DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-10 are pending under this Office action.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Ryu (US 20190188501 A1) in view of Luzhnica, etc. (US 11516158 B1).
Regarding claim 1, Ryu teaches that a content generation device (See Ryu: Figs. 3-5, and [0047], “Referring to FIG. 3, a method of classifying road risk information according to an embodiment of the present invention may include an information collection step S310, an information processing step S320, an information learning step S330, an information classification step S340, and an information transmission step S350.”; and [0060], “Referring to FIG. 5, a mobile device 200 according to another embodiment of the present invention may include a communication unit 210, an input unit 220, a control unit 230, a display unit 240, and a storage unit 250”) comprising:
one or more memories storing instructions (See Ryu: Figs. 3-5, and [0067], “The storage unit 250 may store and manage the collected road state information, at least one predefined learning model, and the road risk information classified through the learning model”); and
one or more processors configured to execute the instructions(See Ryu: Figs. 3-5, and [0012], “In addition, the control unit may activate a mobile application, perform image processing on the collected road state information through the activated mobile application, convert a result of the image processing into a predefined grayscale image, and learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning”; and [0063], “The control unit 230 may extract a Region of Interest (ROI) from a road image among the collected road state information, perform image processing on the extracted ROI, and create a predefined grayscale image on the basis of a result of the image processing”. The control unit to processing image is mapped to the processor) to:
acquire a road surface data indicating a state of a road surface through which a target user passes (See Ryu: Fig. 1, and [0034], “The vehicle device 100 is attached to a vehicle and may acquire various road state information, such as road images, sensing values and the like, from a road on which the vehicle is running. The vehicle device 100 may include a camera for acquiring the road images, and a sensor for acquiring the sensing values. The vehicle device 100 may connect to the user device 200 through wireless communication and provide the user device 200 with various types of acquired information”); and
input a prompt including the road surface data to a machine-learned content generation model (See Ryu: Figs. 3-5, and [0050], “The information learning step S330 may learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning, and recognize road risk information on the basis of a result of the learning”. Note that the road image is input to the ML model, not the prompt which will be addressed in a secondary art) to generate, by the content generation model, content to be presented to the user passing through the road surface (See Ryu: Figs. 3-5, and [0052], “The information transmission step S350 may transmit the detected road surface defects to the user device”).
However, Ryu fails to explicitly disclose that input a prompt including the road surface data to a machine-learned content generation model.
However, Luzhnica teaches that input a prompt including the road surface data to a machine-learned content generation model (See Luzhnica: fig. 92, and Lines 44-66, “Prompt data is inputted in methods typically after ITSD input, typically with most, generally all, or all of the situational prompt data, if any, being inputted prior to most, generally all, or all of the instructional prompt data. Prompt data can be inputted by selection of selectable prompt content (which can be, e.g., presented via an interface as full content or presented by terms, symbols, etc., that represent content). Prompt data also or alternatively can be input through direct submissions to a system (e.g., by typing in content into a freeform submission box associated with instructional prompt input, situational prompt input, or both). E.g., in aspects, a user is presented via the interface with the ability to directly input at least some instructional prompt content; to select 2-7, 2-6, 3-5, 3-7, or 2-5 situational prompt content categories, and to input type-restricted/size-restricted additional situational prompt content, instructional prompt content, or both (e.g., by presenting 2, 3, 4, or 5 fields, where a user inputs select discrete content such as a person's name, associated organization, etc.). In aspects, systems do not provide for/methods do not comprise input of situational prompt input”. Note that instructional input, situational inputs to the machine learning model are automatically processed by the ML model to generate draft message and content).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Ryu to have input a prompt including the road surface data to a machine-learned content generation model as taught by Luzhnica in order to enables generating effective complex messages in a flexible and personalized manner in an effective manner (See Luzhnica: Fig. 1, and Col. 9 Lines 52-61, “The preceding discussion demonstrates that there are a wide variety of approaches that have been proposed or are being employed in systems for generating artificial intelligence-facilitated messages. Nonetheless, significant problems and limitations in messaging-related artificial intelligence systems persist, despite the significant amount of investment in the field, indicating that the development of truly effective artificial intelligence-facilitated messaging systems, which reliably generate effective complex messages in a flexible and personalized manner, requires new approaches involving significant human inventive ingenuity”). Ryu teaches a method and system that may provide an artificial intelligence system for providing road risk information based on the road surface information and using the ML model to generate content for the user; while Luzhnica teaches a system and method that may provide a user with access to an internet-connected computer system that comprises neural networks to generate messages and content for the user based on the user prompt inputs including instructional prompts and situational prompts. Therefore, it is obvious to one of ordinary skill in the art to modify Ryu by Luzhnica to generate content using ML model based on the road surface data and user input prompts. The motivation to modify Ryu by Luzhnica is “Use of known technique to improve similar devices (methods, or products) in the same way”.
Regarding claim 3, Ryu and Luzhnica teach all the features with respect to claim 1 as outlined above. Further, Ryu and Luzhnica teach that the content generation device according to claim 1, wherein
the one or more processors are further configured to execute the instructions to: analyze the road surface data using a road surface analysis model obtained by machine learning (See Ryu: Figs. 3-4, and [0038], “The database 400 may store and manage the collected road state information, at least one predefined learning model, and the road risk information classified through the learning model”; [0042], “The information processing unit 320 may extract a Region of Interest (ROI) from a road image among the collected road state information, perform image processing on the extracted ROI, and create a predefined grayscale image on the basis of a result of the image processing. Although all the road images may be used in the present invention, only the ROI may be converted and used as a predefined grayscale image for effective utilization of system resources. In addition, the ROI is set in a variety of forms. The ROI is set in a polygonal form, such as a rectangular or trapezoidal form, by adjusting the outer line of the ROI in a direction parallel to the lanes by utilizing a sense of perspective”; and [0050], “The information learning step S330 may learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning, and recognize road risk information on the basis of a result of the learning”); and
input a prompt including at least part of an analysis result of the road surface data to the content generation model to generate content by the content generation model (See Luzhnica: fig. 92, and Lines 44-66, “Prompt data is inputted in methods typically after ITSD input, typically with most, generally all, or all of the situational prompt data, if any, being inputted prior to most, generally all, or all of the instructional prompt data. Prompt data can be inputted by selection of selectable prompt content (which can be, e.g., presented via an interface as full content or presented by terms, symbols, etc., that represent content). Prompt data also or alternatively can be input through direct submissions to a system (e.g., by typing in content into a freeform submission box associated with instructional prompt input, situational prompt input, or both). E.g., in aspects, a user is presented via the interface with the ability to directly input at least some instructional prompt content; to select 2-7, 2-6, 3-5, 3-7, or 2-5 situational prompt content categories, and to input type-restricted/size-restricted additional situational prompt content, instructional prompt content, or both (e.g., by presenting 2, 3, 4, or 5 fields, where a user inputs select discrete content such as a person's name, associated organization, etc.). In aspects, systems do not provide for/methods do not comprise input of situational prompt input”; and Col. 9 Lines 51-62, “The preceding discussion demonstrates that there are a wide variety of approaches that have been proposed or are being employed in systems for generating artificial intelligence-facilitated messages. Nonetheless, significant problems and limitations in messaging-related artificial intelligence systems persist, despite the significant amount of investment in the field, indicating that the development of truly effective artificial intelligence-facilitated messaging systems, which reliably generate effective complex messages in a flexible and personalized manner, requires new approaches involving significant human inventive ingenuity”. Note that instructional input, situational inputs to the machine learning model are automatically processed by the ML model to generate draft message and content).
Regarding claim 9, Ryu and Luzhnica teach all the features with respect to claim 1 as outlined above. Further, Ryu and Luzhnica teach that a content generation method (See Ryu: Figs. 3-5, and [0047], “Referring to FIG. 3, a method of classifying road risk information according to an embodiment of the present invention may include an information collection step S310, an information processing step S320, an information learning step S330, an information classification step S340, and an information transmission step S350.”; and [0060], “Referring to FIG. 5, a mobile device 200 according to another embodiment of the present invention may include a communication unit 210, an input unit 220, a control unit 230, a display unit 240, and a storage unit 250”), comprising:
by a computer (See Ryu: Figs. 3-5, and [0037], “In addition, the deep learning algorithm refers to a technique used to allow a computer to make a decision and learn like a human being and to cluster or classify objects or data through the computer. For example, the deep learning algorithm includes Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM) and the like”),
acquiring a road surface data indicating a state of a road surface through which a target user passes (See Ryu: Fig. 1, and [0034], “The vehicle device 100 is attached to a vehicle and may acquire various road state information, such as road images, sensing values and the like, from a road on which the vehicle is running. The vehicle device 100 may include a camera for acquiring the road images, and a sensor for acquiring the sensing values. The vehicle device 100 may connect to the user device 200 through wireless communication and provide the user device 200 with various types of acquired information”); and
inputting a prompt (See Luzhnica: fig. 92, and Lines 44-66, “Prompt data is inputted in methods typically after ITSD input, typically with most, generally all, or all of the situational prompt data, if any, being inputted prior to most, generally all, or all of the instructional prompt data. Prompt data can be inputted by selection of selectable prompt content (which can be, e.g., presented via an interface as full content or presented by terms, symbols, etc., that represent content). Prompt data also or alternatively can be input through direct submissions to a system (e.g., by typing in content into a freeform submission box associated with instructional prompt input, situational prompt input, or both). E.g., in aspects, a user is presented via the interface with the ability to directly input at least some instructional prompt content; to select 2-7, 2-6, 3-5, 3-7, or 2-5 situational prompt content categories, and to input type-restricted/size-restricted additional situational prompt content, instructional prompt content, or both (e.g., by presenting 2, 3, 4, or 5 fields, where a user inputs select discrete content such as a person's name, associated organization, etc.). In aspects, systems do not provide for/methods do not comprise input of situational prompt input”. Note that instructional input, situational inputs to the machine learning model are automatically processed by the ML model to generate draft message and content) including the road surface data to a machine-learned content generation model (See Ryu: Figs. 3-5, and [0050], “The information learning step S330 may learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning, and recognize road risk information on the basis of a result of the learning”. Note that the road image is input to the ML model, not the prompt which will be addressed in a secondary art) to generate, by the content generation model, content to be presented to the user passing through the road surface (See Ryu: Figs. 3-5, and [0052], “The information transmission step S350 may transmit the detected road surface defects to the user device”).
Regarding claim 10, Ryu and Luzhnica teach all the features with respect to claim 1 as outlined above. Further, Ryu and Luzhnica teach that a non-transitory recording medium that records a program for causing a computer to function as a content generation device (See Ryu: Figs. 3-5, and [0047], “Referring to FIG. 3, a method of classifying road risk information according to an embodiment of the present invention may include an information collection step S310, an information processing step S320, an information learning step S330, an information classification step S340, and an information transmission step S350.”; and [0060], “Referring to FIG. 5, a mobile device 200 according to another embodiment of the present invention may include a communication unit 210, an input unit 220, a control unit 230, a display unit 240, and a storage unit 250”), the program causing the computer to execute:
acquiring a road surface data indicating a state of a road surface through which a target user passes (See Ryu: Figs. 3-5, and [0012], “In addition, the control unit may activate a mobile application, perform image processing on the collected road state information through the activated mobile application, convert a result of the image processing into a predefined grayscale image, and learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning”; and [0063], “The control unit 230 may extract a Region of Interest (ROI) from a road image among the collected road state information, perform image processing on the extracted ROI, and create a predefined grayscale image on the basis of a result of the image processing”. The control unit to processing image is mapped to the processor); and
inputting a prompt (See Luzhnica: fig. 92, and Lines 44-66, “Prompt data is inputted in methods typically after ITSD input, typically with most, generally all, or all of the situational prompt data, if any, being inputted prior to most, generally all, or all of the instructional prompt data. Prompt data can be inputted by selection of selectable prompt content (which can be, e.g., presented via an interface as full content or presented by terms, symbols, etc., that represent content). Prompt data also or alternatively can be input through direct submissions to a system (e.g., by typing in content into a freeform submission box associated with instructional prompt input, situational prompt input, or both). E.g., in aspects, a user is presented via the interface with the ability to directly input at least some instructional prompt content; to select 2-7, 2-6, 3-5, 3-7, or 2-5 situational prompt content categories, and to input type-restricted/size-restricted additional situational prompt content, instructional prompt content, or both (e.g., by presenting 2, 3, 4, or 5 fields, where a user inputs select discrete content such as a person's name, associated organization, etc.). In aspects, systems do not provide for/methods do not comprise input of situational prompt input”. Note that instructional input, situational inputs to the machine learning model are automatically processed by the ML model to generate draft message and content) including the road surface data to a machine-learned content generation model (See Ryu: Figs. 3-5, and [0050], “The information learning step S330 may learn the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning, and recognize road risk information on the basis of a result of the learning”. Note that the road image is input to the ML model, not the prompt which will be addressed in a secondary art) to generate, by the content generation model, content to be presented to the user passing through the road surface (See Ryu: Figs. 3-5, and [0052], “The information transmission step S350 may transmit the detected road surface defects to the user device”).
Claims 2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ryu (US 20190188501 A1) in view of Luzhnica, etc. (US 11516158 B1), further in view of Wang, etc. (US 20200217677 A1),
Regarding claim 2, Ryu and Luzhnica teach all the features with respect to claim 1 as outlined above. However, Ryu, modified by Luzhnica, fails to explicitly disclose that the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: acquire the road surface data and at least one of map data and health-related data of the user, and generate content based on the road surface data and at least one of the map data and the health-related data of the user.
However, Wang teaches that the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: acquire the road surface data and at least one of map data and health-related data of the user (See Wang: Fig. 2, and [0035], “Referring to FIG. 2, in an embodiment of the present invention, a user utilizes a client (e.g., IoT enabled device or from mobile phone) to request navigational assistance to a destination (210). Program code executing on one or more processors determines a current location of the client (220). The program code obtains the request and obtains data relevant to routing (e.g., personal health data and environmental data) (230). The relevant data obtained by the program code to utilize in analyses to determine an optimal route includes, but is not limited to, traffic details, IoT enabled device data, personality insights, data maps, and/or route maps. Program code cognitively analyzes this data to provide a route to the user, via the client (240). The route provided to the user by the program code is an optimal route having minimal health-related obstacles”), and
generate content based on the road surface data and at least one of the map data and the health-related data of the user (See Wang: Fig. 2, and [0035], “Referring to FIG. 2, in an embodiment of the present invention, a user utilizes a client (e.g., IoT enabled device or from mobile phone) to request navigational assistance to a destination (210). Program code executing on one or more processors determines a current location of the client (220). The program code obtains the request and obtains data relevant to routing (e.g., personal health data and environmental data) (230). The relevant data obtained by the program code to utilize in analyses to determine an optimal route includes, but is not limited to, traffic details, IoT enabled device data, personality insights, data maps, and/or route maps. Program code cognitively analyzes this data to provide a route to the user, via the client (240). The route provided to the user by the program code is an optimal route having minimal health-related obstacles”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Ryu to have the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: acquire the road surface data and at least one of map data and health-related data of the user, and generate content based on the road surface data and at least one of the map data and the health-related data of the user as taught by Wang in order to provide route guidance to a user through a navigational device with a personal computing device with navigational capabilities, that is efficient, from a processing perspective, and personalized, based on environmental factors, as anticipated to be experienced by the driver (See Wang: Fig. 1, and [0023], “From a user-standpoint, embodiments of the present invention provide various advantages over existing navigational systems. As will be illustrated herein, in embodiments of the present invention, the program code optimizes existing smart route selection mechanisms to provide a health-based system for selection of routes for polluted areas, while personalizing the health-based selection to the user based on factors including, but not limited to pattern, nature, and personal characteristics of the user and, optionally a relevant set of individuals. Additionally, the program code provides optimal routes to destinations in case of hazardous environmental conditions, including but not limited to, smog and other environmental conditions to which a user may be sensitive to (e.g., a user with asthma may seek to avoid certain pollutants en route to a destination). Also, embodiments of the present invention utilize temporal conditions, including but not limited to, time, situation, location, and other environmental and user-specific parameters, to determine and manage a route traversed by a user to a desired destination”). Ryu teaches a method and system that may provide an artificial intelligence system for providing road risk information based on the road surface information and using the ML model to generate content for the user; while Wang teaches a system and method that may provide a user with route guidance based on the road condition, environment factors, and personal health conditions to avoid polluted routes. Therefore, it is obvious to one of ordinary skill in the art to modify Ryu by Wang to generate content using ML model based on the road surface data, map information, and personal health data. The motivation to modify Ryu by Wang is “Use of known technique to improve similar devices (methods, or products) in the same way”.
Regarding claim 4, Ryu, Luzhnica, and Wang teach all the features with respect to claim 2 as outlined above. Further, Wang teaches that the content generation device according to claim 2, wherein
the one or more processors are further configured to execute the instruction to: input a prompt including attribute information about an on-map object included in the map data to the content generation model to generate content by the content generation model (See Wang: Figs. 1-3, and [0023], “From a user-standpoint, embodiments of the present invention provide various advantages over existing navigational systems. As will be illustrated herein, in embodiments of the present invention, the program code optimizes existing smart route selection mechanisms to provide a health-based system for selection of routes for polluted areas, while personalizing the health-based selection to the user based on factors including, but not limited to pattern, nature, and personal characteristics of the user and, optionally a relevant set of individuals. Additionally, the program code provides optimal routes to destinations in case of hazardous environmental conditions, including but not limited to, smog and other environmental conditions to which a user may be sensitive to (e.g., a user with asthma may seek to avoid certain pollutants en route to a destination). Also, embodiments of the present invention utilize temporal conditions, including but not limited to, time, situation, location, and other environmental and user-specific parameters, to determine and manage a route traversed by a user to a desired destination”; and [0035], “Referring to FIG. 2, in an embodiment of the present invention, a user utilizes a client (e.g., IoT enabled device or from mobile phone) to request navigational assistance to a destination (210). Program code executing on one or more processors determines a current location of the client (220). The program code obtains the request and obtains data relevant to routing (e.g., personal health data and environmental data) (230). The relevant data obtained by the program code to utilize in analyses to determine an optimal route includes, but is not limited to, traffic details, IoT enabled device data, personality insights, data maps, and/or route maps. Program code cognitively analyzes this data to provide a route to the user, via the client (240). The route provided to the user by the program code is an optimal route having minimal health-related obstacles”. Note that the smog, pollutants en route is mapped to the object on-map, and the generated best route to avoid the polluted environment is mapped to the content generated).
Regarding claim 5, Ryu and Luzhnica teach all the features with respect to claim 3 as outlined above. Further, Wang teaches that the content generation device according to claim 3, wherein the one or more processors are further configured to execute the instructions to:
generate a recommended route for the user based on an analysis result of the road surface data (See Wang: Fig. 3, and [0037], “In embodiments of the present invention, program code comprising a cognitive engine 300 obtains and analyzes data in order to recommend an initial route and progressively update the route, as dictated by the analysis of both environmental and personal factors. In some embodiments of the present invention, the cognitive engine obtains the IoT data 316, via an IoT gateway 315. In some embodiments of the present invention, program code comprising an in-scope area detector can locate computing devices, including IoT devices, within the defined bounded area, that can provide environmental information, and computing devices, including IoT devices, proximate to the user and/or registered to the user, which can provide personal (e.g., health-related) information about the user. Before obtaining data from a user, the user provides permission to the program code to communicate with the computing devices and to the data. Permissions may be provided in a granular level such that the user is cognizant of the access he or she enables”); and
input the analysis result of the road surface data and the recommended route to the content generation model to generate the content by the content generation model (See Wang: Fig. 3, and [0002], “For many travelers, a best route from a starting point to a destination may not necessarily be the fastest route, but, unfortunately, many existing navigational systems consider “fastest” as synonymous with “best.” In addition to reaching a given destination within a minimal amount of time, users utilizing navigational systems for driving, walking, and/or otherwise navigating to a destination, may have personal traits that render a given route better. Additionally, environmental factors that the user could encounter en route could also establish one route as superior to another. One example of an environmental concern that could impact a user experience on a routes (therefore rendering some routes superior to others, for that user) is a prevalence of smog, or other air pollutants. This type of pollution is arguably unhealthy for all users to traverse, but it is particularly problematic for users with various health concerns to navigate routes where they would encounter air pollution”; and [0035], “Referring to FIG. 2, in an embodiment of the present invention, a user utilizes a client (e.g., IoT enabled device or from mobile phone) to request navigational assistance to a destination (210). Program code executing on one or more processors determines a current location of the client (220). The program code obtains the request and obtains data relevant to routing (e.g., personal health data and environmental data) (230). The relevant data obtained by the program code to utilize in analyses to determine an optimal route includes, but is not limited to, traffic details, IoT enabled device data, personality insights, data maps, and/or route maps. Program code cognitively analyzes this data to provide a route to the user, via the client (240). The route provided to the user by the program code is an optimal route having minimal health-related obstacles”. Note that the optimal route is mapped to the final generated content).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ryu (US 20190188501 A1) in view of Luzhnica, etc. (US 11516158 B1), further in view of Wang, etc. (US 20200217677 A1), Gutman (US 20120173135 A1) and DeLuca, etc. (US 20190178671 A1).
Regarding claim 6, Ryu and Luzhnica teach all the features with respect to claim 5 as outlined above. However, Ryu, modified by Luzhnica and Wang, fails to explicitly disclose that the content generation device according to claim 5, wherein the one or more processors are configured to further execute the instruction to: generate the recommended route to the user by mathematical optimization calculation using a constraint condition on the analysis result of the road surface data.
However, Gutman teaches that the content generation device according to claim 5, wherein the one or more processors are configured to further execute the instruction to: generate the recommended route to the user by mathematical optimization calculation using a constraint condition on the analysis result of the road surface data (See Gutman: Fig. 12, and [0388], “Referring now to FIG. 12, therein is shown a flow chart of a method 1200 of operation of the navigation system 100 with constrained resource route planning optimizer in a further embodiment of the present invention. The method 1200 includes setting a predetermined arrival level for arriving at a replenishment location in a block 1202; calculating an estimated arrival level for arriving at a replenishment location in a block 1204; generating a target location based on the estimated arrival level meeting or exceeding the predetermined arrival level in a block 1206; and generating a travel route to a destination based on selecting the replenishment location from the target location for displaying on a device in a block 1208”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Ryu to have the content generation device according to claim 5, wherein the one or more processors are configured to further execute the instruction to: generate the recommended route to the user by mathematical optimization calculation using a constraint condition on the analysis result of the road surface data as taught by Gutman in order to reduce error for identifying the replenishment locations, so that the vehicle can safely reach the destination (See Gutman: Fig. 2, and [0387], “It has been discovered that the present invention provides the navigation system 100 to identify the replenishment location 210 accurately and generate the travel route 214 efficiently for safer operation of the vehicle, the navigation system 100, and other user interface system within the vehicle. The accuracy is provided by identifying the replenishment location 210 by searching not only from the start location 204 to the destination 206, but also from the destination 206 to the start location 204. The bi-directional approach can reduce error for identifying the replenishment location 210 that the vehicle can safely reach. Subsequently, the navigation system 100 can generate the travel route 214 that can aid the vehicle to safely reach the destination 206 via the replenishment location 210 most suitable for the vehicle for replenishment”). Ryu teaches a method and system that may provide an artificial intelligence system for providing road risk information based on the road surface information and using the ML model to generate content for the user; while Gutman teaches a system and method that may generate the best route to the destination using the route planning optimizer under the constrained resource conditions. Therefore, it is obvious to one of ordinary skill in the art to modify Ryu by Gutman to generate content based on the best route determined by the route planning optimizer under the constrained resource conditions. The motivation to modify Ryu by Gutman is “Use of known technique to improve similar devices (methods, or products) in the same way”.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ryu (US 20190188501 A1) in view of Luzhnica, etc. (US 11516158 B1), further in view of Wang, etc. (US 20200217677 A1), Gutman (US 20120173135 A1), and DeLuca, etc. (US 20190178671 A1).
Regarding claim 7, Ryu, Luzhnica, Wang, and Gutman teach all the features with respect to claim 6 as outlined above. However, Ryu, modified by Luzhnica, Wang and Gutman, fails to explicitly disclose that the content generation device according to claim 6, wherein the one or more processors are configured to further execute the instruction to: input feedback information about the recommended route from a user to a route generation model to generate, by the route generation model, a route different from the recommended route.
However, DeLuca teaches that the content generation device according to claim 6, wherein the one or more processors are configured to further execute the instruction to: input feedback information about the recommended route from a user to a route generation model to generate, by the route generation model, a route different from the recommended route (See DeLuca: Fig. 1, and [0003], “However, understanding directions from a navigation system in an unfamiliar and/or heavily trafficked area can be difficult for a vehicle operator using the navigation system. When the vehicle operator fails to initially understand directions from the navigation system, the vehicle operator may make navigational errors, for which the navigational system must then compensate by calculating a new navigational route based on the now erroneous position of the vehicle. Misunderstood directions can make current navigation systems operate inefficiently and/or can make confused vehicle operators a danger to themselves and to others”; [0074], “Referring now to FIG. 5, example implementation 500 according to illustrative embodiments is shown. As shown on map 520, user 220 is driving a car down North Street, when system 200 outputs navigation instruction 502 to user 220, instructing user 220 to, “Turn right on Main Street” at location A. As user 220 continues along North Street to location B, feedback analyzer 204 determines that GPS location 242 associated with user 220 is not following route (1) of navigation directions 244 from navigation system 240. In response to user action 228 indicating that user 220 failed to carry out navigation instruction 502, feedback analyzer formulates inquiry 504 to prompt user 220 to tell system 200 why user 220 failed to understand navigation instruction 502: “You missed the turn. What happened?.””; and [0077], “Based on these updated profiles, instruction synthesizer 210 can select a navigation instruction delivery technique that references lighted objects in the vicinity of user 220. Accordingly, instruction synthesizer 210 can generate navigation instruction 512, referring to the brightly lit stoplight ahead at location C, and guide user 220 to alternative route (2)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Ryu to have the content generation device according to claim 6, wherein the one or more processors are configured to further execute the instruction to: input feedback information about the recommended route from a user to a route generation model to generate, by the route generation model, a route different from the recommended route as taught by DeLuca in order to provide navigation instructions to a vehicle operator that are optimized to prevent confusion (See DeLuca: Fig. 1, and [0005], “Therefore, there is a need for a navigation guidance system that can provide navigation instructions to a vehicle operator that are optimized to prevent confusing the vehicle operator based on both the vehicle operator and the surrounding environment in which he or she is operating the vehicle”). Ryu teaches a method and system that may provide an artificial intelligence system for providing road risk information based on the road surface information and using the ML model to generate content for the user; while DeLuca teaches a system and method that may generate the alternative routes based on the road surface data, user personal data and the user feedback to avoid confusion. Therefore, it is obvious to one of ordinary skill in the art to modify Ryu by DeLuca to generate content based on road surface data, and the user feedback data, etc. The motivation to modify Ryu by DeLuca is “Use of known technique to improve similar devices (methods, or products) in the same way”.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ryu (US 20190188501 A1) in view of Luzhnica, etc. (US 11516158 B1), further in view of Wang, etc. (US 20200217677 A1), Gutman (US 20120173135 A1), DeLuca, etc. (US 20190178671 A1) and Chen, etc. (US 20240005132 A1).
Regarding claim 8, Ryu and Luzhnica teach all the features with respect to claim 1 as outlined above. However, Ryu, modified by Luzhnica, Wang and Gutman, fails to explicitly disclose that the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: determine whether the content satisfies a predetermined condition using a determination model that machine learned a relationship between content and whether to output the content; and output the content to a user terminal in a case where the determination unit determines that the content is allowed to be output.
However, Chen teaches that the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: determine whether the content satisfies a predetermined condition using a determination model that machine learned a relationship between content and whether to output the content (See Chen: Fig.1, and [0047], “Typically, the online system 110 presents a content item responsive to receiving a request from a client device 116 of a user (e.g., when the user performs a search query on the ordering interface). Thus, the online system 110 may be required to identify candidate content items and select a content item for presentation within a very short amount of time (e.g., milliseconds). The prediction system 130 may deploy the machine-learned prediction model to generate lift predictions for users and items as requests from client devices 116 are received such that, for example, content items with a threshold level of lift predictions are selected for presentation”; and Fig. 7, and [0072], “In particular, given a user and a content item promoting a respective item, the lift prediction module 470 may obtain a set of features for the user and the item. When the first embodiment of the lift prediction model (described in FIGS. 2 and 5) is used, the set of features may include a first set of user features, a second set of item features, and a third set of user and item cross features. The lift prediction module 470 may apply the first embodiment of the lift prediction model to the set of features to generate the lift prediction as described in conjunction with FIG. 2. When the second embodiment of the lift prediction model (described in FIGS. 3 and 6) is used, the set of features may include a first set of user features and a second set of item features. The lift prediction module 470 may apply the second embodiment of the lift prediction model to the set of features to generate the lift prediction as described in conjunction with FIG. 3. The lift predictions may be provided back to the module of the online system 110 that initiated the requests, such that the online system 110 may provide content items for display to a user based on the generated lift predictions. In one instance, the content items that are associated with lift predictions equal to or above a predetermined threshold are presented to users. Alternatively, the content items that are associated with lift predictions less than a predetermined threshold are not presented to users. However, this is one example, and it is appreciated that the generated lift predictions can be used in any way to select content items for presentation to users. For example, the lift predictions can be fed into other models or downstream tasks as an input feature. For example, the lift predictions can be fed into another model that predicts likelihoods of users performing desired actions on content items as an important input feature”. Note that he lift prediction model is mapped to the determination model); and
output the content to a user terminal in a case where the determination unit determines that the content is allowed to be output (See Chen: Figs.1-5, and [0072], “In particular, given a user and a content item promoting a respective item, the lift prediction module 470 may obtain a set of features for the user and the item. When the first embodiment of the lift prediction model (described in FIGS. 2 and 5) is used, the set of features may include a first set of user features, a second set of item features, and a third set of user and item cross features. The lift prediction module 470 may apply the first embodiment of the lift prediction model to the set of features to generate the lift prediction as described in conjunction with FIG. 2. When the second embodiment of the lift prediction model (described in FIGS. 3 and 6) is used, the set of features may include a first set of user features and a second set of item features. The lift prediction module 470 may apply the second embodiment of the lift prediction model to the set of features to generate the lift prediction as described in conjunction with FIG. 3. The lift predictions may be provided back to the module of the online system 110 that initiated the requests, such that the online system 110 may provide content items for display to a user based on the generated lift predictions. In one instance, the content items that are associated with lift predictions equal to or above a predetermined threshold are presented to users. Alternatively, the content items that are associated with lift predictions less than a predetermined threshold are not presented to users. However, this is one example, and it is appreciated that the generated lift predictions can be used in any way to select content items for presentation to users. For example, the lift predictions can be fed into other models or downstream tasks as an input feature. For example, the lift predictions can be fed into another model that predicts likelihoods of users performing desired actions on content items as an important input feature”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Ryu to have the content generation device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: determine whether the content satisfies a predetermined condition using a determination model that machine learned a relationship between content and whether to output the content; and output the content to a user terminal in a case where the determination unit determines that the content is allowed to be output as taught by Chen in order to predict incremental lift for users and items in addition to predictions of other metrics. (See Chen: Fig. 1, and [0030], “It is advantageous to predict incremental lift for users and items in addition to predictions of other metrics. However, this is inherently a difficult problem because a user cannot be presented and not presented with a content item at the same time. If accurate lift predictions can be generated, the online system 110 can evaluate performance of content items more effectively and use the lift predictions to target users that are likely to be affected by presentation of content items relative to users that are not. However, conventional models for predicting lift have limited capacity to process large amounts of data. Moreover, conventional models for predicting lift involve generating a training dataset that divides a group of users into a treatment group and a control group such that only the treatment group is presented with a content item. Since the number of users in the control group are typically significantly different (e.g., smaller or larger) than the number of users in the treatment group, this can lead to inaccurate and biased results”). Ryu teaches a method and system that may provide an artificial intelligence system for providing road risk information based on the road surface information and using the ML model to generate content for the user; while Chen teaches a system and method that may train a machine-learned lift prediction model to determine lift predictions for users and items associated with the online system and present only those content that has the prediction lift greater than some predetermined threshold. Therefore, it is obvious to one of ordinary skill in the art to modify Ryu by Chen to evaluate the generated content and present only those content with some lift greater than a predetermined threshold. The motivation to modify Ryu by Chen is “Use of known technique to improve similar devices (methods, or products) in the same way”.
Conclusion
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/GORDON G LIU/Primary Examiner, Art Unit 2618
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