DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 24 January 2025. Currently, claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (system and method). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 and 18 recite the abstract idea of a system for generating a marketplace content brief that receives product data associated with a product and identifies at least one brand characteristic of a brand associated with the product, based on the product data and identifies at least one attribute of the product based on the product data and generates an attribute insight output based on at least the at least one attribute of the product and receives demographic data associated with the product and generates a demographic insight output based on at least the demographic data associated with the product and receives a suggested strategy output, wherein the suggested strategy output is generated based on, at least, the attribute insight output and the demographic insight output and generates a content brief output based on the suggested strategy output. The claims are directed to a type of generating content based on an analyzed product and brand data including demographic insights. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain method of organizing human activity including commercial interactions including business relations, marketing and sales. Applicant’s claims are organizing human activity because the claims show analysis of demographic data associated with a product which is human activity related to sales and marketing and that data is organized by the generation of content. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to perform steps and provides, at a display, an actionable output that includes at least the content brief output) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to perform steps and provides, at a display, an actionable output that includes at least the content brief output (as evidenced by para [0054]-[0073] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-10, 12-13 also do not include additional elements that integrate the judicial exception into a practical application because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the at least one brand characteristic includes at least one a top competitive brand characteristic, a brand color characteristic, a brand logo characteristic, a brand statistics characteristic, a brand voice characteristic, and a brand tone characteristic and identify key product attributes of the product; map the key product attributes to one or more products associated with the product; and map the key product attributes to one or more search terms and generate the attribute insight output further based on the key product attributes, the map of key product attributes to the one or more products associated with the product, and the map of the key product attributes to the one or more search terms and generate at least one persona associated with the product; and receive purchasing data indicating products frequently purchased with the product and generate the demographic insight output further based on the at least one personal associated with the product and the purchasing data indicating products frequently purchased with the product and determine at least one consumer search behavior characteristic based on the product data and determine the at least one consumer search behavior characteristic by: generating at least one search family; adding marketplace data to the product data; and generating a consumer search behavior insight output based on the at least one consumer search behavior characteristic and wherein the suggested strategy output is generated further based on the consumer search behavior insight output and identify at least one winning archetype associated with the product and generate a winning archetype insight output based on the at least one winning archetype associated with the product. Dependent claims 11, 14-17, 19-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as identify the at least one winning archetype associated with the product by: receiving images associated with the product; receiving an image stack associated with the product; preprocessing the images associated with the product; and identifying image archetypes based on the preprocessed images associated with the product and wherein the suggested strategy output is received from a large language model and wherein the large language model is configured to: receive, at least, the attribute insight output and the demographic insight output; receive at least one image prompt; and generate the suggested strategy output based on, at least, the at least one image prompt, the attribute insight output, and the demographic insight output and based on the suggested strategy output: generate written content associate with the product; generate visual content associated with the product; and receive user-directed regeneration input and based on the written content associated with the product, the visual content associated with the product, and the user-directed regeneration input (as evidenced by para [0054]-[0073] of applicant’s own specification) are well understood, routine and conventional in the field.
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.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-13, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Selinger et al. (US 2010/0250336 A1) (hereinafter Selinger) in view of Short et al. (US 20120197653 A1) (hereinafter Short).
Claims 1 and 18:
Selinger, as shown, discloses the following limitations of claims 1 and 18:
A system (and corresponding method) for generating a marketplace content brief (see para [0016], " In this illustrated example, each of the users 140 may interact with an embodiment of the MSPR service 105 to obtain recommendations for products available from a retailer 130, such as to obtain dynamically generated product recommendations for the user in particular situations. In this illustrated example, the MSPR service 105 includes or otherwise has access to various product information 120 and user interaction information 115. The product information 120 may, for example, include a database and/or other data collection related to a catalog of products and/or services available from one or more retailers 130, such as may include descriptions, prices, availability and other information about the products and/or services. The user interaction information 115 may, for example, include information regarding how numerous of the users 140 and/or other users have previously interacted in various ways with products included in product information 120. As part of recommending products for a particular one of the users 140, the illustrated MSPR service 105 may process the product information 120 using various recommendation strategies to identify or otherwise determine which products of the numerous products available from the retailer 130 to recommend to the particular user, such as by identifying the products that are determined to be most relevant to the user based on the various recommendation strategies and the currently available information. In some embodiments, at least some of the recommendation strategies may use the user interaction information 115 to determine which products to recommend to a particular user, such as to identify products that are popular, to identify products that are similar to or otherwise related to one or more other products currently selected by the user or otherwise determined to be of current interest to the user, and/or to otherwise likely to be of interest to a particular user based at least in part on prior interactions of multiple users. As previously noted, in some embodiments, the MSPR service 105 may generate product recommendations for users based at least in part on aggregating results of multiple recommendation strategies, such as discussed in more detail elsewhere."), the system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor (see para [0049]-[0051], showing equivalent computing functionality and components) to: receive product data associated with a product (see para [0016], " In this illustrated example, each of the users 140 may interact with an embodiment of the MSPR service 105 to obtain recommendations for products available from a retailer 130, such as to obtain dynamically generated product recommendations for the user in particular situations. In this illustrated example, the MSPR service 105 includes or otherwise has access to various product information 120 and user interaction information 115. The product information 120 may, for example, include a database and/or other data collection related to a catalog of products and/or services available from one or more retailers 130, such as may include descriptions, prices, availability and other information about the products and/or services. The user interaction information 115 may, for example, include information regarding how numerous of the users 140 and/or other users have previously interacted in various ways with products included in product information 120. As part of recommending products for a particular one of the users 140, the illustrated MSPR service 105 may process the product information 120 using various recommendation strategies to identify or otherwise determine which products of the numerous products available from the retailer 130 to recommend to the particular user, such as by identifying the products that are determined to be most relevant to the user based on the various recommendation strategies and the currently available information. In some embodiments, at least some of the recommendation strategies may use the user interaction information 115 to determine which products to recommend to a particular user, such as to identify products that are popular, to identify products that are similar to or otherwise related to one or more other products currently selected by the user or otherwise determined to be of current interest to the user, and/or to otherwise likely to be of interest to a particular user based at least in part on prior interactions of multiple users. As previously noted, in some embodiments, the MSPR service 105 may generate product recommendations for users based at least in part on aggregating results of multiple recommendation strategies, such as discussed in more detail elsewhere.");
identify at least one brand characteristic of a brand associated with the product, based on the product data (see para [0011], showing identifying top selling item from a particular category or retailer and identifying items associated with a particular advertiser, retailer or manufacturer where it is obvious to one of ordinary skill in the art that a particular category or retailer can be considered a brand given broadest reasonable interpretation and because retailers often are their own brand such as GAP);
identify at least one attribute of the product based on the product data (see para [0011], "] As noted above, the recommendation strategies that may be used may have various forms in various embodiments, and in some embodiments may be based at least in part on data regarding prior interactions of numerous users with numerous items. For example, in at least some embodiments, the prior interactions of users with items may involve interactions of customers of one or more retailers related to products or other items that are available from those retailers, or instead may involve interactions of other types of users in other situations (e.g., users who perform searches with search engines, users who view information about products from a product review service, etc.). A non-exclusive list of types of interactions of customers of online or other retailers with items for which interaction data is gathered may include, for example, the following: performing searches (e.g., for particular items, for items of a particular category or other defined group of items, for items having one or more indicated attributes, etc.); browsing item categories; viewing detailed information about particular items; purchasing items; doing item returns; etc. The interaction data about the prior user interactions with items may then be analyzed and summarized in various ways, such as, for example, in the following non-exclusive manners: to identify items that are popular (e.g., the top item sellers in a particular category or from a particular retailer during a particular period of time; the items that are most often selected by users, such as to view detailed information about the items; the items with the highest user ratings; the items most often included in results of users' searches and/or selected by users from such search results; the "hottest" items of an item group to reflect those items having the largest changes in their ratings or sales or other popularity measure during a particular period of time; etc.); to identify items that are similar to each other or otherwise related to each other (e.g., items that have similar or otherwise related items attributes, such as price, type, size, etc.; users who viewed this item are most likely to also view these other items; users who viewed this item are most likely to purchase these items; users who purchased this item are most likely to also purchase these other items; users who searched for this item attribute and/or browsed this item category are most likely to view and/or purchase these items or items with these attributes or items in these categories; etc.); to identify items that are popular among users similar to a user for whom recommendations are being made (e.g., users with similar demographics; users in the same or nearby geographic regions, etc.); to identify items that have been explicitly associated with one another, such as by a retailer, an advertiser, a manufacturer, and/or another user (e.g., "buy together" items); to identify items that are similar or otherwise related to items interacted with by a particular user, such as a user to whom recommendations are to be provided (e.g., interactions related to items purchased by the user, items viewed by the user, items added to a shopping cart of the user, etc.); etc. Some or all of the various types of analyzed or summarized user interaction data may then each be used as a distinct recommendation strategy, such as to use information about top item sellers in a particular category as one recommendation strategy when a user interest in that category is indicated or suspected, to use information about users who viewed a particular item as being most likely to purchase other identified items as one recommendation strategy when a user interest in that particular item is indicated or suspected, etc. Additional details related to particular recommendation strategies are included below.");
generate an attribute insight output based on at least the at least one attribute of the product (see para [0011], "The interaction data about the prior user interactions with items may then be analyzed and summarized in various ways, such as, for example, in the following non-exclusive manners: to identify items that are popular (e.g., the top item sellers in a particular category or from a particular retailer during a particular period of time; the items that are most often selected by users, such as to view detailed information about the items; the items with the highest user ratings; the items most often included in results of users' searches and/or selected by users from such search results; the "hottest" items of an item group to reflect those items having the largest changes in their ratings or sales or other popularity measure during a particular period of time; etc.); to identify items that are similar to each other or otherwise related to each other (e.g., items that have similar or otherwise related items attributes, such as price, type, size, etc.; users who viewed this item are most likely to also view these other items; users who viewed this item are most likely to purchase these items");
receive demographic data associated with the product (see para [0011], "to identify items that are popular among users similar to a user for whom recommendations are being made (e.g., users with similar demographics; users in the same or nearby geographic regions, etc.); ");
generate a demographic insight output based on at least the demographic data associated with the product (see para [0011], "to identify items that are popular among users similar to a user for whom recommendations are being made (e.g., users with similar demographics; users in the same or nearby geographic regions, etc.); to identify items that have been explicitly associated with one another, such as by a retailer, an advertiser, a manufacturer, and/or another user (e.g., "buy together" items); to identify items that are similar or otherwise related to items interacted with by a particular user, such as a user to whom recommendations are to be provided (e.g., interactions related to items purchased by the user, items viewed by the user, items added to a shopping cart of the user, etc.); etc. Some or all of the various types of analyzed or summarized user interaction data may then each be used as a distinct recommendation strategy, such as to use information about top item sellers in a particular category as one recommendation strategy when a user interest in that category is indicated or suspected, to use information about users who viewed a particular item as being most likely to purchase other identified items as one recommendation strategy when a user interest in that particular item is indicated or suspected, etc. Additional details related to particular recommendation strategies are included below.");
receive a suggested strategy output, wherein the suggested strategy output is generated based on, at least, the attribute insight output and the demographic insight output (see para [0011]-[0013], showing recommendations based on the analyzed data);
generate a content brief output based on the suggested strategy output (see para [0013], " Furthermore, as previously noted, information about current selections of a particular user may be gathered in at least some embodiments based at least in part on providing a GUI for display to the user that includes selectable information about numerous recommended items. For example, a Web page may be generated and displayed to a user that includes images of and/or other indications of numerous items, such as for an initial group of recommended items, and with the images or other displayed indications of the items being selectable by the user. If the user makes a current selection of one of the recommended items, a second group of recommended items may be dynamically generated for the user based at least in part on the current selection, and then displayed to the user via the GUI, such as for a second group that includes some or all of the items of the first group (and optionally additional items), or instead a second group that does not include any of the items of the first group. Similarly, if the user makes a second current selection of one of the recommended items of the second group, a third group of recommended items may similarly be dynamically generated for the user based at least in part on the current selection(s), and then displayed to the user via the GUI. Such user selections may continue to be repeatedly monitored and used in this manner as part of an ongoing interactive recommendation exploration session, and in some embodiments other types of user selections may similarly be used (e.g., a user selection to remove one or more currently selected items from the group of current selection, also referred to as a "de-selection"; other user feedback of a positive or negative nature regarding one or more currently selected items or other items; etc.). Non-limiting examples of portions of one such GUI are illustrated with respect to FIGS. 2A-2D. In addition, as previously noted, the recommendation results from one or more recommendation strategies may be weighted or otherwise ranked in some embodiments, and if so the recommended items that are displayed to a user via the GUI may be displayed so that items with higher rankings are displayed more prominently than other items with lower rankings (e.g., by displaying the items in decreasing order of their associated rankings, such that the items with the highest rankings are displayed first; by using different fonts, colors, placement or other visual indications to indicate items with higher rankings; etc.). Thus, for example, if the display of a second group of recommended items replaces a prior display of a first group of recommended items, and the second group includes at least some of the items of the first group, the weighting of those items from the first group may change in the second group, and thus the prominence of the display of those items from the first group may similarly change in the display of those items as part of the second group. Furthermore, in at least some embodiments, the display of a second group of recommended items that replaces a prior display of a first group of recommended items may be performed in various manners, including to modify a previously displayed Web page without reloading the Web page in a client browser application (e.g., via the use of AJAX, or Asynchronous JavaScript and XML, or other scripting or instructions or execution mechanisms included as part of the previously displayed Web page). Additional details related to displaying GUIs and recommended items to users are included below."); and
provide, at a display, an actionable output that includes at least the content brief output (see para [0013], " Furthermore, as previously noted, information about current selections of a particular user may be gathered in at least some embodiments based at least in part on providing a GUI for display to the user that includes selectable information about numerous recommended items. For example, a Web page may be generated and displayed to a user that includes images of and/or other indications of numerous items, such as for an initial group of recommended items, and with the images or other displayed indications of the items being selectable by the user. If the user makes a current selection of one of the recommended items, a second group of recommended items may be dynamically generated for the user based at least in part on the current selection, and then displayed to the user via the GUI, such as for a second group that includes some or all of the items of the first group (and optionally additional items), or instead a second group that does not include any of the items of the first group. Similarly, if the user makes a second current selection of one of the recommended items of the second group, a third group of recommended items may similarly be dynamically generated for the user based at least in part on the current selection(s), and then displayed to the user via the GUI. Such user selections may continue to be repeatedly monitored and used in this manner as part of an ongoing interactive recommendation exploration session, and in some embodiments other types of user selections may similarly be used (e.g., a user selection to remove one or more currently selected items from the group of current selection, also referred to as a "de-selection"; other user feedback of a positive or negative nature regarding one or more currently selected items or other items; etc.). Non-limiting examples of portions of one such GUI are illustrated with respect to FIGS. 2A-2D. In addition, as previously noted, the recommendation results from one or more recommendation strategies may be weighted or otherwise ranked in some embodiments, and if so the recommended items that are displayed to a user via the GUI may be displayed so that items with higher rankings are displayed more prominently than other items with lower rankings (e.g., by displaying the items in decreasing order of their associated rankings, such that the items with the highest rankings are displayed first; by using different fonts, colors, placement or other visual indications to indicate items with higher rankings; etc.). Thus, for example, if the display of a second group of recommended items replaces a prior display of a first group of recommended items, and the second group includes at least some of the items of the first group, the weighting of those items from the first group may change in the second group, and thus the prominence of the display of those items from the first group may similarly change in the display of those items as part of the second group. Furthermore, in at least some embodiments, the display of a second group of recommended items that replaces a prior display of a first group of recommended items may be performed in various manners, including to modify a previously displayed Web page without reloading the Web page in a client browser application (e.g., via the use of AJAX, or Asynchronous JavaScript and XML, or other scripting or instructions or execution mechanisms included as part of the previously displayed Web page). Additional details related to displaying GUIs and recommended items to users are included below.")
Although Selinger shows identify at least one brand characteristic of a brand associated with the product, based on the product data is obvious, it is not explicit. In analogous art, Short discloses the following limitations:
identify at least one brand characteristic of a brand associated with the product, based on the product data (see para [0028]-[0029], "Although product database 160 and brand database 170 are illustrated as distinct databases, they could be combined into a single database system. In such an embodiment, each type of object can be distinguished from each other by a namespace attribute identifying the object as a product, a brand, a good, a service, or other type of item. In some scenarios, a product and brand are associated with the same object. For example, a style of shoe can be bound with a brand (e.g., Converse.RTM. Chuck Taylor hi tops). As discussed above, a single object can include attributes conforming to universal namespace 145 rules where the corresponding attributes identifies the product as well as the brand bound to the product. Thus, the system can track how the product's attributes or metrics can be compared with a brand metrics. Through such comparisons, especially across many product and brand pairings, recommendation engine 150 can discover if there are relationships among the products and brands. If recommendation engine 150 derives a relationship, then recommendation engine 150 can conduct an analysis on how a target product, or a target brand, can be aligned with other objects regardless of type. Discovered or derived relationships can be quantified as one or more relationship metrics as discussed with respect to FIG. 3 below.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Short with Selinger because including brand identification enables a better understanding for the products in making recommendations (see Short, [0004]-[0008]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the brand identification system as taught by Short in the method for generating product recommendations of Selinger, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 2:
Selinger does not explicitly disclose wherein the at least one brand characteristic includes at least one a top competitive brand characteristic, a brand color characteristic, a brand logo characteristic, a brand statistics characteristic, a brand voice characteristic, and a brand tone characteristic
wherein the at least one brand characteristic includes at least one a top competitive brand characteristic, a brand color characteristic, a brand logo characteristic, a brand statistics characteristic, a brand voice characteristic, and a brand tone characteristic (see para [0040], "In a preferred embodiment the correlation engine uses namespace 245 to convert product or brand properties into quantified metrics as illustrated. As an example considered a scenario where a brand object corresponds to a logo. A normalization engine creates a logo attribute and analyzes the logo for relevant metrics. In the example show, the analysis indicates that a logo comprises 49% red and 12% blue, possibly detected through an image recognition algorithm (e.g., SIFT, color histograms, etc.). Such values can be measured empirically from on-line photographs, audio recordings, text data, or could even be entered directly from a brand owner. Similarly, product objects can also have quantized metrics.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the brand identification system as taught by Short in the method for generating product recommendations of Selinger, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 3:
Further, Selinger discloses the following limitations:
identify key product attributes of the product (see para [0011], "The interaction data about the prior user interactions with items may then be analyzed and summarized in various ways, such as, for example, in the following non-exclusive manners: to identify items that are popular (e.g., the top item sellers in a particular category or from a particular retailer during a particular period of time; the items that are most often selected by users, such as to view detailed information about the items; the items with the highest user ratings; the items most often included in results of users' searches and/or selected by users from such search results; the "hottest" items of an item group to reflect those items having the largest changes in their ratings or sales or other popularity measure during a particular period of time; etc.); to identify items that are similar to each other or otherwise related to each other (e.g., items that have similar or otherwise related items attributes, such as price, type, size, etc.; users who viewed this item are most likely to also view these other items; users who viewed this item are most likely to purchase these items; users who purchased this item are most likely to also purchase these other items; users who searched for this item attribute and/or browsed this item category are most likely to view and/or purchase these items or items with these attributes or items in these categories; etc.);" where price, size etc. can be considered key attributes);
map the key product attributes to one or more products associated with the product (see para [0011], "users who searched for this item attribute and/or browsed this item category are most likely to view and/or purchase these items or items with these attributes or items in these categories; etc.); to identify items that are popular among users similar to a user for whom recommendations are being made (e.g., users with similar demographics; users in the same or nearby geographic regions, etc.); to identify items that have been explicitly associated with one another, such as by a retailer, an advertiser, a manufacturer, and/or another user (e.g., "buy together" items); to identify items that are similar or otherwise related to items interacted with by a particular user, such as a user to whom recommendations are to be provided (e.g., interactions related to items purchased by the user, items viewed by the user, items added to a shopping cart of the user, etc.); etc."); and
map the key product attributes to one or more search terms (see para [0011], "users who searched for this item attribute and/or browsed this item category are most likely to view and/or purchase these items or items with these attributes or items in these categories; etc." showing a correlation or mapping with attributes and searches)
Claim 4:
Further, Selinger discloses the following limitations:
generate the attribute insight output further based on the key product attributes, the map of key product attributes to the one or more products associated with the product (see para [0011], showing recommendations and strategies based on key attributes such as price and size which are connected to products and items specifically and also associated with another or similar categories, etc. and see para [0013]), and
the map of the key product attributes to the one or more search terms (see para [0011], "users who searched for this item attribute and/or browsed this item category are most likely to view and/or purchase these items or items with these attributes or items in these categories; etc." showing a correlation or mapping with attributes and searches)
Claims 5-6:
Further, Selinger discloses the following limitations:
receive purchasing data indicating products frequently purchased with the product (see para [0030], "For example, the initial group of product recommendations may be generated by the MSPR service using various recommendation strategies to identify products that may be of potential interest to the user, some of which may be based on previous interactions of multiple users with various of the products available from the retailer. Such recommendation strategies may include identifying products that are determined to be popular based on the interactions of multiple customers of the retailer (or other users), such as to identify products that are frequently purchased by (e.g., top selling items), frequently viewed by, frequently searched for by, highly rated by, etc., the multiple customers.")
Selinger, however, does not specifically disclose generate at least one persona associated with the product. In analogous art, Short discloses the following limitations:
generate at least one persona associated with the product (see para [0046], "One should also appreciate that a relationship can be a positive, neutral, or negative relationship. When relationship metrics 340 indicate a strong, positive relationship, a recommendation can be generated in the form of an alignment of the target product object with a brand corresponding to a brand object; a golf game can be associated with Tiger Woods for example. The reverse can also be true. A recommendation can also include aligning a brand with a product; Nike can be aligned with an extreme sports video game for example.")
generate the demographic insight output further based on the at least one personal associated with the product and the purchasing data indicating products frequently purchased with the product (see para [0045]-[0046], "In chart 300 each data point represents a product-brand pairing where the type of data point indicates an amount of revenue generated for the product. Revenue can be considered a third dimension to the data point beyond amount of red in a logo and Fluoride content. Although revenue is used in this example, an additional dimension of the plot beyond revenue could be any other metric as well (e.g., product metric, brand metric, etc.). For example, each data point could represent buzz, market penetration, review scores, demographic, or other metrics. One should keep in mind that brand objects and product objects can be considered N-tuples of information where any member of the N-tuple can be plotted or analyzed against one or more other members. One should also appreciate that a relationship can be a positive, neutral, or negative relationship. When relationship metrics 340 indicate a strong, positive relationship, a recommendation can be generated in the form of an alignment of the target product object with a brand corresponding to a brand object; a golf game can be associated with Tiger Woods for example. The reverse can also be true. A recommendation can also include aligning a brand with a product; Nike can be aligned with an extreme sports video game for example.” where revenue can be considered purchasing data)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the brand identification system as taught by Short in the method for generating product recommendations of Selinger, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7:
Further, Selinger discloses the following limitations:
determine at least one consumer search behavior characteristic based on the product data (see para [0030], "For example, the initial group of product recommendations may be generated by the MSPR service using various recommendation strategies to identify products that may be of potential interest to the user, some of which may be based on previous interactions of multiple users with various of the products available from the retailer. Such recommendation strategies may include identifying products that are determined to be popular based on the interactions of multiple customers of the retailer (or other users), such as to identify products that are frequently purchased by (e.g., top selling items), frequently viewed by, frequently searched for by, highly rated by, etc., the multiple customers. In some embodiments, recommendation strategies that identify popular items may be constrained to one or more time periods, such as to identify items that are recently popular, historically popular, popular at various times of the year, etc. As part of identifying products using a recommendation strategy, the MSPR service may also assign a relevance score to each of the identified items for use in various ways, such as for determining which items to provide for display to the user (e.g., the MSPR service may provide items with the highest relevance scores for display to the user), for displaying items to the user in various manners according to the relevance scores, for aggregating results of multiple recommendation strategies, etc. For example, with respect to recommendation strategies that identify popular items, the corresponding relevance score of each recommended item may be based at least in part on a level of popularity the items are determined to have (e.g., for top selling items, the relevance score may be based on a number of units of each item that have been recently sold to customers, such that items with the highest numbers of units sold have the highest relevance scores; for top viewed items, the relevance score may be based on a number of distinct users who viewed information related to the item; etc.). In other embodiments, the relevance scores may also or instead be based at least in part on a determined conditional probability that the recommended product will likely be purchased by the user if displayed in the current context of display 200, etc. The corresponding relevance scores produced by each distinct recommendation strategy may be normalized for comparison to and/or combination with relevance scores produced by other distinct recommendation strategies.")
Claims 8-9:
Further, Selinger discloses the following limitations:
determine the at least one consumer search behavior characteristic by: generating at least one search family (see para [0022], "In some embodiments, the MSPR service 105 may be included as part of a Web site of a particular retailer 130, such as to provide the described functionality for dynamically recommending products of the retailer 130 to users of the Web site. For example, in some such embodiments, the GUI of the MSPR service 105 may be provided as one or more Web pages of the Web site, or as an embedded Web application included in one or more of various Web pages of the Web site. In some such embodiments, the users 140 may initiate access with the MSPR service 105 via the Web site of the retailer 130 in various ways to obtain an initial generated group of selectable product recommendations. For example, in some embodiments, the user may select one or more products available on the Web site (e.g., from a product page, a product listing, a search results list, etc.), with an initial generated group of product recommendations being related to the one or more selected products; the user may indicate a desire to browse a group of recommended products of a particular type and/or category, with the initial generated group of product recommendations being products of that particular type or category; the user may enter one or more search terms for recommended products, with the initial group of recommended products being products that match one or more of the search terms; etc." where the group can be considered a family);
adding marketplace data to the product data (see para [0030], where MSPR assigning a relevance score based on search and product data can be considered marketplace data for the product data); and
generating a consumer search behavior insight output based on the at least one consumer search behavior characteristic (see para [0030], showing recommendation strategies based on the relevance score based on search behavior)
wherein the suggested strategy output is generated further based on the consumer search behavior insight output (see para [0030], showing recommendation strategies are displayed)
Claim 10:
Further, Selinger discloses the following limitations:
identify at least one winning archetype associated with the product (see para [0012], "a particular one of multiple available recommendation strategies may instead be selected for use in a particular situation, such as based on a dynamic determination that the particular recommendation strategy is optimal or otherwise preferred for the particular situation, or instead based on a prior selection or configuration to use that particular recommendation strategy in that particular situation (e.g., based on prior configuration by a human operator, based on a prior automated selection of that particular recommendation strategy, etc.).)
Claims 11-13:
Further, Selinger discloses the following limitations:
identify the at least one winning archetype associated with the product by: receiving images associated with the product (see para [0013], " For example, a Web page may be generated and displayed to a user that includes images of and/or other indications of numerous items, such as for an initial group of recommended items, and with the images or other displayed indications of the items being selectable by the user.");
receiving an image stack associated with the product (see para [0026]-[0027], "In the example of FIG. 2A, an example interactive display 200 is illustrated that includes user-selectable information related to multiple recommended products or other items available from an example retailer. The display 200 includes a selected item area 205, which in this example initially displays a user instruction 207 to the user. The display 200 also includes multiple item images displayed in a recommended item area 270 (shown surrounded in bold for reference purposes), with each of the item images corresponding to a distinct product or other item recommended for the user by an embodiment of the MSPR service (not shown). In this example, each of the displayed item images in the recommended item area 270 is selectable by the user, such that the user may select a displayed image of a particular recommended item to indicate an interest in other items that are similar to or otherwise related in various ways to the selected recommended item. For example, a user may select one of the item images displayed in the recommended item area 270 by "clicking on" the item image (e.g., using a mouse or other pointing device), or by otherwise interacting with the display 200 in such a manner to indicate a selection of the item image (e.g., using a moveable highlight, voice commands, a touch screen, etc.). In the example display 200, the multiple item images are presented in a grid-like manner, with each item image being displayed in a particular grid location of the recommended item area 270, such as by being part of one of multiple illustrated rows 272a-272d and one of multiple illustrated columns 270a-270d, although the row and column indications are not displayed to the user in this example. To facilitate discussion of particular grid locations corresponding to a particular row and a particular column position, such grid locations may be abbreviated in [row]:[column] form in the following discussion--for example, the grid location at row 272a and column 270a (corresponding in this example to an image of item 1) may be abbreviated using such a form as 272a:270a. While the recommended item area 270 is illustrated as displaying a four-by-four grid of item images in this example, it will be appreciated that other types of displays are possible in other embodiments. For example, in some embodiments, more or fewer rows or columns may be included in the grid, with at least one embodiment having five rows and nine columns; while in other embodiments, item images may be presented in other manners not limited to grids (e.g., a list). In addition, in other embodiments, other information corresponding to the recommended items may be provided in the recommended item area 270, such as in addition to or instead of item images, including other visual representations of items (e.g., icons), item names, item descriptions, item prices, etc. Furthermore, in some embodiments, the various item images, icons, and/or descriptions may be illustrated in a non-uniform manner, such as with at least some more relevant items having higher associated relevance scores being displayed in a larger form than at least some less relevant items, such as to increase the prominence of the more relevant items.");
Selinger does not specifically disclose preprocessing the images associated with the product. In analogous art, Short discloses the following limitations:
preprocessing the images associated with the product (see para [0040], "In a preferred embodiment the correlation engine uses namespace 245 to convert product or brand properties into quantified metrics as illustrated. As an example considered a scenario where a brand object corresponds to a logo. A normalization engine creates a logo attribute and analyzes the logo for relevant metrics. In the example show, the analysis indicates that a logo comprises 49% red and 12% blue, possibly detected through an image recognition algorithm (e.g., SIFT, color histograms, etc.). Such values can be measured empirically from on-line photographs, audio recordings, text data, or could even be entered directly from a brand owner. Similarly, product objects can also have quantized metrics."); and
identifying image archetypes based on the preprocessed images associated with the product (see para [0019], "External metrics such as consumer awareness, sales performance, or demographic information can be integrated and aligned with both the brand and the product. After processing this data to achieve optimal relationships between the data sets it is then possible to isolate out specific brands and products as they relate to each other to create optimal bi-directional inferences to assist in the marketing and business decisions of brand or product owners." and see para [0049], "Although optimal alignment 310 and non-optimal alignment 320 are presented as regions within a graph, one should appreciate that an alignment is considered a manageable object within the correlation ecosystem. In some embodiments, alignments can include properties representative of relationship metrics 340, possibly in the form instructions that can be used to configure a display, or other output device, to present the alignments. For example, a product provider using the correlation engine might receive a visual report presented within a browser where the report includes possible graphical representations of relationship metrics 340 or other alignment properties. Contemplated alignment properties can include quantified values representing indications that a target product should be associated with a brand object, indications of the strength of alignment (e.g., optimal, non-optimal, on a scale, etc.), indications alignment direction (e.g., positive, negative, neutral, value, etc.), or indications of quality of alignment (e.g., confidence level, etc.).")
generate a winning archetype insight output based on the at least one winning archetype associated with the product (see para [0052], "The recommendation engine can also be responsive to data entering or leaving the databases. As product or brand metrics change, the recommendation engine can update its recommendations or relationship metrics according, possibly in real-time. In such embodiments, the recommendation engine can also present recommendations or relationships as a function of time, which can be used as a leading indicator of a developing relationship. If a leading indictor satisfies triggering criteria, corrective actions, if desired, can be taken to enhance or hinder the growing relationship.")
wherein the suggested strategy output is generated further based on the winning archetype insight output (see para [0052], "The recommendation engine can also be responsive to data entering or leaving the databases. As product or brand metrics change, the recommendation engine can update its recommendations or relationship metrics according, possibly in real-time. In such embodiments, the recommendation engine can also present recommendations or relationships as a function of time, which can be used as a leading indicator of a developing relationship. If a leading indictor satisfies triggering criteria, corrective actions, if desired, can be taken to enhance or hinder the growing relationship.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the brand identification system as taught by Short in the method for generating product recommendations of Selinger, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 16-17:
Further, Selinger discloses the following limitations:
based on the suggested strategy output: generate written content associate with the product (see para [0041], "In other embodiments, various other functionality may be provided by an embodiment of the MSPR service, such as for display in a GUI of the MSPR service. For example, in some embodiments, a user may interact with various information corresponding to a displayed recommended product to provide feedback with respect to that product, such as to provide positive and/or negative ratings with respect to the displayed recommended product (e.g., a numerical rating, a star rating, a thumbs-up/down rating, etc.), with such feedback being used in various ways in various embodiments, such as to reduce (if a negative rating) and/or increase (if a positive rating) the relevance scores for products that are determined to be similar to or otherwise related to the rated displayed recommended product. In addition, in some embodiments, the users may interact with the displayed images in various other ways to obtain and/or view information related to recommended products, such as by "hovering" over a displayed image of a recommended product (e.g., with a mouse pointer) to cause information related to a recommended product associated with the displayed image to be provided to the user (e.g., price, description, sales information, availability, etc.). In still other embodiments, functionality may be provided that allows the user to interact with the GUI to initiate a purchase of a displayed recommended item. In addition, in some embodiments, information may be displayed or otherwise provided to users to indicate why a particular item is being recommended (e.g., this particular item is highly relevant for being viewed by users who also viewed currently selected item 109, and is moderately relevant for being a top seller for an initially selected category of items).");
generate visual content associated with the product (see para [0032], " At some time after the multiple item images corresponding to the first group of recommended products is displayed to the user in the recommended item area 270 of FIG. 2A, the user interacts with the display 200 to select item 6 image 253 at grid position 272b:270b, such as to indicate an interest in item 6 and/or in items related to item 6, with the results of the selection being illustrated in FIG. 2B. In particular, in the modified display 200 of FIG. 2B, multiple item images corresponding to a new second group of products recommended for the user are currently being displayed in the recommended item area 270, with the second group of recommended products having been generated by the MSPR service in response to the user selection of item 6. Information related to the selected item 6 is now displayed in the selected item area 205, such as to indicate that item 6 is a currently selected product of the user. For example, in this illustrated example, the information related to the selected item 6 includes item 6 image 210 and various product information 212 (e.g., product name, description, price), as well as user-selectable control 214 that may be selected by the user to view additional information related to the item 6 (and optionally to initiate a purchase of the item 6), and user-selectable control 216 that may be selected by the user to de-select or otherwise remove the item 6 from being a currently selected item (e.g., if the recommended items of the second group, which are based in part on item 6 being a currently selected item, are determined by the user to be less relevant than the recommended items of the first group or otherwise to not be of sufficient interest to the user). In other embodiments, other information and user-selectable controls related to the currently selected item may be shown in the selected item area 205, such as to also have user-selectable controls via which the user may initiate a purchase of the currently selected item, rate or otherwise provide feedback regarding the currently selected item, etc."); and
receive user-directed regeneration input (see para [0032], "For example, in this illustrated example, the information related to the selected item 6 includes item 6 image 210 and various product information 212 (e.g., product name, description, price), as well as user-selectable control 214 that may be selected by the user to view additional information related to the item 6 (and optionally to initiate a purchase of the item 6), and user-selectable control 216 that may be selected by the user to de-select or otherwise remove the item 6 from being a currently selected item (e.g., if the recommended items of the second group, which are based in part on item 6 being a currently selected item, are determined by the user to be less relevant than the recommended items of the first group or otherwise to not be of sufficient interest to the user). In other embodiments, other information and user-selectable controls related to the currently selected item may be shown in the selected item area 205, such as to also have user-selectable controls via which the user may initiate a purchase of the currently selected item, rate or otherwise provide feedback regarding the currently selected item, etc.")
generate the content brief output based on the written content associated with the product, the visual content associated with the product, and the user-directed regeneration input (see para [0032], " At some time after the multiple item images corresponding to the first group of recommended products is displayed to the user in the recommended item area 270 of FIG. 2A, the user interacts with the display 200 to select item 6 image 253 at grid position 272b:270b, such as to indicate an interest in item 6 and/or in items related to item 6, with the results of the selection being illustrated in FIG. 2B. In particular, in the modified display 200 of FIG. 2B, multiple item images corresponding to a new second group of products recommended for the user are currently being displayed in the recommended item area 270, with the second group of recommended products having been generated by the MSPR service in response to the user selection of item 6. Information related to the selected item 6 is now displayed in the selected item area 205, such as to indicate that item 6 is a currently selected product of the user. For example, in this illustrated example, the information related to the selected item 6 includes item 6 image 210 and various product information 212 (e.g., product name, description, price), as well as user-selectable control 214 that may be selected by the user to view additional information related to the item 6 (and optionally to initiate a purchase of the item 6), and user-selectable control 216 that may be selected by the user to de-select or otherwise remove the item 6 from being a currently selected item (e.g., if the recommended items of the second group, which are based in part on item 6 being a currently selected item, are determined by the user to be less relevant than the recommended items of the first group or otherwise to not be of sufficient interest to the user). In other embodiments, other information and user-selectable controls related to the currently selected item may be shown in the selected item area 205, such as to also have user-selectable controls via which the user may initiate a purchase of the currently selected item, rate or otherwise provide feedback regarding the currently selected item, etc." and see para [0041], "In other embodiments, various other functionality may be provided by an embodiment of the MSPR service, such as for display in a GUI of the MSPR service. For example, in some embodiments, a user may interact with various information corresponding to a displayed recommended product to provide feedback with respect to that product, such as to provide positive and/or negative ratings with respect to the displayed recommended product (e.g., a numerical rating, a star rating, a thumbs-up/down rating, etc.), with such feedback being used in various ways in various embodiments, such as to reduce (if a negative rating) and/or increase (if a positive rating) the relevance scores for products that are determined to be similar to or otherwise related to the rated displayed recommended product. In addition, in some embodiments, the users may interact with the displayed images in various other ways to obtain and/or view information related to recommended products, such as by "hovering" over a displayed image of a recommended product (e.g., with a mouse pointer) to cause information related to a recommended product associated with the displayed image to be provided to the user (e.g., price, description, sales information, availability, etc.). In still other embodiments, functionality may be provided that allows the user to interact with the GUI to initiate a purchase of a displayed recommended item. In addition, in some embodiments, information may be displayed or otherwise provided to users to indicate why a particular item is being recommended (e.g., this particular item is highly relevant for being viewed by users who also viewed currently selected item 109, and is moderately relevant for being a top seller for an initially selected category of items).")
Claims 14-15, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Selinger and Short, as applied above, and further in view of Aberle (US 11,748,577 B1).
Claims 14 and 19:
Selinger and Short do not specifically disclose wherein the suggested strategy output is received from a large language model. In analogous art, Aberle discloses the following limitations:
wherein the suggested strategy output is received from a large language model (col 8, line 57 to col 9, line 13, "For example, the generative stitching subsystem 160 may generate a language model influence matrix based on the discrete requirement and re-ranked content from the semantic re-ranking and weighted re-ranking subsystem 140. The generative stitching subsystem 160 may provide the language model influence matrix as input via the language model API endpoint 150, which may interface with the language model 155. The language model 155 may include a pretrained deep-learning AI large language model trained to generate text from an input such as the language model influence matrix. An example of the language model 155 may include the OpenAI GPT-3 language model, Google LAMBDA, BigScience BLOOM, Multitask Unified Model (MUM), or other transformer-based language models. The language model 155 may return automatically generated text based on the language model influence matrix. In some examples, the generative stitching subsystem 160 may obtain, as an output of the language model 155, different sets of automatically generated text that are different from one another." )
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Aberle with Selinger and Short because using an LLM provides a useful tool for generating content when integrating with challenging amount of unstructured text (see Aberle, col 1, line 17-36)
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for generating content based on large language models as taught by Aberle in the Selinger and Short combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 15 and 20:
Further, Selinger discloses the following limitations
wherein the large language model is configured to: receive, at least, the attribute insight output and the demographic insight output; receive at least one image prompt; and generate the suggested strategy output based on, at least, the at least one image prompt, the attribute insight output, and the demographic insight output (see para [0011], [0013], [0022], [0030], showing attribute and image inputs, prompts and outputs used to generate and display recommendations and and see para [0055], " While not illustrated here, the MSPR system may further have other modules or associated functionality in other embodiments, such as to generate some or all of the user interaction information 324 (e.g., based on monitoring users' interactions with retailers or other services; based on retrieving and processing information from retailers or other services related to such interactions, such as to identify data to be used with particular recommendation strategies and/or to identify particular recommendation strategies based on automatically learned types of relationships between items; based on users' interactions with the MSPR system to select particular items or otherwise provide feedback specific to particular items and/or to relationships between particular items, such as based on series of selections of recommended items or in other manners; etc.), to generate some or all of the product information 324, etc. Additional details related to various operations of embodiments of the MSPR system and an associated MSPR service are included elsewhere." where it is obvious to one o ordinary skill in the art that learning could be implemented using the LLM used to generate content in Aberle)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for generating content based on large language models as taught by Aberle in the Selinger and Short combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mendenhall et al. (US 2013/0325630 A1), a system for providing personalized content browsing experience where an archetype of a user is determined and archetype is associated with at least one attribute and content is filtered based on at least one attribute associated with the determined archetype and at least one descriptor associated with the content
Hawkins et al. (AU 2015218438 A1), a system that constructs a robust recipient profile where the system receives data associated with recipient digital interactions from, e.g., streaming and/or batch sources and the recipient data may include digital transactional data, social media data, or other recipient-specific information
Zhecev "A dive into the marketing trends of 2024: insights to unlocking potential", a paper that examines the most contemporary trends in marketing as seen by world-renowned consults, strategists, CMOs and other decision-maker which includes embracing the capabilities of GenAI, ML, LLMs, AR/VR, among others and displaying brand engagement and sustainability sets new standards in marketing tactics
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
Prosecution Insights