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 .
Status of Claims
This office action is in response to the amendment filed on 7/10/2026.
Claims 1 and 2 have been amended. Claims 14 and 17-30 have been canceled.
Claims 31-35 have been added.
Claims 1-13, 15, 16, and 31-35 are pending and have been examined.
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 1-13, 15, 16, and 31-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-13, 15, and 16 are directed to a method. Claims 31-34 are directed to one or more non-transitory computer storage media. Claims 35 is directed to a system. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claims 1, 31, and 35 recite virtually identical limitations. Claim 1 will be used as representative. Each claims additional elements will be addressed individually.
Claims 1, 31, and 35:
obtaining, from a user that displays first search results generated by a search entity for a user, data indicating interactions of the user with one or more items of the first search results;
generating, based on the obtained interaction data, a review that predicts content of a review of at least one of the one or more items generated by the user;
executing a review generation objective function based on the predicted content of the review;
adjusting, based on the output of the executed review generation objective function, one or more parameters of the search entity; and
using the search entity to generate second search results for the user, wherein the second search results are different than the first search results.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
Claims 2, 32:
wherein using the updated search entity to generate the second search results for the user comprises:
obtaining item listings using a query analyzer entity in response to a user query; and
ranking the obtained item listings using a ranking entity.
Claims 3, 33:
wherein the ranking entity uses the generated review to rank the obtained item listings.
Claim 4:
wherein the query analyzer entity uses the generated review to obtain the item listings.
Claims 5, 34:
wherein generating the review occurs during a same session as the display of the first search results.
Claim 6:
wherein obtaining the interaction data comprises:
obtaining a signal indicating a user selection of the user
Claim 8:
wherein obtaining the interaction data comprises:
obtaining at least one or more of the following: listing content, quality signals indicative of user interactions with the one or more items of the first search results, lister identification for the one or more items of the first search results, conversations between the user and lister for the one or more items of the first search results, shipping information, delivery information, search queries, search queries within a time range, or search queries within the same session.
Claim 11:
wherein training the review generation entity comprises:
using a discriminator network to determine whether reviews generated by the review generation entity were generated by humans or not.
Claim 12:
wherein the generated review includes a written portion in a language of the user.
Claim 13
wherein the generated review includes a rating portion indicating a rating of the at least one of the one or more items.
Claim 15:
comprising: receiving a first query and generating the first search results in response to receiving the first query; and
receiving a second query and generating the second search results in response to receiving the second query, wherein the first query and the second query are the same.
Claim 16:
comprising: providing the second search results to the user
The claims provide a manner of obtaining interaction data of a user with one or more items of search results, generating a review that predicts content of a review based on the interaction data, executing a review objective function, adjusting parameters based on the objective function, and use the generated review to update the search results. But for the inclusion of generic computing components, the claims can be performed in the human mind or with pen and paper. A human analog would be able to obtain the interaction data, generate a review based on the interaction data, execute an objective function, adjust parameters, and use the data to update search results. As a result, the claims fall within the “mental process” grouping of abstract idea.
Additionally, such activity is considered as certain methods of organizing human activity. Providing search result information to a user, monitoring their interactions with an item of the search results, using the interaction data to generate a review, and then using the review to update search results is both commercial interactions and managing personal relationships and behaviors. As discussed in the spec (paragraphs [0001]-[0004]), the claims are providing improved search results for users to obtain goods or services.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: script executing on a user device/ user device (claim 1, 6, 7, 16, 31, 35); search engine (claim 1, 2, 14, 31, 32, 35); review generation engine (claim 1, 9, 10, 11, 31, 35); query analyzer engine (claim 2, 4, 32); ranking engine (claim 2, 3, 32, 33); graphical user interface (claim 6, 7); discriminator network (claim 11); wherein obtaining the interaction data comprises: obtaining a signal indicating a mouse hovering over a portion of a graphical user interface (GUI) of the user device. (claim 7); comprising: training the review generation engine using a supervised learning technique (claim 9); comprising: training the review generation engine using an unsupervised learning technique. (claim 10); one or more non-transitory computer storage media encoded with program instructions (claim 31); one or more computers and one or more storage devices on which are stored instructions (claim 35);
The user device, one or more non-transitory computer storage media encoded with program instructions, and one or more computers and one or more storage devices on which are stored instructions are recited at a high level of generality and merely implements the abstract idea using a generic computing device (spec [0037]). The script executing on a user device, search engine, review generation engine, query analyzer engine, ranking engine, and discriminator network are recited at a high level of generality and merely “apply it” (the abstract idea) using generic computing devices. The various engines are merely names for the software modules used to implement the abstract idea (spec [0038]). Claim 1 does not outright recite a computing device, however, it can be inferred that a computer is required to implement the various recited engines. However, nothing in the claims improves upon computers, technology, or a technical field (See MPEP 2106.05(f)).
The graphical user interface is recited at a high level of generality. The interface appears to be a generic interface with no specific interface elements claimed and merely used to display information and allow a user to make selections. Nothing in the claims improves upon interfaces, technology, or a technical field. Thus, the interface does not go beyond the “apply it” level of implementation (See MPEP 2106.05(f)).
The high-level recitation of training the review generation engine using a supervised learning technique and training the review generation engine using an unsupervised learning technique are recited at a high level of generality does not go beyond the “apply it” level of implementation. Nothing in the claims improves upon supervised learning or unsupervised learning (See MPEP 2106.05(f)).
The limitations regarding “wherein obtaining the interaction data comprises: obtaining a signal indicating a mouse hovering over a portion of a graphical user interface (GUI) of the user device” is only tangentially related to the invention. Nothing in the claims improves upon techniques for tracking mouse hovering, technology, or a technical field. Thus, such limitation is considered insignificant extra solution activity (See MPEP 2106.05(g)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea and provide insignificant extra solution activity of data gathering.
Further, tracking mouse hovering on an interface is well understood, routine, and conventional at the time of the invention. (See https://web.archive.org/web/20241108074731/https://thestory.is/en/journal/heatmap-and-mouse-tracking/ - “Mouse cursor tracking and mouse heatmaps are increasingly popular research methods, especially in usability testing of web applications (UX testing).” – 2024; https://www.technologyreview.com/2011/05/20/259194/the-next-big-thing-in-analytics-tracking-your-cursors-every-move/ - method of tracking mouse hovering to determine track popularity of features of an interface – 2011; https://medium.com/design-bootcamp/mouse-tracking-what-it-is-and-how-to-use-to-understand-user-behaviour-30180e6da44c - “A really common use of Mouse Tracking occurs in marketing testing. You can track your user’s mouse movements on your marketing landing pages to analyze how effective these pages are at converting users.” – 2021; https://www.reddit.com/r/privacy/comments/f0z0tj/almost_every_website_you_visit_records_exactly/?rdt=37390#:~:text=Mouse%20tracking%20is%20used%20to%20see%20if,try%20doing%20something%20over%20and%20over%20again. – “Almost Every Website You Visit Records Exactly How Your Mouse Moves” – 2020; https://pmc.ncbi.nlm.nih.gov/articles/PMC8219569/ - “Mouse cursor tracking has become a prominent method for characterizing cognitive processes, used in a wide variety of domains of psychological science” – 2020;)
As a result, the claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claim(s) 1-4, 6, 8-13, 16, 31-33, and 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al (US 11,030,535) in view of Harris et al (US 10,997,641) in view of Riley et al (US 8,195,654)
As per claims 1, 31, and 35:
Claims 1, 31, and 35 recite virtually identical limitations. Limitations unique to each claim will be addressed individually. Limitations that are common to the claims will be addressed together.
Arora teaches:
Claim 1:
A method for operating an exchange platform, the method comprising: ([C3L53-C4L12])
Claim 31:
One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: ([C3L53-C4L12])
Claim 35:
A system comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: ([C3L53-C4L12])
Claims 1, 31, 35:
obtaining, using a script executing on a user device that displays first search results generated by a search engine for a user, data indicating interactions of the user with one or more items of the first search results; ([C3L20-53] The host 110 can be any type of entity that hosts an electronic marketplace or a similar portal (e.g., a storefront) that allows customers 102 to acquire (e.g., complete transactions for) items available via the portal. For example, the host 110 may represent an online retailer, or a host of any type of site allowing for online transacting, such as sites including, without limitation, informational sites, social networking sites, deal sites, group buying sites, blog sites, search engine sites, news and entertainment sites, and so forth. In some implementations, the host 110 operates a retail website that hosts an electronic catalog with one or more items provided by various merchants 112(1), 112(2), . . . , 112(P) (collectively 112). [C4L36-56] The computer-readable memory 116 may further include a data collector 120 configured to collect or receive behavior data 122 about customers 102 and merchant data 123 about merchants 112. The behavior data 122 may be based on engagements between the customers 102 and the various merchants 112 that provide items for purchase, and it may be indicative of a customer's satisfaction with a particular merchant 112. Additionally, or alternatively, the merchant 112 may have an electronic marketplace (e.g., a website) that is accessible to the customer 102 via the network(s) 108. In this scenario, the customer 102 may search and/or browse items offered online by the merchant 112, save items to an electronic shopping cart, and so on. [C5L14-36] In general, the behavior data 122 may include, without limitation, purchase data (e.g., an amount of gratuity (or “tip”) provided by the customer 102, transaction amounts for items purchased by the customer 102, quantities of items ordered, types of items ordered, etc.), the amount of time spent (duration of the visit) at the merchant location or on the merchant's website or mobile shopping application, the time (e.g., time of day and/or date) of the customer engagement with the merchant 112, an amount of time the customer 102 waited to be served (e.g., seated at a table) by the merchant 112, a number of friends accompanying the customer 102 or otherwise invited by the customer 102 to the engagement, social signals (e.g., “likes,” hashtags, check-ins, etc.), audio data obtained via a microphone of the client device 104, an amount of time and/or number of instances the customer 102 used a mobile device 104 while visiting a merchant location, customer browsing behavior on a merchant's 112 website, click-through data from the merchant's 112 website, explicitly-provided customer ratings/scores of the merchants 112, explicit customer reviews of the merchants 112, customer activity subsequent to the engagement with the merchant 112 (e.g., visiting a different, but similar merchant 112), and so on. [C12L40-55] The UI 500 may further include a second settings control 506 that allows the customer 102 to specify levels of access to personal information that may be used by the system. For example, the customer 102 may specify whether the system can use his/her location information (e.g., based on global positioning system (GPS) data, social media check-ins, etc.), or whether the system can access the microphone of the customer's client device 104 to record audio data during a customer-merchant engagement, or the camera of the client device 104 to record images and/or video during a customer-merchant engagement, or whether the system can access online activities of the customer 102 during a customer-merchant engagement (e.g., click-through data, browsing behavior, search queries, etc.).See also [C5L52-C6L23])
generating, using a trained review generation engine and based on the obtained interaction data, a review that predicts content of a review of at least one of the one or more items generated by the user; ([C8L7-29] As noted above, the output of the customer satisfaction classifier 132 may be provided to the rating module 134 and/or a review generator 138. The rating module 134 may be configured to determine a rating for a particular merchant 112 based on the customer satisfaction score (e.g., class label) for the particular merchant 112. The rating module 134 may determine a merchant rating on any suitable scale or range, such as a 5-star rating scale. For example, a highest customer satisfaction score from the classifier 132 may correspond to a 5-star rating. In some embodiments, the rating module 134 can generate multiple ratings for a particular merchant 112, such as merchant ratings for different categories (e.g., service—both timeliness and quality, items quality, cleanliness of merchant location, etc.). The ratings determined by the rating module 134 are inferred from the customer satisfaction score, rather than provided by the customer 102 explicitly. The determined ratings may be stored as new merchant data 123 in a data store (e.g., a database) for the various merchants 112 that utilize the electronic marketplace of the host 110. In some embodiments, merchant ratings may be used to rank the merchants 112. [B8L29-55] The review generator 138 may be configured to generate an implicit (i.e., system-generated) customer review based on a merchant rating for a particular merchant 112 output by the rating module 134 and/or a customer satisfaction score for a particular merchant 112 output by the customer satisfaction classifier 132. Thus, one or more implicit customer reviews can be generated from the customer satisfaction score, the merchant rating, or a combination thereof. In some embodiments, the review generator 138 may utilize templates to generate implicit customer reviews. For example, one template may be used for a 5-star rating, while another template may be used for a 1-star rating. See also [C6L40-C8L6]))
Arora does not expressly teach updating the search engine using the generated review; and using the updated search engine to generate second search results for the user, wherein the second search results are different than the first search results.
Harris teaches:
updating the search engine using the generated review; and (Fig. 2; C4L64-C5L12] FIG. 2 is an illustrative flow diagram of an updated catalog search process according to various embodiments. At block 210, a supplier catalog database is maintained for an E-procurement system in which supplier systems provide information pertaining to the catalogs through which buyer systems can procure products and services. For example, when a user of a particular buyer system performs a search/query in the E-procurement system to find a particular item for purchase, the E-procurement system is limited to listing items from those catalogs which are approved and enabled/integrated with the particular buyer system. [C5L15-26] At block 215, as a particular buyer system performs searches of the set of catalogs associated/enabled/integrated with the particular buyer system for particular items, the E-procurement system tracks these searches and their parameters (e.g., query expressions) and their resulting matches with particular catalogs.[C5L48-56] In various embodiments, the queries and results of the queries are tracked for the particular buyer system and stored such as in a computer database or other data repository system. Other events pertaining to the buyer systems may also be tracked including the actual purchase of items and their connection, if any, to item searches and to particular catalogs maintained or external to the E-procurement system. [C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
using the updated search engine to generate second search results for the user, wherein the second search results are different than the first search results. (Fig. 2; C4L64-C5L12] FIG. 2 is an illustrative flow diagram of an updated catalog search process according to various embodiments. At block 210, a supplier catalog database is maintained for an E-procurement system in which supplier systems provide information pertaining to the catalogs through which buyer systems can procure products and services. For example, when a user of a particular buyer system performs a search/query in the E-procurement system to find a particular item for purchase, the E-procurement system is limited to listing items from those catalogs which are approved and enabled/integrated with the particular buyer system. [C5L15-26] At block 215, as a particular buyer system performs searches of the set of catalogs associated/enabled/integrated with the particular buyer system for particular items, the E-procurement system tracks these searches and their parameters (e.g., query expressions) and their resulting matches with particular catalogs.[C5L48-56] In various embodiments, the queries and results of the queries are tracked for the particular buyer system and stored such as in a computer database or other data repository system. Other events pertaining to the buyer systems may also be tracked including the actual purchase of items and their connection, if any, to item searches and to particular catalogs maintained or external to the E-procurement system. [C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include updating the search engine using the generated review; and using the updated search engine to generate second search results for the user, wherein the second search results are different than the first search results as taught by Harris with the implicit reviews of Arora in order to enhance the process of procurement for both buyer systems and supplier systems ([C1L34-39]).
Arora in view of Harris does not expressly teach executing a review generation objective function based on the predicted content of the review and adjusting, based on output of the executed review generation objective function, one or more parameters updating of the search engine.
Riley teaches:
executing a review generation objective function based on the predicted content of the review; ([C3L35-44] Trained model 108, given signals 109 for a particular document and search query, generates predicted ratings. Ideally, the predicted ratings will match the evaluator ratings that a human would typically assign to the document/search query pairing. The predicted ratings could be used in a number of applications, such as to rank documents that are to be returned from a search engine or to evaluate the results of search engines. [C7L58-C8L4] A regression (or ranking) analysis may next be performed on the generated signals and the corresponding human relevance ratings/rankings (act 403). In one implementation, linear regression using the least squares method of measuring error may be used in which the signals in signal set 715 are the independent (X) variables and the human relevance ratings are the dependent (Y) variable. Other regression analysis techniques could also be used, such as, without limitation, logistic regression, Poisson regression, or other supervised learning techniques. The result of the regression analysis may be a number of weights that define how future values of a signal set 715 are to be combined to generate a predicted relevance rating for the signal set. These weights thus define the trained human evaluation model 227.)
adjusting, based on output of the executed review generation objective function, one or more parameters of the search engine; ([C8L44-52] The operations described with the reference to the flow chart of FIG. 8 generally relate to refining or re-ranking results of a search engine. In alternate embodiments, the results of the search engine could be initially ranked based on the predicted relevance ratings. For example, the search engine may return a set of unordered documents that match the search query. A predicted relevance rating could be calculated for each of the documents and then used to rank the documents.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include executing a review generation objective function based on the predicted content of the review and adjusting, based on output of the executed review generation objective function, one or more parameters updating of the search engine as taught by Riley with the implicit reviews of Arora in view of Harris in order to improve the ability of a search engine to return relevant results ([C1L52-53]).
Arora in view of Harris in view of Riley teaches the limitations of claims 1 and 31. As per claims 2 and 32:
Arora in view of Riley does not expressly teach wherein using the updated search engine to generate second search results for the user comprises: obtaining item listings using a query analyzer engine in response to a user query; and ranking the obtained item listings using a ranking engine.
Harris further teaches:
wherein using the search engine to generate second search results for the user comprises: obtaining item listings using a query analyzer engine in response to a user query; and ranking the obtained item listings using a ranking engine. ([C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include obtaining item listings using a query analyzer engine in response to a user query; and ranking the obtained item listings using a ranking engine as taught by Harris with the implicit reviews of Arora in view of Riley in order to enhance the process of procurement for both buyer systems and supplier systems ([C1L34-39]).
Arora in view of Harris in view of Riley teaches the limitations of claims 2 and 32. As per claim 3 and 33:
Arora teaches adding implicit reviews to a pool of customer reviews. See Fig. 4; [C11L10-28] FIG. 4 illustrates an example screen rendering of a user interface (UI) 400 for presenting inferred merchant ratings and implicit customer reviews in a customer review forum. [C11L29-41] A list of customer reviews shows two explicit customer reviews 406(1) and 406(2), which are customer reviews provided by the respective customers themselves, as well as an implicit customer review 408, which was automatically generated by the review generator 138. See also [C11L42-C12L14])
Arora in view of Riley does not expressly teach using the reviews to rank item listings.
Harris teaches:
wherein the ranking engine uses the generated review to rank the obtained item listings ([C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include ranking of search results based on reviews as taught by Harris with the implicit reviews of Arora in view of Riley in order to enhance the process of procurement for both buyer systems and supplier systems ([C1L34-39]).
Arora in view of Harris in view of Riley teaches the limitations of claim 2. As per claim 4:
Arora teaches adding implicit reviews to a pool of customer reviews. See Fig. 4; [C11L10-28] FIG. 4 illustrates an example screen rendering of a user interface (UI) 400 for presenting inferred merchant ratings and implicit customer reviews in a customer review forum. [C11L29-41] A list of customer reviews shows two explicit customer reviews 406(1) and 406(2), which are customer reviews provided by the respective customers themselves, as well as an implicit customer review 408, which was automatically generated by the review generator 138. See also [C11L42-C12L14])
Arora in view of Riley does not expressly teach using the reviews to rank item listings.
Harris teaches:
wherein the query analyzer engine uses the generated review to obtain the item listings. ([C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include obtaining the listings based on reviews as taught by Harris with the implicit reviews of Arora in order to enhance the process of procurement for both buyer systems and supplier systems ([C1L34-39]).
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 6:
Arora further teaches:
wherein obtaining the interaction data comprises: obtaining a signal indicating a user selection on a graphical user interface (GUI) of the user device. ([C4L36-56] The computer-readable memory 116 may further include a data collector 120 configured to collect or receive behavior data 122 about customers 102 and merchant data 123 about merchants 112. The behavior data 122 may be based on engagements between the customers 102 and the various merchants 112 that provide items for purchase, and it may be indicative of a customer's satisfaction with a particular merchant 112. Additionally, or alternatively, the merchant 112 may have an electronic marketplace (e.g., a website) that is accessible to the customer 102 via the network(s) 108. In this scenario, the customer 102 may search and/or browse items offered online by the merchant 112, save items to an electronic shopping cart, and so on. [C5L14-36] In general, the behavior data 122 may include, without limitation, purchase data (e.g., an amount of gratuity (or “tip”) provided by the customer 102, transaction amounts for items purchased by the customer 102, quantities of items ordered, types of items ordered, etc.), the amount of time spent (duration of the visit) at the merchant location or on the merchant's website or mobile shopping application, the time (e.g., time of day and/or date) of the customer engagement with the merchant 112, an amount of time the customer 102 waited to be served (e.g., seated at a table) by the merchant 112, a number of friends accompanying the customer 102 or otherwise invited by the customer 102 to the engagement, social signals (e.g., “likes,” hashtags, check-ins, etc.), audio data obtained via a microphone of the client device 104, an amount of time and/or number of instances the customer 102 used a mobile device 104 while visiting a merchant location, customer browsing behavior on a merchant's 112 website, click-through data from the merchant's 112 website, explicitly-provided customer ratings/scores of the merchants 112, explicit customer reviews of the merchants 112, customer activity subsequent to the engagement with the merchant 112 (e.g., visiting a different, but similar merchant 112), and so on.)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 8:
Arora teaches:
wherein obtaining the interaction data comprises: obtaining at least one or more of the following: listing content, quality signals indicative of user interactions with the one or more items of the first search results, lister identification for the one or more items of the first search results, conversations between the user and lister for the one or more items of the first search results, shipping information, delivery information, search queries, search queries within a time range, or search queries within the same session. ([C4L51-56] In this scenario, the customer 102 may search and/or browse items offered online by the merchant 112, save items to an electronic shopping cart, and so on. Additionally, or alternatively, the merchant 112 may offer its items for acquisition via the electronic marketplace of the host 110. [C5L14-36] In general, the behavior data 122 may include, without limitation, purchase data (e.g., an amount of gratuity (or “tip”) provided by the customer 102, transaction amounts for items purchased by the customer 102, quantities of items ordered, types of items ordered, etc.), the amount of time spent (duration of the visit) at the merchant location or on the merchant's website or mobile shopping application, the time (e.g., time of day and/or date) of the customer engagement with the merchant 112, an amount of time the customer 102 waited to be served (e.g., seated at a table) by the merchant 112, a number of friends accompanying the customer 102 or otherwise invited by the customer 102 to the engagement, social signals (e.g., “likes,” hashtags, check-ins, etc.), audio data obtained via a microphone of the client device 104, an amount of time and/or number of instances the customer 102 used a mobile device 104 while visiting a merchant location, customer browsing behavior on a merchant's 112 website, click-through data from the merchant's 112 website, explicitly-provided customer ratings/scores of the merchants 112, explicit customer reviews of the merchants 112, customer activity subsequent to the engagement with the merchant 112 (e.g., visiting a different, but similar merchant 112), and so on. [C12L40-55] The UI 500 may further include a second settings control 506 that allows the customer 102 to specify levels of access to personal information that may be used by the system. For example, the customer 102 may specify whether the system can use his/her location information (e.g., based on global positioning system (GPS) data, social media check-ins, etc.), or whether the system can access the microphone of the customer's client device 104 to record audio data during a customer-merchant engagement, or the camera of the client device 104 to record images and/or video during a customer-merchant engagement, or whether the system can access online activities of the customer 102 during a customer-merchant engagement (e.g., click-through data, browsing behavior, search queries, etc.).)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 9:
Arora further teaches:
comprising: training the review generation engine using a supervised learning technique. ([C6L24-39] Furthermore, the machine learning model 128 may be trained using a supervised, unsupervised, or semi-supervised machine learning technique.)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 10:
Arora further teaches:
comprising: training the review generation engine using an unsupervised learning technique. ([C6L24-39] Furthermore, the machine learning model 128 may be trained using a supervised, unsupervised, or semi-supervised machine learning technique.)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 11:
Arora further teaches:
wherein training the review generation engine comprises: using a discriminator network to determine whether reviews generated by the review generation engine were generated by humans or not. (Fig. 4; element 314 (referred to as 414, appears to be a typo in the drawing); [C12L1-14] FIG. 4 shows that the UI 400 may further include a filtering mechanism 414 (e.g., a drop down menu) for a user to filter the customer reviews by the implicit customer reviews 408 or the explicit customer reviews 406, and/or any additional filtering criteria (e.g., most recent, most popular, etc.).
PNG
media_image1.png
444
742
media_image1.png
Greyscale
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 12:
Arora teaches:
wherein the generated review includes a written portion in a language of the user. (Fig. 2; Fig. 4, element 416;
PNG
media_image2.png
130
783
media_image2.png
Greyscale
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 13:
Arora teaches:
wherein the generated review includes a rating portion indicating a rating of the at least one of the one or more items. (Fig. 2; Fig. 4, element 416;
PNG
media_image2.png
130
783
media_image2.png
Greyscale
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 16:
Arora in view of Riley does not expressly teach comprising: providing the second search results to the user device.
Harris teaches:
comprising: providing the second search results to the user device. ([C6L14-50] At block 225, an expanded/enhanced search is performed for catalogs that match with the identified low match output rate query expressions for a particular buyer. In an embodiment, the search may query catalogs in the E-procurement system not enabled/associated/authorized with the particular buyer system. The updated search may match a portion or all of the identified low output query expressions with additional catalogs such as those enabled by other buyer systems. The updated search can also be based upon community intelligence and/or artificial intelligence including, for example, similar queries from other buyer systems that have resulted in purchases of the same or similar products, product reviews and purchase history for seller systems by other buyer systems, news articles about particular supplier systems, and other information. For example, the updated search could rank/recommend search results corresponding to better product reviews and/or more frequent product purchases over other search results.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include providing the second search results to the user device as taught by Harris with the implicit reviews of Arora in view of Riley in order to enhance the process of procurement for both buyer systems and supplier systems ([C1L34-39]).
Claim(s) 5 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al (US 11,030,535) in view of Harris et al (US 10,997,641) in view of Riley et al (US 8,195,654) in view of Baron et al (US 2022/0019627)
Arora in view of Harris in view of Riley teaches the limitations of claims 1 and 31. As per claims 5 and 34:
Arora in view of Harris in view of Riley does not expressly teach wherein generating the review occurs during a same session as the display of the first search results.
Baron teaches:
wherein generating the review occurs during a same session as the display of the first search results. ([0007] According to an aspect, a method for searching within user-generated reviews for multiple entities includes receiving a primary search query to search of corpus of entities, obtaining, in response to the primary search query, a search result including a list of entities that are responsive to the primary search query, receiving a secondary search query to search a plurality of user-generated reviews for the list of entities such that the secondary search query functions as a filter on the search result, identifying a set of user-generated reviews associated with the list of entities from the plurality of user-generated reviews, generating a filtered list of entities based on the set of user-generated reviews, where the filtered list of entities includes a first entity and a second entity and the second entity is different from the first entity, and providing the filtered list of entities for display on a user interface of a client device.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein generating the review occurs during a same session as the display of the first search results as taught by Baron with the implicit reviews of Arora in view of Harris in view of Riley in order to discover or generate results based on user-generated reviews (paragraph [0002]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al (US 11,030,535) in view of Harris et al (US 10,997,641) in view of Riley et al (US 8,195,654) in view of French et al (US 2013/0159923)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 5:
Arora in view of Harris in view of Riley does not expressly teach wherein obtaining the interaction data comprises: obtaining a signal indicating a mouse hovering over a portion of a graphical user interface (GUI) of the user device.
French teaches:
wherein obtaining the interaction data comprises: obtaining a signal indicating a mouse hovering over a portion of a graphical user interface (GUI) of the user device. ([0016] Various aspects of the technology described herein are generally directed to systems, methods, and computer-readable storage media for, among other things, previewing search results determined in response to search queries input, for instance, into a search box associated with a browser toolbar. Descriptors of search results determined to match or satisfy an input search query are presented in a search results window that overlays a first portion of the browser web page, and fetching or downloading of a web page associated with each of the presented search results begins. Upon receiving an indication (e.g., a hover-over or mouse-over event) that the user desires to preview one of the presented search results, a preview of a web page associated with the indicated search result is presented such that it overlays a second portion of the browser page.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein obtaining the interaction data comprises: obtaining a signal indicating a mouse hovering over a portion of a graphical user interface (GUI) of the user device as taught by French with the implicit reviews of Arora in view of Harris in view of Riley in order to indicate what the user considers as desired information ([0003]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al (US 11,030,535) in view of Harris et al (US 10,997,641) in view of Riley et al (US 8,195,654) in view of Haveliwala et al (US 2005/0216434)
Arora in view of Harris in view of Riley teaches the limitations of claim 1. As per claim 15:
Arora in view of Harris in view of Riley does not expressly teach receiving a first query and generating the first search results in response to receiving the first query; and receiving a second query and generating the second search results in response to receiving the second query, wherein the first query and the second query are the same.
Haveliwala teaches:
comprising: receiving a first query and generating the first search results in response to receiving the first query; and receiving a second query and generating the second search results in response to receiving the second query, wherein the first query and the second query are the same. (Fig. 6-8, 10; [0030] As shown on FIGS. 6-8, an Edit Profile link 616 allows the user to edit the interests in his user profile at any time. FIG. 9 illustrates where the user has returned to the topic directory page 200 as illustrated in FIG. 2, and deleted his existing topic interest in Computers, and created a different interest in "Music". The user then again selects the Start Searching button 208. The search engine provides an updated set of search results, which documents will be the same as before, since the query term has not changed. However, as of the last search, the user has positioned the control 610 for full personalization, and hence the search engine applies this setting and ranks the documents according the existing (and new) personalization profile, this time for "Music." FIG. 10 illustrates the dramatic difference this makes in the search results. Here, the top six results are all related to music and arts, and none of the previous results relating to computers make the top of the search result list. This shows how the user's personalization profile can significantly alter the ranking of a given set of documents.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comprising: receiving a first query and generating the first search results in response to receiving the first query; and receiving a second query and generating the second search results in response to receiving the second query, wherein the first query and the second query are the same as taught by Haveliwala with the implicit reviews of Arora in view of Harris in view of Riley in order to personalize search results in accordance with the interests of the users ([0003]).
Response to Arguments
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101 and 103.
Applicants arguments regarding rejections under 35 USC 103 are moot in light of new grounds of rejection which have been necessitated by amendment.
With regard to 101, the examiner respectfully disagrees. Any improvement is to the abstract idea itself and not underlying technology. The claims provide no meaningful details regarding the objective function, and even if they did this would simply part of the abstract idea. The objective function and the data used in to provide the calculation are merely part of the abstract idea. Further, applicant has not even claimed machine learning with regard to the independent claims and even if it was claimed, this is in no way an improvement to machine learning itself. As a result, such rejections have been maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
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, Waseem Ashraff can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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.
CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621
/CHRISTOPHER STROUD/ Primary Examiner, Art Unit 3621
Prosecution Insights