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 .
Claims 1-20 are pending for examination. Claim(s) 1, 11, and 20 have been amended This action is Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/22/2026 has been entered.
Response to Arguments
Applicant's arguments filed 5/22/2026 with respect to the 35 U.S.C. 102/103 rejection(s) have been fully considered but they are not persuasive.
Applicant Argues: For the reasons set forth in applicant's prior response, the cited references do not teach the claim as amended herein. Accordingly, this rejection should be withdrawn.
Examiner’s Response: The examiner respectfully disagrees for the reasons set forth in the Final Rejection dated 3/10/2026. Therefore, those arguments are not found persuasive.
Applicant's arguments filed 5/22/2026 with respect to the 35 U.S.C. 101 rejection(s) have been fully considered but they are not persuasive.
Applicant Argues: The claimed invention recites an improvement to the technical field of by addressing a fundamental limitation of computing systems. Specifically, computing systems cannot inherently understand natural language text the way humans can. Instead, computing systems require structured data to be input and instructions to be executed, which limits their ability to process unstructured data like natural language text. In the context of the claimed invention, these limitations mean that computing systems cannot easily identify which among many reviews are relevant to a user or generate summaries of those reviews.
The specification identifies this as a problem with computing systems in the Background. Specifically, users "find it difficult or time-consuming to search for relevant reviews" because they typically need to perform direct keyword searches within the natural language text of reviews. Specification at [0002]. This requires searching for multiple "variants of words or phrases," but can still leave out relevant reviews if they miss certain variants. Id. These are issues that arise because computing systems do not actually understand natural language text, which limits users to these kinds of structured keyword searches.
The claimed system addresses this problem with computing systems by identifying relevant user reviews using review embeddings and generating the summarized review using a large language model. Specifically, by using a transformer model to generate user review embeddings that can be compared to embeddings representing users and contextual information, the online system can identify relevant user reviews to be summarized by the large language model. The claimed system can thereby overcome the limitations of computing systems in understanding and processing natural language text using the particular claimed embeddings.
Examiner’s Response: The examiner respectfully disagrees. As shown in the rejection below the improvement found in the claim lies within the abstract idea as identified in Step 2A-Prong 1. The “..generating: embeddings comprises applying a transformer model...”, as noted below in the 35 U.S.C. 101 rejection, is noted to be an element in the claimed steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element, even in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The claimed invention also addresses a further technical subproblem that arises when an LLM is used to process data. Large language models have input-size limits called context windows that make it impractical for a computer system to provide every available review for every item to the LLM at once. The claimed system addresses that subproblem by first using embeddings to identify a set of reviews for each item based on a comparison between the user embedding and the review embeddings and then generating the LLM prompt so that the prompt includes the identified set of reviews rather than the entire universe of available reviews. By limiting the user reviews to related ones as identified using the embeddings, the online system ensures that the prompt to the large language model includes relevant user reviews without exceeding a context window of the large language model.
For the reasons set forth above, the claims address the technical problems described above. Accordingly, the claimed invention provides an improvement to a technical field and the § 101 rejection should be withdrawn.
Examiner’s Response: The examiner respectfully disagrees. As shown in the rejection below the improvement found in the claim lies within the abstract idea asidentified in Step 2A-Prong 1. The aforementioned features of “[...] first using embeddings to identify a set of reviews for each item based on a comparison between the user embedding and the review embeddings” is noted to be part of the abstract idea. The “prompting a” LLM, as noted below in the 35 U.S.C. 101 rejection, is noted to be an element in the claimed steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element, even in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Therefore, the examiner finds this argument not persuasive.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-20 are directed to process, manufacture, and/or a machine. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 11 and 20, taken as representative, recites at least the following limitations that recite an abstract idea:
A method,
receiving
generating a review embedding for each user review for each item of the plurality of items based at least in part on a corresponding user review for a corresponding item,
receiving
responsive to the received request:
identifying the set of items;
identifying contextual information associated with a current session of the user
generating a user embedding for the user based at least in part on user data associated with the user and the identified contextual information associated with the user;
comparing the user embedding to a set of review embeddings for each item of the set of items, wherein comparing the user embedding to a review embedding of the set of review embeddings comprises:
computing a difference value between the user embedding and the review embedding;
identifying a set of user reviews for each item of the set of items based at least in part on the comparing;
generating a prompt for a summarized review for each item of the set of items, wherein the prompt comprises the identified set of user reviews for each item of the set of items and a request to summarize, for the user, the identified set of user reviews for each item of the set of items;
providing the prompt
sending
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 11 and 20 includes receiving reviews and generating embeddings, receiving a request for information describing a set of items; and responsive to the request - identifying the items, generating an embedding for the set of items, generating a prompt for a summarized review and obtaining a summarized review; and sending a summarized review along with the items., thus, claim 1, and similar claim(s) 11 and 20, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" or "legal interactions" in the form of marketing or sales activities.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 11 and 20, recite i.e., system with a processor/medium, online system, client device, transformer/language model (i.e., applying/prompting), user interface non-transitory... medium, processor. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, ⁋[0011], ⁋ [0025]). These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 11 and 20 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 11 and 20 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 11 and 20 are “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 11 and 20, i.e., system with a processor/medium, online system, client device, transformer/language model (i.e., applying/prompting), user interface non-transitory... medium, processor; thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 11 and 20that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 11 and 20 is ineligible.
Regarding Claims 2-10 and 12-19, claims 2-10 and 12-19 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of “commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations i.e., further features related to summarized reviews. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (e.g., Claim 10 – augmented reality). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1-4, 6-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (US 20180075110 A1) in view of Jindal et al. (US 11,886,809 B1).
Regarding Claim 1;
Cho discloses a method, performed at a computer system comprising a processor and a computer-readable medium ([0019]-[0020] and [0022]), comprising:
receiving, at an online system, a plurality of user reviews for a plurality of items included among one or more inventories of one or more retailers associated with the online system, wherein each user review of the plurality of user reviews is associated with an item of the plurality of items and comprises natural language text relating got the item ([0031] - In another embodiment, a single computer system can host each of topic modeling system 310, web server 320, display system 360, and user attribute system 370 and [0037] - For example, web server 320 can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities and [0045]-[0046] - Method 400 can comprise an activity 405 of receiving a plurality of user reviews of a product. For example, reviews module 512 (FIG. 5) of topic modeling system 310 (FIG. 5) can be configured to receive a plurality of user reviews of one or more products);
generating a review embedding for each user review for each item of the plurality of items based at least in part on a corresponding user review for a corresponding item ([0031] and [0037] and [0045]-[0046] - Method 400 can further comprise an activity 410 of performing topic modeling of the plurality of user reviews of the product to find or extract at least one snippet within the plurality of user reviews relating to at least one user attribute category of a plurality of user attribute categories);
receiving, from a client device associated with a user of the online system, a request for information describing a set of items ([0058] - In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user.); and
responsive to the received request ([0058]):
identifying the set of items (FIG. 7 and [0058] - In some embodiments, such as the non-limiting embodiment shown in the screenshot of FIG. 7, facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only a picture of the product, a price of the produce, an overall rating of the product, a name of the product, and/or a snippet of a user review. In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user);
identifying contextual information associated with a current session of the user with the online system ([0046] - Method 400 can further comprise an activity 410 of performing topic modeling of the plurality of user reviews of the product to find or extract at least one snippet within the plurality of user reviews relating to at least one user attribute category of a plurality of user attribute categories [...] In many embodiments, user attribute categories comprise value-conscious, quality-conscious, brand-conscious, product popularity, gender, age, location, and/or any combination thereof. Performing topic modeling of the plurality of reviews of a product to find or extract at least one snippet can be accomplished according to any topic modeling or other snippet generation known in the art configured to find a snippet within a review relating to a desired aspect or user attribute category, such as but not limited to running variant of topic models of the plurality of reviews);
generating a user embedding for the user based at least in part on user data associated with the user and the identified contextual information associated with the user ([0046] - Method 400 can further comprise an activity 410 of performing topic modeling of the plurality of user reviews of the product to find or extract at least one snippet within the plurality of user reviews relating to at least one user attribute category of a plurality of user attribute categories. For example, topic modeling module 514 (FIG. 5) of topic modeling system 310 (FIG. 5) can be configured to run various topic modeling of the plurality of user reviews of a product to find at least one snippet within the plurality of user reviews relating to at least one user attribute category);
comparing the user embedding to a set of review embeddings for each item of the set of items ([0048]-[0049] - A first snippet determined to have a higher score for a first user attribute category has a higher probability of association with the first user attribute category than a second snippet determined to have a lower score for the first user attribute category relative to the first snippet. Thus, when selecting snippets as described below, a snippet with a higher score for a particular user attribute category may be selected for display proximate a product. Scoring of the snippet can be accomplished when performing topic modeling on the user reviews);
identifying a set of user reviews for each item of the set of items based at least in part on the comparing ([0048]-[0049] - A first snippet determined to have a higher score for a first user attribute category has a higher probability of association with the first user attribute category than a second snippet determined to have a lower score for the first user attribute category relative to the first snippet. Thus, when selecting snippets as described below, a snippet with a higher score for a particular user attribute category may be selected for display proximate a product. Scoring of the snippet can be accomplished when performing topic modeling on the user reviews);
generating a prompt for a summarized review for each item of the set of items, wherein the prompt comprises the identified set of user reviews for each item of the set of items and a request to summarize, for the user, the identified set of user reviews for each item of the set of items (FIG. 7 and [0058] - In some embodiments, such as the non-limiting embodiment shown in the screenshot of FIG. 7, facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only a picture of the product, a price of the produce, an overall rating of the product, a name of the product, and/or a snippet of a user review. In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user);
providing the prompt to a large language model to obtain the summarized review for each item of the set of items ([0046] - In some embodiments, a natural language processor is utilized to parse natural language of user reviews to find at least one snippet within the plurality of user reviews relating to at least one user attribute category [...] As used herein, a snippet refers to one or more sentences or phrases within a user review. In many embodiments, user attribute categories comprise value-conscious, quality-conscious, brand-conscious, product popularity, gender, age, location, and/or any combination thereof. Performing topic modeling of the plurality of reviews of a product to find or extract at least one snippet can be accomplished according to any topic modeling or other snippet generation known in the art configured to find a snippet within a review relating to a desired aspect or user attribute category, such as but not limited to running variant of topic models of the plurality of reviews); and
sending a user interface for display to the client device associated with the user, wherein the user interface comprises the set of items and the summarized review for each item of the set of items (FIG. 7 and [0058] - In some embodiments, such as the non-limiting embodiment shown in the screenshot of FIG. 7, facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only a picture of the product, a price of the produce, an overall rating of the product, a name of the product, and/or a snippet of a user review. In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user).
Cho fails to explicitly disclose:
...wherein generating a review embedding for a user review comprises applying a transform model to the natural language text of the user review;
[...]
...wherein comparing the user embedding to a review embedding of the set of review embeddings comprises:
computing a difference value between the user embedding and the review embedding.
However, in an analogous art, Jindal teaches
...wherein generating a ... embedding ... comprises applying a transform model to the natural language text... (col. 3, lines 4-16 - The identification system leverages a natural language model (e.g., Word2Vec) trained on a corpus of text to learn relationships between words and identify similar words (e.g., based on semantic similarity) to generate embeddings for the intent phrases and the tags in an embedding space. For example, the natural language model generates the embeddings such that embeddings generated for intent phrases and tags are separated by a relatively small distance in the embedding space if words in the intent phrases are similar to words in the tags. Conversely, the embeddings generated for the intent phrases and the tags are separated by a relatively large distance in the embedding space if words in the intent phrases are not similar to words in the tags.)
[...]
...wherein comparing the ... embedding to an [other] embedding of the set of ... embeddings comprises:
computing a difference value between the ... embedding and the [other] embedding (col. 3, lines 4-16 - The identification system leverages a natural language model (e.g., Word2Vec) trained on a corpus of text to learn relationships between words and identify similar words (e.g., based on semantic similarity) to generate embeddings for the intent phrases and the tags in an embedding space. For example, the natural language model generates the embeddings such that embeddings generated for intent phrases and tags are separated by a relatively small distance in the embedding space if words in the intent phrases are similar to words in the tags. Conversely, the embeddings generated for the intent phrases and the tags are separated by a relatively large distance in the embedding space if words in the intent phrases are not similar to words in the tags and co.. 4, lines 46-51 - As used herein, the term “embedding” refers to a numerical representation of a word or multiple words in an embedding space. By way of example, embeddings for words and embeddings for other words that are generated by a trained natural language model are comparable to infer similarities and differences between the words and the other words.)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Abhyankar to review embeddings for a user review/user embedding/review embedding of the set of review mebeddings of Cho to include wherein generating a ... embedding ... comprises applying a transform model to the natural language text...; [...]; ...wherein comparing the ... embedding to an [other] embedding of the set of ... embeddings comprises: computing a difference value between the ... embedding and the [other] embedding.
One would have been motivated to combine the teachings of Jindal to Cho to do so as it provides / allows unchallenging identification (Jindal, as gleaned, col. 2, lines 41-53).
Regarding Clam 2;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein identifying the contextual information associated with the current session of the user with the online system comprises: identifying one or more surfaces for presenting the summarized review for each item of the set of items, wherein the one or more surfaces comprise one or more selected from the group consisting of: a set of search results, a set of browsing results, and a set of advertisements (FIG. 7 and [0037] - For example, web server 320 can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities and [0058] - In some embodiments, such as the non-limiting embodiment shown in the screenshot of FIG. 7, facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only a picture of the product, a price of the produce, an overall rating of the product, a name of the product, and/or a snippet of a user review. In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user. In some embodiments, display of the one or more snippets and respective products is determined by an administrator of an ecommerce website for promotional purposes in alternative or addition to a user search query. Facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only the snippet of the user review proximate or adjacent to a display of the product on the device.).
Regarding Clam 3;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein identifying the contextual information associated with the current session of the user with the online system is based at least in part on information describing one or more of: the received request for information describing the set of items and a set of items included in a shopping list associated with the user (FIG. 7 and [0037] - For example, web server 320 can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities and [0046] - In some embodiments, such as the non-limiting embodiment shown in the screenshot of FIG. 7, facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only a picture of the product, a price of the produce, an overall rating of the product, a name of the product, and/or a snippet of a user review. In some embodiments, display of the one or more snippets and respective products is responsive to a search query entered by the user. In some embodiments, display of the one or more snippets and respective products is determined by an administrator of an ecommerce website for promotional purposes in alternative or addition to a user search query. Facilitating the display on the device of the snippet proximate the product can comprise facilitating the display of only the snippet of the user review proximate or adjacent to a display of the product on the device).
Regarding Clam 4;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses, further comprising: generating a plurality of prompts for the summarized review for each item of the set of items ([0046]); and selecting a prompt from the plurality of prompts based at least in part on a set of previous interactions by the user with one or more items that indicate a performance of each prompt of the plurality of prompts ([0052] - Determining the user attribute category can comprise determining the user attribute category based upon a browsing or search history of the user and/or a profile information of the user. For example, if a user is signed into an account on an ecommerce website, one or more of the plurality of user attribute categories may be known for the user from the profile information entered by the user when registering for the account on the ecommerce website. In some embodiments, a user may select the user attribute categories in which the user is interested. In some embodiments, a user attribute may be determined by the location of the user or the ecommerce website navigation history of the user. In some embodiments, determining a user attribute category can comprise defining one or more models around predefined behavior and mining for patterns indicative to particular user segments and/or user attribute categories. In some embodiments, patterns of interest in activity that can be clustered are found given historic data pertaining to user activity. Semantic associations or behavior tags to these clusters are usually assigned by introspecting samples from the populations.)
Regarding Clam 6;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein generating the user embedding for the user comprises: retrieving the user data associated with the user, wherein the user data comprises one or more selected from the group consisting of: a set of demographic information associated with the user, a set of interests associated with the user, a set of orders placed by the user with the online system, and a set of interactions by the user with the online system ([0037] - For example, web server 320 can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities. [0046] - In many embodiments, user attribute categories comprise value-conscious, quality-conscious, brand-conscious, product popularity, gender, age, location, and/or any combination thereof and [0052] - Determining the user attribute category can comprise determining the user attribute category based upon a browsing or search history of the user and/or a profile information of the user. For example, if a user is signed into an account on an ecommerce website, one or more of the plurality of user attribute categories may be known for the user from the profile information entered by the user when registering for the account on the ecommerce website. In some embodiments, a user may select the user attribute categories in which the user is interested. In some embodiments, a user attribute may be determined by the location of the user or the ecommerce website navigation history of the user. In some embodiments, determining a user attribute category can comprise defining one or more models around predefined behavior and mining for patterns indicative to particular user segments and/or user attribute categories. In some embodiments, patterns of interest in activity that can be clustered are found given historic data pertaining to user activity. Semantic associations or behavior tags to these clusters are usually assigned by introspecting samples from the populations.); and generating the user embedding for the user based at least in part on the user data associated with the user ([0046])
Regarding Clam 7;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein receiving the plurality of user reviews for the plurality of items included among the one or more inventories of the one or more retailers associated with the online system comprises: receiving one or more selected from the group consisting of: a title for a corresponding user review, information identifying a user associated with a corresponding user review, a date that a corresponding user review was received, a rating associated with an item, and information describing a reason for the rating (FIG.6 – depicts a title (i.e., Very solid phone for the price), information identifying user associated with a corresponding user revie (i.e., An anonymous customer), a rating arrocited with an time (i.e., star based rating), and information describing a reason for the rating (i.e., 600) and [0048]).
Regarding Clam 8;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein generating the review embedding for each user review for each item of the plurality of items is based at least in part on one or more types of content included in the corresponding user review for the corresponding item, the one or more types of content selected from the group consisting of: text content, image content, and video content (FIG. 6 and [0048])
Regarding Clam 9;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein comparing the user embedding to the set of review embeddings for each item of the set of items comprises: determining a measure of similarity between the user embedding and each review embedding of the set of review embeddings, wherein the measure of similarity is selected from the group consisting of: a cosine similarity, a Euclidean distance, and a dot product ([0055]).
Regarding Clam 10;
Cho in view of Jindal discloses the method to claim 1.
Cho further discloses wherein the client device associated with the user is an augmented reality device ([0035]-[0036] - [0035] In specific examples, a wearable user computer device can comprise a head mountable wearable user computer device (e.g., one or more head mountable displays, one or more eyeglasses, one or more contact lenses, one or more retinal displays, etc.) or a limb mountable wearable user computer device (e.g., a smart watch). In these examples, a head mountable wearable user computer device can be mountable in close proximity to one or both eyes of a user of the head mountable wearable user computer device and/or vectored in alignment with a field of view of the user. In more specific examples, a head mountable wearable user computer device can comprise (i) Google Glass™ product or a similar product by Google Inc. of Menlo Park, Calif., United States of America; (ii) the Eye Tap™ product, the Laser Eye Tap™ product, or a similar product by ePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product, the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or a similar product by Vuzix Corporation of Rochester, N.Y., United States of America. In other specific examples, a head mountable wearable user computer device can comprise the Virtual Retinal Display™ product, or similar product by the University of Washington of Seattle, Wash., United States of America. Meanwhile, in further specific examples, a limb mountable wearable user computer device can comprise the iWatch™ product, or similar product by Apple Inc. of Cupertino, Calif., United States of America, the Galaxy Gear or similar product of Samsung Group of Samsung Town, Seoul, South Korea, the Moto 360 product or similar product of Motorola of Schaumburg, Ill., United States of America, and/or the Zip™ product, One™ product, Flex™ product, Charge™ product, Surge™ product, or similar product by Fitbit Inc. of San Francisco, Calif., United States of America).
Regarding Claim(s) 11-14 and 16-19 claim(s) 11-14 and 16-19 is/are directed to a/an medium associated with the metho claimed in claim(s) 1-4 and 6-9. Claim(s) 11-14 and 16-19 is/are similar in scope to claim(s) 1-4 and 6-9, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 20; claim(s) 20 is/are directed to a/an system associated with the method claimed in claim(s) 1. Claim(s) 20 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (US 20180075110 A1) in view of Jindal et al. (US 11,886,809 B1) and further in view of Abhyankar et al. (US 2020/0301953 A1).
Regarding Clam 5;
Cho in view of Jindal discloses the method to claim 4.
Cho in view of Jindal fails to explicitly disclose wherein selecting the prompt from the plurality of prompts is based at least in part on one or more of: an offline evaluation method for the plurality of prompts and a result of an A/B test performed on the plurality of prompts.
However, in an analogous art, wherein selecting the prompt from the plurality of prompts is based at least in part on one or more of: an offline evaluation method for the plurality of prompts and a result of an A/B test performed on the plurality of prompts ([0063] - For example, the server system 110 can implement a form of A/B testing where documents, prompts, user interface labels, and other content to a first group of users uses one term in a candidate synonym pair, while the same content uses a second term in the candidate synonym pair for the same content. The server system 110 can track the user interactions of users in both groups, to determine whether and to what extent user behavior changes due to the use of the different terms).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Abhyankar to the prompts of Cho in view of Jindal to include wherein selecting the prompt from the plurality of prompts is based at least in part on one or more of: an offline evaluation method for the plurality of prompts and a result of an A/B test performed on the plurality of prompts.
One would have been motivated to combine the teachings of Abhyankar to Cho in view of Jindal to do so as it provides / allows to adjust the weights in the semantic graph to alter search results, recommendations, application behavior, and other aspects of the user's experience (Abhyankar, [0044]).
Regarding Claim(s) 15; claim(s) 15 is/are directed to a/an medium associated with the method claimed in claim(s) 5. Claim(s) 15 is/are similar in scope to claim(s) 5, and is/are therefore rejected under similar rationale.
Conclusion
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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