DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 an abstract idea without significantly more.
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) (step 1). 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) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-8 are directed to a process, claims 9-16 are directed to a manufacture, and claims 17-20 are directed to a machine. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving, via a search interface, a search query…; generating a prompt to provide to a first machine-learning language model to identify a set of query tags associated with the search query, wherein the query tags include at least one of: a product concept tag, an attribute tag, or a brand tag for the search query; receiving, as output from the first machine-learning model, the set of query tags associated with the search query; obtaining a list of product tags associated with the search query, wherein a product tag in the list of product tags is associated with a conversion rate of products matching the product tag associated with a historical search query; identifying a set of candidate products for the search query; for each candidate product: obtaining a set of features for the candidate product, wherein at least one or more features are obtained from the list of product tags or the set of query tags; providing the set of features to a second machine-learning model to generate a score for the candidate product; selecting at least a subset of the candidate products based on the scores for the set of candidate products; and transmitting instructions … to cause display of the selected subset of candidate products”. Claim 1.
With the recited additional element reserved for consideration alone and all together combined with its recited roles in the claim under step 2A prong two, a careful analysis of the remaining limitations above results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as certain methods of organizing human activity, for example commercial interactions (including advertising, marketing or sales activities or behaviors). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional element in the present claims is a client device. Claims 9-16 also include a non-transitory computer-readable medium, and claims 17-20 include one or more processors and a memory storing instructions. These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the memories of claims 9-20 merely store instructions intended for execution by processor(s) that are explicitly incorporated only in claims 17-20. The client device performs only two recited roles in the claims and both are simple input/output steps essentially establishing the device only as a node standing in for a user (i.e., serving only as an identified source for a query, and a recipient for instructions intended to cause a display of information). This additional element does not integrate the judicial exception into a practical application because it performs no meaningful role and its participation amounts to no more than tangential user input and output steps to imply that the exception is somehow applied using a generic computer component. The claim is almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of steps without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional element does not improve the functioning of any computer or other technology or technical field, it does not apply the judicial exception with or by use of a particular machine, it does not transform or reduce a particular article to a different state or thing, and it fails to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims and the method itself is described only by way of the intended results of unidentified operations. No particular actions or specific functions are identified and performed by any particularly identified machines. There is also no reference to the use of the method in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that remaining dependent claims 2-8, 10-16, and 18-20 do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that only further describe the data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yates et al. (Patent No.: US 12,488,050 B2) in view of Guo et al. (Patent No.: US 10,922,737 B2).
Yates teaches, a) receiving a search query from a client device, b) generating a prompt to a first machine-learning language model, c) identifying a set of query tags, and d) tags including a product concept tag, attribute tag, or brand tag, and discloses regarding
Claim 1. A method comprising: ● receiving, via a search interface, a search query from a client device (see at least Yates abstract “receive a user search,” figs. 3, 5, 7, c4:20-25 “User interface module 152 functions to receive a user input of a search query … via a user interface of the client device”); ● generating a prompt to provide to a first machine-learning language model to identify a set of query tags associated with the search query, wherein the query tags include at least one of: a product concept tag, an attribute tag, or a brand tag for the search query (see at least Yates c7:1-21: “category or topic tags related to the search request, …. search query, or the augmented search query, is provided to a first search sub-system that utilizes one or more trained generative AI models. The search query, or the augmented search query, is provided as a prompt to the generative AI models. In response, the generative AI models will generate an output of a confabulated listing of items responsive to the prompt. The confabulated listing of items includes one or more text embeddings, image embeddings and/or multimedia embeddings”);
● obtaining a list of product tags associated with the search query, wherein a product tag in the list of product tags is associated with a conversion rate of products matching the product tag associated with a historical search query (see at least Yates fig.6, c1:32-47 “"historically good" items based on user feedback to map representative items to queries,” c3:3-26 “includes at least one or more of the following: query embeddings which are historic embeddings associated with a prior user query,” c24:40-45 “machine learning ranking models for predicting quality, clicks, conversions, and other scores used in allocation”).
● identifying a set of candidate products for the search query (see at least Yates abstract “search result listing is generated,” figs. 3-6, c7:25-30 “generates a second listing of search results using the search query”);for each candidate product:
● providing the set of features to a second machine-learning model to generate a score for the candidate product (see at least Yates figs.2, 6, 8, c5:20-42 “second one or more models may receive output from the primary generative AI model,” c9:47-67 “Example 4. The method of any one of Examples 1-3, wherein generating the first listing further comprises: providing an output of the first generative AI model to a second generative AI model, the second generative AI model trained on domain specific topics and providing for output the first listing from the second generative AI model. Example 7. The method of any one of Examples 1-6, generating the second listing comprises: determining a similarity score for one or more type and identifiers related to the search query; retrieving multiple listings of content based on the determined similarity score”); ● selecting at least a subset of the candidate products based on the scores for the set of candidate products (see at least Yates figs.2, 6, 8, c5:20-42 “second one or more models may receive output from the primary generative AI model,” c9:47-67 “Example 4. The method of any one of Examples 1-3, wherein generating the first listing further comprises: providing an output of the first generative AI model to a second generative AI model, the second generative AI model trained on domain specific topics and providing for output the first listing from the second generative AI model. Example 7. The method of any one of Examples 1-6, generating the second listing comprises: determining a similarity score for one or more type and identifiers related to the search query; retrieving multiple listings of content based on the determined similarity score”); and ● transmitting instructions to the client device to cause display of the selected subset of candidate products on the client device (see at least Yates figs. 3, 5-7, c14:40-50 “In step 512, the further trained generative AI models generate an output of a listing of items responsive to the prompt. In step 514, the listing of items are provided for display via the user interface of the client device,” c24:4-13 “The system may generate and display a user interface of the search ranking results then receives the "update listings" response and potentially updates the listings shown to the user”).
Yates teaches all of the above as noted, but does not explicitly disclose receiving, as output from the first machine-learning model, the set of query tags associated with the search query; and for each candidate product obtaining a set of features for the candidate product, wherein at least one or more features are obtained from the list of product tags or the set of query tags.
Guo also teaches a) receiving a search query from a client device, b) generating a prompt to a first machine-learning language model, c) identifying a set of query tags, and d) tags including a product concept tag, attribute tag, or brand tag, and further discloses wherein the method further comprises:
● receiving, as output from the first machine-learning model, the set of query tags associated with the search query (see at least Guo fig.5, c1: 25-46 “generating a first tag list based on at least one product characteristic corresponding to the target product and the user preference corresponding to at least one user, wherein the first tag list has a plurality of first tags corresponding to different product features,” c3:20-30 “modeling (such as Content similarity, etc.) to filter the product features that have a high correlation with the target product, thereby to generate the product feature tags”); and
for each candidate product: ● obtaining a set of features for the candidate product, wherein at least one or more features are obtained from the list of product tags or the set of query tags (see at least Guo fig.5, c2:12-17 “FIG. 4 is a schematic diagram of a user interface having a product list, a product feature tag list and a target customer characteristics tag list,” c1:26-46 “a first tag list based on at least one product characteristic corresponding to the target product and the … tag list has a plurality of first tags corresponding to different product features; and displaying the product information, the product list, and the first tag list”).
Yates in view of Guo teaches regardingClaim 2. The method of claim 1, further comprising: for one or more products retrieved from a catalogue database, generating a second prompt to provide to the first machine-learning model to identify a set of product tags associated with the product, wherein the product tags include at least one of: ● a product concept tag, an attribute tag, or a brand tag for the product (see at least Yates c5:20-42 “the modified query is sent to a trained generative AI model for input via a prompter…. a primary (e.g., a foundational model) generative AI model for receiving the modified query via the prompter,” c7:1-21 “category or topic tags related to the search request, …. search query, or the augmented search query, is provided to a first search sub-system that utilizes one or more trained generative AI models. The search query, or the augmented search query, is provided as a prompt to the generative AI models. In response, the generative AI models will generate an output of a confabulated listing of items responsive to the prompt. The confabulated listing of items includes one or more text embeddings, image embeddings and/or multimedia embeddings,” 47-57 “providing the search query, or the augmented search query, as a prompt to a first generative AI model”); and ● obtaining the set of product tags for each converted product (see at least Yates fig.6, c1:32-47 “"historically good" items based on user feedback to map representative items to queries,” c3:3-26 “includes at least one or more of the following: query embeddings which are historic embeddings associated with a prior user query,” c24:40-45 “machine learning ranking models for predicting quality, clicks, conversions, and other scores used in allocation”).Claim 3. The method of claim 2, wherein obtaining the list of product tags further comprises: ● for the historical search query, identifying products that users converted on (see at least Yates fig. 6, c1:33-47 “system uses the embeddings of query-representative items to retrieve items …. this technique used human-reviewed items or "historically good" items based on user feedback to map representative items to queries,” c3:3-25 “query embeddings which are historic embeddings associated with … real product item listing embeddings”); ● obtaining the set of product tags for each converted product (see at least see at least Yates fig. 6, c1:33-47 “system uses the embeddings of query-representative items to retrieve items …. this technique used human-reviewed items or "historically good" items based on user feedback to map representative items to queries,” c3:3-25 “query embeddings which are historic embeddings associated with … real product item listing embeddings”); ● identifying, for each product tag, the conversion rate of products matching the product tag (see at least see at least Yates fig. 6, c1:33-47 “system uses the embeddings of query-representative items to retrieve items …. this technique used human-reviewed items or "historically good" items based on user feedback to map representative items to queries,” c3:3-25 “query embeddings which are historic embeddings associated with … real product item listing embeddings”); and ● ranking the product tags according to the conversion rate of products matching the product tags (see at least Yates c24:40-45 “machine learning ranking models for predicting quality, clicks, conversions, and other scores used in allocation”).Claim 4. The method of claim 1, wherein the historical search query includes the search query (see at least Yates fig.6, c1:32-47 “"historically good" items based on user feedback to map representative items to queries,” c3:3-26 “includes at least one or more of the following: query embeddings which are historic embeddings associated with a prior user query”).Claim 5. The method of claim 1, wherein obtaining the one or more features for the candidate product comprises obtaining one or more of: ● a click-through rate (CTR) received for the candidate product given the search query and the attribute tag, the brand tag, or the product concept for the search query (see at least Yates c24:40-45 “the system 600 uses 2nd and 3rd stage machine learning ranking models for predicting quality, clicks, conversions, and other scores used in allocation consume the features in the Feature Pool,” c26: 35-40 “The similarity scores from any one or more of the first through fourth examples may be fed by the system 600 into a click model to predict which item the user will select along with other features like average click rate and price. The click model predicts an optimal list order, and that list order 40 is sent to the user”. Please note: The phrase "one or more of" precedes the recitation of alternative or optional limitations only one of which is required. Language claiming elements in the alternative is anticipated by the presence of any single alternative. Beyond that it does not result in any further limitation because it merely represents contingencies that are not required. Applicant is reminded that optional or conditional elements do not narrow the claims because they can always be omitted. See e.g. MPEP §2111.04 "Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure."; and In re Johnston, 435 F.3d 1381,77 USPQ2d 1788, 1790 (Fed. Cir. 2006) ("As a matter of linguistic precision, optional elements do not narrow the claim because they can always be omitted."). As a result of the optional nature of the listed limitations the present claim could be interpreted as not further limiting claim 1 from which it depends, depending on the interpretation of “obtaining.”).Claim 6. The method of claim 1, wherein identifying the set of candidate products for the search query comprises identifying, at a recall layer, one or more candidate products that match at least one product tag in the list of product tags for the search query (see at least Yates figs. 2, 6, 7, 8, c4:33-41 “module determines similarity of one or more embeddings of the confabulated listings generated from the one or more generative AI models with one or more embeddings for real product items”).Claim 7. The method of claim 1, further comprising: ● obtaining an indication of conversion for a selected product in the subset (see at least Yates fig. 6, c1:33-47 “system uses the embeddings of query-representative items to retrieve items …. this technique used human-reviewed items or "historically good" items based on user feedback to map representative items to queries,” c3:3-25 “query embeddings which are historic embeddings associated with … real product item listing embeddings”); ● generating a training example including the set of features for the selected product and a label indicating conversion of the selected product (see at least Yates figs. 5-6, c1:40-47 “system may track or record the reason "why" certain items were selected as "relevant",” c3:3-26 “query embeddings which are historic embeddings associated with a prior user query; confabulated embeddings generated by the trained generative AI models”); and ● training one or more parameters of the second machine-learning model based on the training example (see at least Yates fig. 5, c3:2-26 “trained generative AI models 136, such as one or more foundation generative AI models and domain refined generative AI models”).Claim 8. The method of claim 1, further comprising: ● obtaining an indication of conversion for a selected product in the subset (see at least Yates fig. 6, c1:33-47 “system uses the embeddings of query-representative items to retrieve items …. this technique used human-reviewed items or "historically good" items based on user feedback to map representative items to queries,” c3:3-25 “query embeddings which are historic embeddings associated with … real product item listing embeddings”); ● generating a training example including the prompt and the output including the set of query tags (see at least Yates fig.5, c5:20-42 “the trained generative AI Model may be publicly trained and may include additional training to prioritize generations relevant to the application domain using standard generative AI task refinement techniques. … The second one or more models may receive output from the primary generative AI model,” c15:3-16 “model may be trained to refine the generative AI model using all listings or documents in a target domain so that generative AI results better resemble the entire corpus of available results,” c14:25-35 “system may provide supervised training where a user may identify one or more search results that are good search results. In other words, a user may confirm search results that are more relative to the prompt. In step 506, the one or more generative AI models may be further trained on the identified good search results”); and ● training one or more parameters of the first machine-learning model based on the training example (see at least Yates fig.5, c5:20-42 “the trained generative AI Model may be publicly trained and may include additional training to prioritize generations relevant to the application domain using standard generative AI task refinement techniques. … The second one or more models may receive output from the primary generative AI model,” c15:3-16 “model may be trained to refine the generative AI model using all listings or documents in a target domain so that generative AI results better resemble the entire corpus of available results,” c14:25-35 “system may provide supervised training where a user may identify one or more search results that are good search results. In other words, a user may confirm search results that are more relative to the prompt. In step 506, the one or more generative AI models may be further trained on the identified good search results”).
Pertaining to computer readable medium claims 9-16
Rejection of claims 9-16 is based on the same rationale noted above. In addition, Yates teaches regarding
Claim 9. A non-transitory computer-readable medium storing instructions (see at least Yates c2:60-65).
Claim 12. The non-transitory computer-readable medium of claim 9, wherein the historical search query includes the search query or is related to the search query (see at least Yates fig.6, c1:32-47 “"historically good" items based on user feedback to map representative items to queries,” c3:3-26 “includes at least one or more of the following: query embeddings which are historic embeddings associated with a prior user query”. Please note: see previous comment concerning optional or alternative limitations.).Claim 14. The non-transitory computer-readable medium of claim 9, wherein identifying the set of candidate products for the search query further comprises identifying one or more candidate products that match at least one product tag in the list or ranked product tags for the search query (see at least Yates figs. 2, 6, 7, 8, c4:33-41 “module determines similarity of one or more embeddings of the confabulated listings generated from the one or more generative AI models with one or more embeddings for real product items”. Please note: see previous comment concerning optional or alternative limitations.).
Pertaining to system claims 17-20
Rejection of claims 17-20 is based on the same rationale noted above. In addition, Yates teaches regarding
Claim 17. A system comprising: ● one or more processors (see at least Yates c2:60-65); and ● a memory storing instructions that when executed by the one or more processors cause the system to perform operations (see at least Yates c2:60-65).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Yates to include receiving, as output from the first machine-learning model, the set of query tags associated with the search query, as taught by Guo since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Guo in the method of Yates. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Gudla et al., Pub. No.: US 2024/0104622 A1: teaches using a machine learning model to rank products in search results by probability of conversion. Uses labels to train the model.
● LINH et al., Pub. No.: US 2014/0089134 A1: teaches creating user tags for products using attributes of the user's profile and a process of creating a list of products that the user can purchase from the attributes. User is prompted for a selection of user tags from a user profile page that include lifestyle attributes, product types, product manufacturers, and product retailers, and responsive to the receipt of the user tags, a list of products is created, ranked, and displayed on the user's mobile device. Teaches storing product tags associated with each of a plurality of products available for purchase and deriving user tags associated with the user based on user preferences, user tags including user gender, product type, product manufacturer, and product retailer; creating a list of products by comparing user tags to product tags, and ranking the list of products by relevance to the tags, price, date of product listing, product manufacturer, and product retailer.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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, Marissa Thein can be reached at 571.272.6764. 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.
/ADAM L LEVINE/Primary Examiner, Art Unit 3689 June 11, 2026
Prosecution Insights