certain characteristics of purchases (e.g., in store pick-up, delivery, etc.) or for other characteristics.).
Regarding claims 5 and 15, Sundaresean and Nelson teach or suggest all the limitations of claims 4 and 14 as noted above. Sundaresean further discloses wherein generating the prompt further comprises: generating the prompt to include instructions to provide one or more example item categories known to be in-season (Sundaresean: Figure 9 - 920).
Regarding claims 6 and 16, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. Sundaresean further discloses wherein correlating the increase in engagement level during the first time period comprises applying a scoring function that disparately weights different types of actions taken by requesting users in relation to the items in the item catalog corresponding to the item category. (Sundaresean: Figure 8, paragraph [0082] - Matrix factorization can be used train and then determine the final user-item scores 820. In this example, the product of the item weight matrix 2 816 and the final item seasonality embeddings 818 is combined with a product of the item weight matrix 1 806 and the item embeddings 80).
Regarding claims 7 and 17, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. Sundaresean further discloses wherein validating that each item category in the list of item categories is in season for the first time period further comprises, for each seasonal item category: generating a subsequent prompt for the machine-learned language model to output a list of time periods when the item category is in season; providing the subsequent prompt to the model serving system for execution by the machine-learned language model; and receiving, from the model serving system, a response generated by the machine-learned language model including the list of time periods that the item category is in season, wherein correlating the increase in engagement level during the first time period comprises correlating the historical user engagement to the list of time periods output by the machine-learned language model (Sundaresean: Figure 9 - aggregate category buyers data 914).
Regarding claims 8 and 18, Sundaresean and Nelson teach or suggest all the limitations of claims 7 and 17 as noted above. Sundaresean further discloses wherein generating the subsequent prompt further comprises: generating the subsequent prompt to output the list of time periods when the item category is in season further based on the geographical location (Sundaresean: paragraph [0005] - for example, in some embodiments, a seasonal recommender system can include a computing device configured to obtain periodic sales data characterizing a number of purchases made of each item of a plurality of items in a specified period and to obtain periodic buyers data characterizing a number of unique customers of each item in the plurality of items in the specified period).
Regarding claims 9 and 19, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. Sundaresean further discloses storing each item in the item catalog under one item category in a master list of item categories; and mapping each item category in the validated list of item categories predicted to be in season to one or more item categories in the master list of item categories, wherein updating the item catalog comprises tagging the one or more items stored under the mapped item categories in the master list of item categories with the in-season badge (Sundaresean: paragraph [0004] - Such seasonal trends and patterns can be extracted at both the item level and the category level and can be stored as item seasonal embeddings, category seasonality embeddings).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan et al (US 2022/0245702 A1), in view of Nelson et al (US 2020/0401976 A1), and further in view of Ma et al (US 12,079855).
Regarding claims 4 and 14, Sundaresean and Nelson teach or suggest all the limitations of claims 1 and 11 as noted above. The combination of Sundaresean and Nelson does not disclose wherein generating the prompt further comprises: generating the prompt to include instructions to order the list of seasonal item categories based on confidence in seasonality prediction. However, Ma teaches wherein generating the prompt further comprises: generating the prompt to include instructions to order the list of seasonal item categories based on confidence in seasonality prediction (Ma: claim 1 - inputting the user-specific historical transaction data into the trained first machine learning model to generate user-specific seasonal product affinity scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the combination of Sundaresean and Nelson, in the apparatus and method wherein generating the prompt further comprises: generating the prompt to include instructions to order the list of seasonal item categories based on confidence in seasonality prediction, as taught by Ma since the claimed invention is just a combination of old elements, and in the combination each element merely would have performed that same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so because it would account for seasonal preferences (Ma: column 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PTO-892 Reference U discloses Seasonal Relevance in E-Commerce Search
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KATHLEEN GAGE PALAVECINO
Primary Examiner
Art Unit 3688
/KATHLEEN PALAVECINO/Primary Examiner, Art Unit 3688
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