DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 23 May 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 statements (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Eligible Subject Matter
The claims are considered eligible subject matter because the abstract idea is integrated into practical implementation by necessarily being rooted in technology.
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 1-3, 6-12, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olson (US 2017/0124603 A1) in view of Douglas et al. (US 2017/0011423 A1) (hereinafter Douglas).
Claims 1, 10 and 19:
Olson, as shown, discloses the following limitations of claims 1, 10 and 19:
A method (and corresponding non-transitory computer readable medium – see para [0059]-[0061], showing equivalent computing functionality and components) comprising: causing a smart display in an environment to present a set of content related to one or more items located in the environment, wherein the smart display presents the set of content for a first time period (see para [0031]-[0032], “The marketing display 100 can be a multimodal display and can operate in different display modes. Referring now to FIG. 2, the marketing display 100 can operate in a first display mode 200 in which the display screen 102 can present a prescribed product-load for the particular marketing display 100. For example, in the first display mode 200 the display screen 102 can present a consumer-focused or shopper-focused communication, such as an advertisement 202 promoting one of the products 114 such as product A (as shown), or any other products 114 or combinations of products 114 as desired by the manufacturer or store. In various configurations, the first display mode 200 can be configured to operate during select times, such as during store hours or when a consumer 120 is detected in proximity to the shelf 110 by the sensor 106. Referring now to FIG. 3, the marketing display 100 can operate in a second display mode 300 in which the display screen 102 can switch from the consumer-focused communication to an employee focused presentation. In one such configuration, the second display mode 300 can display an image or video that includes the configuration of the shelf 110, an image of each product 114, the desired placement of products 114 on the shelf trays 112, and a scannable UPC barcode for each product 114. Such product load information can assist the employee in properly configuring the shelf 110 and products 114 according the desire of the manufacturer or store. In this configuration, the employee 130 can read the code from the display screen 102 with a bar code scanner to determine if one or more of the products 114 are in stock so as to determine whether to restock the shelf 110 or order more products 114. In various configurations, the second display mode 300 can be configured to operate during select times, such as during hours in which the store is closed, or when an employee 302 is detected by the sensor 106. In various embodiments, the employee 302 can be detected through a mobile app that is executing on a smartphone 304, detection of an employee badge 306 by the sensor 106, detection of a signal from an employee transponder 308 such as an RFID or other device (that in an embodiment can be incorporated into the employee identification badge 306), or facial recognition or other recognition of the employee 302 by the sensor 106 for example by identifying an employee uniform or article of clothing. In an configuration, the smart phone 304 can interact with the sensor 106 of the marketing display 100 through any suitable means, including but not limited to networked communications, point-to-point wireless communications such as BLUETOOTH, IBEACONS, or NFC (near field communications), or any suitable visual or audio cues produced by the smartphone 304 sensed by the sensor 106. In various configurations, the marketing display 100 can operate in the second display mode 300 by executing software in the marketing display 100 or by receiving information from a separate computer system to which the marketing display 100 is in communication.”);
accessing a first set of location data captured by location sensors coupled to one or more shopping carts, the first set of location data indicating a location of a user of a shopping cart in the environment (see para [0032], “In various embodiments, the employee 302 can be detected through a mobile app that is executing on a smartphone 304, detection of an employee badge 306 by the sensor 106, detection of a signal from an employee transponder 308 such as an RFID or other device (that in an embodiment can be incorporated into the employee identification badge 306), or facial recognition or other recognition of the employee 302 by the sensor 106 for example by identifying an employee uniform or article of clothing. In an configuration, the smart phone 304 can interact with the sensor 106 of the marketing display 100 through any suitable means, including but not limited to networked communications, point-to-point wireless communications such as BLUETOOTH, IBEACONS, or NFC (near field communications), or any suitable visual or audio cues produced by the smartphone 304 sensed by the sensor 106. In various configurations, the marketing display 100 can operate in the second display mode 300 by executing software in the marketing display 100 or by receiving information from a separate computer system to which the marketing display 100 is in communication.” and see para [0048], “In process block 814, the marketing display identifies the consumer or an activity of the consumer. For example, as described above, a sensor of the marketing display can determine the identity of the consumer or an identifying characteristic of the consumer. In another example, a sensor can determine the activity of the consumer, such as whether the consumer picks up a product, places a product into the consumer's shopping cart, or touches an area of the display requesting additional information. Processing continues to process block 816.”);
accessing sensor data captured by sensors coupled to the one or shopping carts (see para [0048], “In process block 814, the marketing display identifies the consumer or an activity of the consumer. For example, as described above, a sensor of the marketing display can determine the identity of the consumer or an identifying characteristic of the consumer. In another example, a sensor can determine the activity of the consumer, such as whether the consumer picks up a product, places a product into the consumer's shopping cart, or touches an area of the display requesting additional information. Processing continues to process block 816.”);
detecting an action performed by the user based on the accessed sensor data, the action being performed in relation to a first item of the one or more items (see para [0037], “if a sensor 106 detects that the consumer has touched or is picking up a product 114, the fourth display mode 500 can display product specific information 502 about the product 114. The marketing display 100 could also initiate a visual feature, such as a light on the shelf where the product is located, associated with that product.”);
identifying…the detected action based on the accessed sensor data (see para [0040], “the marketing display 100 can be configured to provide analytic data about the consumer to the store, to a manufacturer, or to another third party. Analytic data can include, but is not limited to, information about consumer activity such as consumer traffic near the marketing display 100 as sensed by one or more sensors 106, consumer dwell time at the marketing display 100, consumer identifiable demographic information, consumer purchase behavior at the marketing display 100, and consumer purchase history. In an embodiment, captured video of consumers can be gathered by the marketing display 100. Analytic data can help to determine the amount of walk-by traffic, determine the number of glances by consumers at the marketing display 100, determine whether the marketing display 100 has garnered the attention of consumers, determine if a consumer 120 has engaged with the products 114 or the marketing display 100, and whether the engagement has resulted in a purchase of a product 114 or repurchase of a product 114. Analytic data can assist stores, manufactures, advertisers, and other parties in understanding the effectiveness of advertising, promotions, consumer loyalty, and consumer behavior in general. Other analytic data can also be determined as would be understood by one of ordinary skill in the art. In an embodiment, consumer data and analytic data can be anonymized or tokenized to safeguard the identity of consumers and ensure that the marketing display 100 complies with any applicable privacy laws, regulations, or policies.”);
causing a second set of content to be presented to the user based on the proximity data stored in relation to the user (see para [0030]-[0031], "] The marketing display 100 can include a sensor 106. In various configurations, the sensor 106 can sense the presence of a consumer 120, employee, or the like, in proximity to the shelf 110, aspects about the consumer 120 including visible or electromagnetic detectable aspects, presence of products 114, or aspects about the products 114. In various configurations, the sensor 106 can be a camera, an infrared detector, a pressure sensor, a contact sensor, and so forth. The marketing display 100 can be a multimodal display and can operate in different display modes. Referring now to FIG. 2, the marketing display 100 can operate in a first display mode 200 in which the display screen 102 can present a prescribed product-load for the particular marketing display 100. For example, in the first display mode 200 the display screen 102 can present a consumer-focused or shopper-focused communication, such as an advertisement 202 promoting one of the products 114 such as product A (as shown), or any other products 114 or combinations of products 114 as desired by the manufacturer or store. In various configurations, the first display mode 200 can be configured to operate during select times, such as during store hours or when a consumer 120 is detected in proximity to the shelf 110 by the sensor 106." and see para [0035], "t. In a configuration, the third display mode 400 can be combined with the detection of an employee 302 as described above for FIG. 3, in which case the display indicator can be made conditional upon the detection of the employee 302 being proximate the marketing display 100." and see para [0042], “At process block 802, the marketing display receives one or more videos. In a configuration the marketing display can receive multiple videos that are stored in memory and played at the appropriate time. For example, the marketing display can receive the default consumer focused video to be played as the selected video as well as one or more alternative videos that are to be played if certain conditions are met, as described below. In another configuration, the marketing display receives a live video stream of the video that is to be contemporaneously played in real-time. Processing continues to process block 804." and see para [0074]).
Olson, however, does not specifically disclose identifying, based on the first set of location data, that the user is within a threshold area of the smart display in the environment during the first time period. In analogous art, Douglas discloses the following limitations:
accessing a first set of location data captured by location sensors coupled to one or more shopping carts, the first set of location data indicating a location of a user of a shopping cart in the environment (see para [0005], "Some embodiments include a system for providing individualized targeted interaction information in an environment based on detection of a device associated with the user within the environment using a plurality of sensors positioned within the environment. In some embodiments, the system includes a memory storing a set of instructions and at least one processor configured to execute the instructions to determine location information of the device based on a communication between the device and at least one of the plurality of sensors, and determine identification information of the user associated with the device. The system may access profile information associated with the user, determine targeted interaction information based on the location information of the device and the profile information associated with the user, and provide the targeted interaction information for display to the user." and Fig 1 and see para [0076], "Numerous other scenarios may be implemented using the disclosed systems and methods. In some embodiments, similar to the above, sensor system 124 may associate one or more items with user 131. For example, in some embodiments, merchant server 122 may determine that user 131 is carrying one or more items or has one or more items in a shopping cart, for example, by detecting user interaction with an item and/or movement of the items in the merchant environment along with user" where it would be obvious to one of ordinary skill in the art that the sensors could be the sensors in the cart of Olson);
identifying, based on the first set of location data, that the user is within a threshold area of the smart display in the environment during the first time period (see para [0053], "In some embodiments, merchant server 122 may be able to determine the duration of the user's presence in the area based on a plurality of communications associated with client device 130 and a particular beacon 230, customer tag 132 and a particular hub 232, or a combination of both. For example, each of beacon 230, hub 232, client device 130 and user tag 132, depending on the implementation, may be programmed to emit a communication signal or listen for a communication signal at a predetermined frequency, such as every 500 ms, for example. Where multiple communications are received by client device 130 or hub 232 over time, the duration of a user's presence can be determined. Merchant system 122 may determine to provide certain targeted interaction information when a user's duration at a particular location exceeds a predetermined threshold. In some embodiments, the threshold may alternatively be dynamically determined based on a user's profile information, the user's location within the environment, products within the user's vicinity, etc. Additionally, merchant server 122 may determine that the user's locations corresponding to events 410 and 415 are near an output device 234. Merchant server 122 may then determine whether to provide targeted interaction information via an output device 234 based on the detected presence of user 131." where user location being near an output device shows within a threshold area);
identifying, based on the first set of location data, that the user is within a threshold area of the smart display in the environment during the first time period (see para [0053], "In some embodiments, merchant server 122 may be able to determine the duration of the user's presence in the area based on a plurality of communications associated with client device 130 and a particular beacon 230, customer tag 132 and a particular hub 232, or a combination of both. For example, each of beacon 230, hub 232, client device 130 and user tag 132, depending on the implementation, may be programmed to emit a communication signal or listen for a communication signal at a predetermined frequency, such as every 500 ms, for example. Where multiple communications are received by client device 130 or hub 232 over time, the duration of a user's presence can be determined. Merchant system 122 may determine to provide certain targeted interaction information when a user's duration at a particular location exceeds a predetermined threshold. In some embodiments, the threshold may alternatively be dynamically determined based on a user's profile information, the user's location within the environment, products within the user's vicinity, etc. Additionally, merchant server 122 may determine that the user's locations corresponding to events 410 and 415 are near an output device 234. Merchant server 122 may then determine whether to provide targeted interaction information via an output device 234 based on the detected presence of user 131." where a user duration at a particular location exceeding a predetermined threshold shows within a threshold area of the smart display in the environment during the first time period);
identifying a timestamp for the detected action based on the accessed sensor data (see para [0053] and see para [0071], " As another example, merchant server 122 may be able to determine from the user's profile information, that the user's current visit is the third such visit within a period of time. Merchant server 122 may also be able to determine from the profile information, the degree of interaction of the user on the previous visits, such as the duration of time spent in the environment, the areas visited within the environment, whether any items were purchased, etc. Based on this information, merchant server 122 may analyze such profile information to determine interaction information that may be relevant to user 131. For example, merchant server may provide targeted interaction information offering personalized assistance to user 131, where user 131 may not have purchased anything in the prior trips, or merchant server 122 may offer special discounts for the repeated purchases within the period of time, etc. In some embodiments, where repeat purchases are made by user 131, for example, merchant server 122 may determine targeted interaction information offering to automatically fulfill a purchase for the user 131. For example, merchant server 122 may provide targeted interaction information via an interface on client device 130 enabling selection to fulfill the purchase order and have the order ready for pick-up at a check-out area or other area of the merchant environment. In some embodiments, the transaction may be automatically fulfilled based on transaction account information stored as profile information in association with user 131." where duration of time spent in the environment can be considered to show identification of timestamps would be obvious to one of ordinary skill in the art in order to make such a determination of duration);
identifying whether the timestamp is within a threshold amount of time after the first time period (see para [0053], where a user duration at a particular location exceeding a predetermined threshold shows within a threshold area of the smart display in the environment during the first time period where it is obvious to one of ordinary skill in the art that a duration has a start and end point);
in response to the timestamp being within the threshold amount of time after the first time period, storing, in association with the user, proximity data indicative of an interaction with the set of content presented by the smart display (see para [0071], showing information related to the duration and degree of interaction of the user is stored in a user profile)
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Douglas with Olson because identifying, based on the first set of location data, that the user is within a threshold area of the smart display in the environment during the first time period enables more effective analysis to be made based on user location and other behavior patterns that can be advantageous in providing individualized services based on a user's presence (see Douglas, para [0002]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for user detection and interaction as taught by Douglas in the marketing display system of Olson, 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 2, 6-8, 11, 15-18, 20:
Olson does not explicitly disclose wherein storing, in association with the user, proximity data indicative of an interaction with the set of content presented by the smart display comprises: storing, in association with the user, the proximity data in a first linked list associated with the first item, wherein the first linked list includes a plurality of sets of proximity data and a plurality of sets of content. In analogous art, Douglas discloses the following limitations:
wherein storing, in association with the user, proximity data indicative of an interaction with the set of content presented by the smart display comprises: storing, in association with the user, the proximity data in a first linked list associated with the first item, wherein the first linked list includes a plurality of sets of proximity data and a plurality of sets of content (see para [0021], "Components of system 100 may be configured to provide an enhanced user experience by providing individualized or targeted interaction information to user 131 as determined based on detection of the user's location in an environment and retrieval of certain profile information of the user relevant to the user's location. For example, in some embodiments, aspects of sensor system 124, provided as part of merchant system 120, may be configured to detect the proximity of a user within a merchant environment. Profile information of the detected user, stored by or accessible to merchant server 122, may be accessed to determine relevant targeted interaction information for output to the user based on the user's detected location. For example, as further described herein with respect to the merchant environment, targeted interaction information may be provided to the user based on a user's location such as at storefront entry, or a particular aisle or section of an aisle, or other location of the store such as the checkout area, etc. In some embodiments, targeted interaction information may include information regarding one or more products or services or other relevant information. The targeted interaction information may be based on profile information of user 131, which may be accessed by merchant system 120 from one or more sources, including a service provider system 110, such as a financial service entity. For example, in some embodiments, profile information may include transaction history or spending information, payment account information, loyalty program information or other related information stored by or accessible to service provider system 110." and see para [0025], [0058] and see para [0047], showing the use of databases to store and retrieve profile information where it is obvious to one of ordinary skill in the art that a relational database shows linked list and see para [0067], " Merchant server may then access profile information of the user (step 515) to identify any information that may be relevant for providing targeted interaction information, based at least on the location of the user. As described above, merchant server 122 may access a subset of profile information categorized or indexed according to the detected location of user 131 or phase of interaction with the environment to facilitate the determination of relevant targeted interaction data.")
causing a second smart display to present the second set of content, wherein the second smart display is at a client device associated with the user (see para [0018], "The individualized services or information provided to the user may dynamically change based on changes in the user's detected location. In some embodiments, the services or information may be provided to the user in part via a client device, such as a smartphone, or other display or output in the merchant environment.")
wherein the proximity data associated with the user is further indicative of a distance between the user's shopping cart and the smart display (see para [0053], " In some embodiments, the threshold may alternatively be dynamically determined based on a user's profile information, the user's location within the environment, products within the user's vicinity, etc. Additionally, merchant server 122 may determine that the user's locations corresponding to events 410 and 415 are near an output device 234. Merchant server 122 may then determine whether to provide targeted interaction information via an output device 234 based on the detected presence of user 131.")
wherein the proximity data includes the first time, a second time, and the action (see para [0051]-[0054], where it is obvious to one of ordinary skill in the art that determination of a duration shows a first and second time as the end points for the duration).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Douglas with Olson because storing the proximity data enables more effective analysis to be made based on user location and other behavior patterns that can be advantageous in providing individualized services based on a user's presence (see Douglas, para [0002]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for user detection and interaction as taught by Douglas in the marketing display system of Olson, 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 3, 9, 12:
Further, Olson discloses the following limitations:
wherein each set of proximity data is associated with a first set of content presented prior to an interaction of the proximity data and a second set of content presented after an interaction of the proximity data and each set of content is associated with a first set of proximity data including a first interaction with the item that occurred before presentation of the set of content and a second set of proximity data including a second interaction with the item that occurred after presentation of the set of content (see para [0038]-[0039], "] Referring now to FIG. 6, the marketing display 100 can operate in a fifth display mode 600 in which the display screen 102 can display product related information based at least in part on the identify of a consumer 120. In this embodiment, the marketing display 100 can identify the identity of the consumer 120 or an identifying characteristic of a consumer 120. In a first configuration, the consumer 120 can be identified by a smartphone 304 carried by the consumer 130. For example, the consumer 120 can be identified using query-response or push-notifications from an app on the smartphone 304, by identifying a mac address associated with the smartphone 304, NFCs, or by other suitable means for identifying the smartphone 304 and correlating that information with the consumer 120 or the previous purchases of the consumer 120. In a second configuration, the identity of the consumer 120 can be identified using facial recognition by the sensor 106, for example a high resolution camera. In a third configuration, an identifying characteristic of the consumer 120 can be identified. For example, the consumer 120 may be wearing a hat or shirt that identifies a particular sports team. Based at least in part on either the identity of the consumer 120, or an identifying characteristic of the consumer 120, the marketing display 100 can operate in a fifth display mode 600. In the fifth display mode 600, the display screen 102 can present a consumer-focused advertisement that is tailored to the identity of, or an identifying characteristic of, the consumer 120. For example, the fifth display mode 600 can display a sport-related advertisement for a consumer 120 based on the detection. For example, if the product 114 is detergent, then the fifth display mode 600 can present a customized advertisement directed to removing stains that occur from sports-related activities. In another example, the fifth display mode 600 can display a health-related advertisement. For example, if the marketing display 100 can correlate the consumer 120 with a health related issue such as diabetes, or previous healthy choice purchases, then the fifth display mode 600 can switch to an advertisement for the low-sugar, or low-fat version of the product 114. In a configuration, the fifth display mode 600 can direct the consumer 120 to the placement of a product 114 on the shelf 110. The fifth display mode 600 can be configured to display advertising specific to the age, gender, nationality, purchase history, geographic location, or any other demographic of the consumer 120 as would be understood by one of ordinary skill in the art. It will be appreciated that a plurality of displays, such as visual displays associated with specific shelves or products, can be used in accordance with embodiments described herein. Referring now to FIG. 7, the marketing display 100 can operate in a sixth display mode 700. In the sixth display mode 700, the display screen 102 can present real-time promotions or real-time information about products 114. For example, the sixth display mode 700 can present price promotions as they become available in real-time. For example, if a 2 for 1 promotions starts on a first date and ends on a second date, the sixth display mode 700 can display a promotional video while the promotion is active. In a configuration, the promotional video of the sixth display mode 700 can alternate with other display modes 200, 300, 400, 500, and 600 or be displayed periodically. In a configuration, the sixth display mode 700 can indicate to the consumer 130 the duration of the promotion. In another example, the sixth display mode 700 can present other real-time information to consumers 130, for example the levels of stock of particular products 114 in the store. In a configuration, the sixth display mode 700 can also present information about where the out-of-stock products 114 are in stock, for example at other nearby stores or online. In another configuration, the sixth display mode 700 can present comparative pricing, manager specials, or other information of importance to consumers 130." and see para [0042], "At process block 802, the marketing display receives one or more videos. In a configuration the marketing display can receive multiple videos that are stored in memory and played at the appropriate time. For example, the marketing display can receive the default consumer focused video to be played as the selected video as well as one or more alternative videos that are to be played if certain conditions are met, as described below. In another configuration, the marketing display receives a live video stream of the video that is to be contemporaneously played in real-time. Processing continues to process block 804.")
wherein the action is one of viewing the first item, picking up the first item, or putting the first item in the user's shopping cart (see para [0037], " Referring now to FIG. 5, the marketing display 100 can operate in a fourth display mode 500 in which the display screen 102 can display product related information based at least in part on the actions of a consumer 120. A sensor 106 can detect an action of a consumer 120 and modify the fourth display mode 500 accordingly. For example, if a sensor 106 detects that the consumer has touched or is picking up a product 114, the fourth display mode 500 can display product specific information 502 about the product 114. The marketing display 100 could also initiate a visual feature, such as a light on the shelf where the product is located, associated with that product. Product specific information 502 can include, but is not limited to, the price of the product 114, the price per unit of the product 114, discounts for purchasing the product 114 in multiple units, related products, nutritional information, and so forth. In another configuration, the consumer 120 can press an information sensor 504 to receive information. In a configuration, the information sensor 504 can be a passive sensor, such as a sticker, tag, or zone on the shelf 110 and the sensor 106 can detect the presence of an appendage of a consumer 120 in proximity to the information sensor 504. In another embodiment, the sensor 106 can be a touch sensitive portion of the marketing display 100, for example a touch sensitive display screen 102. In an embodiment, when the consumer 130 has selected and removed the product 114 from the shelf 110, the fourth display mode 500 can thank the consumer 120. In a configuration, the sensor 106 can detect when the consumer 120 has placed the selected product 114 in a shopping cart and thank the consumer 120 after the detection. In a configuration, a shopping cart can include a sensor and be in communication with the marketing display 100. In one embodiment, specific products can be associated with specific locations such that a consumer might win a prize a check-out for selecting a lucky product from the shelf. The lucky product can have the location and associated UPS or identifying information recorded such that the consumer only wins the prize, coupon, or the like upon completing the purchase of the lucky product. Embodiments of gamification for shopping applications are contemplated, where consumers can earn points, prizes, discounts, and the like by taking certain actions detected by the display system and associated components." and see para [0040], [0048])
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Olson and Douglas, as applied above, and further in view of Gore et al. (US 2024/0320424 A1) (hereinafter Gore)
Claims 4 and 13:
Olson and Douglas do not specifically disclose generative language models. In analogous art, Gore discloses the following limitations:
creating a first prompt including the proximity data and a first request for the second set of content to present to the user (see para [0017]-[0018], ". The content intelligence system determines context rich, personalized prompts for individual users in real time based on machine learned insights to minimize the number of prompts required to generate accurate, engaging content for each specific user. The contextual insights included in prompts determined by content intelligence system, eliminate A/B testing and other testing and validation cycles required by other systems to generate accurate responses and/or engaging content for specific topics. The content intelligence system uses the machine learned contextual insights to provide higher quality content faster and more efficiently (e.g., by requiring fewer input prompts and fewer network and compute resources to generate responses to the additional prompts). The content intelligence system also improves the quality of the outputs generated by language models by augmenting their stagnant training datasets with machine learned insights that are continuously improved and may be updated in real time. Embedding dynamic, highly specific context information into prompts gives language models access to fresh, up to date data about specific topics that are not found their training corpuses. The language models can then use the machine learned insights to generate accurate and highly personalized content for vast number of unique users (e.g., any user having one or more data points in a data cloud storing identity records). The data cloud described herein includes identity records for over 300 million unique individuals, therefore, the content intelligence system can generate accurate, unique, and meaningful content for more than 300 million individuals in real time (e.g., a fraction of a second). The content intelligence system may be implemented within the SaaS network architecture described in FIG. 1 below so that the content generation functionality may be scaled to generate accurate, unique content for individual users included in multiple audience segments. Each audience segment may be targeted by multiple content campaigns that are specific to a brand, product, or any subject matter of interest." and see para [0029], "Identity records in the data cloud 206 may be stored in identity graphs 208 that include multiple nodes and edges connecting two or more nodes. Each node may include a unique identifier and one or more consumer attributes associated with the unique identifier may be stored as node metadata. Consumer attributes may include one or more pieces of consumer data (e.g., location data, demographic data, device metadata, machine learned attributes, interest codes, and the like), event data (e.g., impressions and other engagement data, and the like) and transaction data (purchase records, purchase amounts, transaction metadata, and the like) associated with the target user. For example, one node may be an email address (e.g., targetcustomer@gmail.com) and the identity attributes included as node metadata may include event data listing timestamped records of emails sent to the email address and open and clickthrough events for the sent emails. Other node metadata may include transaction data listing timestamped purchase records for products ordered using the email address and consumer data listing a physical address, device ID or other identifier associated with the email address, interest codes that identity the subject of webpages accessed by the email address, and the like." and see para [0033], " Identity attributes for target users determined by the identity component 230 may be provided to the learning module 240. The learning module 240 may include one or more context models 242 that provide the machine learned insights for content personalization and a prompt generator 244 that incorporates the machine learned insights into a prompt used to generate natural language output from a language model." and see para [0064], "The data preprocessing module 604 may also process new and/or updated identity records that include new consumer data 520 (a new postal address associated with a user), new event data 522 (e.g., responses to previously generated personalized content included in completed campaigns), and/or new transaction data (e.g., purchases of products mentioned in previously generated personalized content). A DSP, ESP, or other publishing system may track clicks, views, conversions, and other event data capturing responses of users to pieces of content that include personalized content. The responses may be stored in the identity records of the users receiving the personalized content and the data preprocessing module 604 may the select new and/or updated identity records including the responses as input data 620 for retraining the context models 242 and/or language models 664. The learning module 240 may retain context models 242 that were originally trained on historical event data with the new set of input data 620 to continuously improve the accuracy of the consumer dimensions predicted by the context models 242. The retaining process may also increase one or more confidence metrics of the context models 242 and increase the specificity of generative AI outputs created using prompts that include the consumer dimensions." where it would be obvious to one of ordinary skill in the art that the consumer attribute data can include the proximity and content request data from the Olson and Douglas combination);
inputting the first prompt to a first generative language model, the first generative language model tuned on a set of linked lists, wherein the set of linked lists includes the first linked list and each linked list is associated with one of the one or more items in the environment (see para [0034], "To generate personalized content, prompts determined by the prompt generator 244 may be transmitted to a language generator 250. The language generator 250 may include one or more language models that may generate natural language text, images, and other content personalized based on a text description included in the prompts. The language models may be implemented as generative pre-trained transformer models that have a decoder-only transformer network with a context window having a pre-defined number of tokens and pre-trained parameters. The language models may be trained to predict the next token of text based on the previous tokens of text in a sequence. The language models may tokenize the text included in an input prompt, ingest the tokens, and generate a personalized text output that is specific to the ingested tokens." and see para [0053], "The context dimensions may include consumer dimensions for one or more users. To determine consumer dimensions, a set of input identity records for a user is passed through each trained deep learning model. A trained model for each consumer dimension (e.g., “price sensitive”, “intelligent” “interested in technology”, and the like) may score the identity records. In various embodiments, input identity records may be scored by three hundred or more models to determine P-scores and I-scores for hundreds of consumer dimensions. The models may output a scaled score for each dimension (e.g., a numerical value between 0 and 1, a percentage, and the like) and the output scores may be thresholded (e.g., compared to a pre-determined threshold score for each dimension) to determine the consumer dimensions. For example, users with records having scores that meet or exceed the predefined dimension threshold (e.g., 0.8) for a particular dimension (e.g., price-sensitive) may be classified as having the particular dimension." where it is obvious to one of ordinary skill in the art that the input identity records could be the linked list data from the Olson and Douglas combination); and
receiving, as output from the first generative language model, the second set of content (see para [0055], "The consumer dimensions for each identity records determined by the machine learning models may be appended to the identity records for a user that are stored in the data cloud. To generate personalized content for a user, a prompt generator may extract the consumer dimensions from the user's identity records. The consumer dimensions may then be filtered to reduce the number of consumer dimensions and engineer the prompt to elicit a targeted output from a language model that is personalized to the user but also optimized to achieve one or more campaign KPIs. The filtered dimensions may be embedded into one or more segments of a prompt and submitted to a generative AI system that returns targeted, personalized content generated by a language model.”).
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Gore with Olson and Olson because integrating a generative language model can leverage machine learning to make more effective insights in the system (see Gore, para [0002]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for optimizing language models based on user context as taught by Gore in the Olson and Gore 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 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Olson and Douglas, as applied above, and further in view of Dumouchel et al. (US 2017/0186068 A1) (hereinafter Dumochel)
Claims 5 and 14:
Olson and Douglas do not specifically disclose causing a second smart display to present the second set of content, wherein the second smart display is affixed to the shopping cart associated with the user. In analogous art, Dumouchel discloses the following limitations:
causing a second smart display to present the second set of content, wherein the second smart display is affixed to the shopping cart associated with the user (see para [0018], "In one embodiment, the invention can be characterized as an in-store location-aware shopping and merchandising system comprising: a portable shopping and merchandising device comprising: a processor; a non-transitory memory coupled to the processor; a shopping cart application module configured to run on the processor; at least one light sensor; a communication module communicatively coupled to the shopping cart software application module and the light sensor; and a display screen operatively coupled to the shopping cart application module, wherein the shopping cart application module is configured to perform the steps of: determining a current location of the portable shopping and merchandising device; receiving information about at least one store item, wherein each store item is associated with at least one location; and displaying on the display screen information about at least one of the at least one store item, wherein at least one of the at least one store item is associated with the current location." and see para [0055], [0070] and see para [0073], "The recipe database 1126 interacts with the store zones to advertiser zones table 1120, via the merchandise plan 1112, based on product UPCs, and return advertisements (wherein each advertisement is associated with at least one location) from the advertising system 1122, such as banners and full size ads, to the shopping cart application module for display on the portable shopping device 102, based on the location of the portable shopping device 102.").
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Dumouchel with Olson and Olson because including a display affixed to the shopping cart enables more information to be provided to the shoppers to enhance the experience (see Gore, para [0004]-[0017]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the in-store location aware shopping and merchandising system as taught by Dumouchel in the Olson and Gore 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.
Dey et al. (US 2017/0169440 A1), a system where shopping related data is obtained by using a weight sensor on a shelf to determine a reduction in weight of product on the shelf, and noting the time, and then detecting weight added to a near-by located shopping cart using a sensor, and noting the time
Remsudeen et al. (EP 3748565 A1), a system for tracking an environment that can obtain perception data from one or more perception capture hardware devices and one or more perception programs and identify an object classification of each of the plurality of objects and track each object of the plurality of objects in the environment
"A Guide to Digital Transformation for Brick-and-Mortar Retailers", a paper on digital transformation using RFID technology for brick and mortar retailers to improve the customer experience
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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