DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. Claims 2-14, 16-18 and 20 are objected to because of the following informalities:
In claims 2-14, “A method” in line 1 should read “The method”.
Appropriate correction is required.
In claims 16-18, “A method” in line 1 should read “The method”.
Appropriate correction is required.
In claim 20, “A method” in line 1 should read “The method”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 9-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 9 recites the limitations "the second plurality of available data” and “the first plurality of available data". There is insufficient antecedent basis for these limitations in the claim.
Claim 15 recites the limitation "the same instance” in line 3. There is insufficient antecedent basis for this limitation in the claim.
The remaining claims are rejected for fully incorporating the deficiencies of the base claim(s) from which they depend.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a series of steps and therefore is a process. As such, claim 1 is one of the statutory categories. The claim recites “in an operational mode providing to the first correlation engine a plurality of available data relating to a first new user, the first correlation engine providing a first output estimated stance for the first query for the first new user” which is merely a concept can be performed in the human mind because it describes a mental process of data analysis and statistical estimation. Human mind through observation and evaluation can perform the claim steps. These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components such as the first correlation engine. Thus, the claim recites a mental process and is directed to a judicial exception.
This judicial exception is not integrated into a practical application because the claim recites the additional elements: “providing a first correlation engine; in a training mode of operation, providing to the first correlation engine for each of a first plurality of first users, a plurality of first available data about each first user of the first plurality of first users and a first stance of each first user relating to a first query”, which indicates mere data gathering. As such, these limitations amount to mere data gathering in conjunction with a law of nature or abstract idea that the courts have found to be insignificant extra-solution activity. See MPEP 2106.05(g). Use of a computer or other machinery in its ordinary capacity for economic or other or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). See MPEP 2106.05(f). The addition of insignificant extra-solution activity does not amount to an inventive concept. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP 2016.05(h).
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Furthermore, the independent claim recites the steps of gathering user data, comparing it, and estimating a person’s stance. The courts have indicated it may not be sufficient to show an improvement to technology include: Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. Thus, claim 1 is directed to the abstract idea.
As such, independent claims 15 and 19 are also directed to the abstract idea for the same reason as claim 1 above because claims 15 and 19 also recite similar steps as claim 1.
Regarding claims 3-8, 10-14 and 16-18, claims further recite the limitations which further recite mental processes and are directed to perform mental processes that fall into the “Mental Processes” groupings of abstract ideas and are directed to a judicial exception. Furthermore, claims include no additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Thus, the claims are ineligible.
Regarding claims 2, 9 and 20, claims further recite the limitations which indicate mere data gathering activity that the courts have found to be insignificant extra-solution activity. The addition of insignificant extra-solution activity does not amount to an inventive concept. Furthermore, claims include no additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Thus, the claims are ineligible.
Claim Rejections - 35 USC § 102
5. 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Qiao et al. (US 2023/0259815 A1) hereinafter Qiao.
As to claim 1, Qiao discloses a method comprising: providing a first correlation engine; in a training mode of operation (Para. 52, “the machine learning models implemented by the evaluation apparatus 170 can be trained on a training dataset that can include information about the respective user groups of the users and user-specific information. This allows the machine learning models to learn the complex relationships between the different user-specific information (attributes) and the corresponding user groups.”.), providing to the first correlation engine for each of a first plurality of first users, a plurality of first available data about each first user of the first plurality of first users and a first stance of each first user relating to a first query (Fig. 3, Para. 53, “the first set of data for the user groups can include user group attributes such as the number of users in the user groups, the geographical locations of the client devices 106 of the users of the user groups, a set (or a list of URLs) of digital contents that are commonly accessed by the users of the user groups etc. The first set of data can further include the distribution of users based on certain user characteristics such as gender, income group, educational qualification that may have been obtained or predicted by other machine learning models implemented by the content distribution system 110.”. Para. 56, the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination, i.e., a first stance of each first user relating to a first query. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest, i.e., a plurality of first available data about each first user of the first plurality of first users. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals. Thus, in a training mode of operation, providing to the first correlation engine for each of a first plurality of first users, a plurality of first available data about each first user of the first plurality of first users and a first stance of each first user relating to a first query.); and
in an operational mode providing to the first correlation engine a plurality of available data relating to a first new user (Fig. 3, Para. 59, “the evaluation apparatus 170 can implement two machine learning models where the first machine learning model is based solely on a training dataset that includes the first set of data that is related to the multiple user groups. This enables the first machine learning model to predict labels that generalize the selection of digital components across all users of each user group. On the flip side, the second machine learning model can be trained on a training dataset that includes the first set of data that is related to the multiple user groups and the second set of data that includes user-specific data thereby enabling the second machine learning models to predict labels that are specific to each of the users of the user groups. The predicted user specific labels can then be used to select digital components that can provide a better user-experience.”. Para. 85, “The digital component distribution system 110 predicts user interest of the user of client device 106 (440). For example, after selecting the particular task-specific machine learning model, the digital component distribution system 110 can provide as input to the selected task-specific machine learning model, the first set of data of the user group to which the client device 106 of the user is a member and/or the user's online activity. The selected task-specific machine learning model processes the input and generates as output a label indicating user interest or classifies the user into one of the predetermined classes that also indicates user interest.”. Thus, in an operational mode providing to the first correlation engine a plurality of available data relating to a first new user.), the first correlation engine providing a first output estimated stance for the first query for the first new user (Para. 30, “The event data can also include a search query that was submitted from the client device 106 to obtain a search results page and/or data specifying search results and/or textual, audible, or other visual content that is included in the search results.”. Thus, the first correlation engine providing a first output estimated stance for the first query for the first new user.).
As to claim 2, the claim is rejected for the same reasons as claim 1 above. In addition, Qiao discloses wherein the plurality of first available data comprises social media feed data (Para. 22, “These techniques can include assigning the users to user groups when the users visit particular resources or perform particular actions at the resource (e.g., interact with a particular item presented on a web page or add the item to a virtual cart). These user groups are generally created in a manner such that each user group includes a sufficient number of users, such that no individual user can be identified.”. Para. 42, distribution parameters for digital component distribution may include user-specific information such as demographic information, user interests, and/or other information that can be used to personalize the user's online experience. In some situations, these characteristics and/or information regarding the user of the client device 106 is readily available. For example, content platforms, i.e., social media, such as the content server 104 or the search system 150 may allow the user to register with the content platform by providing such user-specific information. In another example, the content platform can use cookies to identify client devices, which can store information about the user's online activity and/or user-specific information. Thus, the plurality of first available data comprises social media feed data.).
As to claim 3, the claim is rejected for the same reasons as claim 2 above. In addition, Qiao discloses wherein the first stance of each first user is automatically detected (Para. 68, “The evaluation apparatus 170 identifies a first set of data for the user group that includes attributes of the user group (310). For example, the evaluation apparatus 170 can identify the multiple user groups maintained and/or managed by the digital component distribution system 110 and a first set of data regarding each of the user groups among the multiple user groups. The first set of data for the user groups can include user group attributes such as the number of users in the user groups, the geographical locations of the client devices 106 of the users of the user groups, a set (or a list of URLs) of digital contents that are commonly accessed by the users of the user groups etc. The first set of data can further include the distribution of users based on certain user characteristics such as gender, income group, educational qualification that may have been obtained or predicted by other machine learning models implemented by the digital component distribution system 110.”. Thus, the first stance of each first user is automatically detected.).
As to claim 4, the claim is rejected for the same reasons as claim 3 above. In addition, Qiao discloses wherein the first stance of some first users is automatically detected by correlating user statements on social media with statements made by individuals having a known stance (Para. 43, “the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes”. Para. 56, “assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”. Thus, the first stance of some first users is automatically detected by correlating user statements on social media with statements made by individuals having a known stance.).
As to claim 5, the claim is rejected for the same reasons as claim 3 above. In addition, Qiao discloses wherein the first stance of some first users is automatically detected by correlating information shared on social media with information shared by individuals having a known stance (Para. 43, “the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes”. Para. 56, “assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”. Thus, the first stance of some first users is automatically detected by correlating information shared on social media with information shared by individuals having a known stance.).
As to claim 6, the claim is rejected for the same reasons as claim 5 above. In addition, Qiao discloses comprising: determining a first group of users of social media having a known stance for the first query; and determining a second group of users of the social media sharing common elements of social media feed data with the determined first group, wherein the common elements are indicative of sharing a same stance for the first query as the first group, the same stance forming the first stance (Para. 43, the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes, i.e., the common elements are indicative of sharing a same stance for the first query as the first group, the same stance forming the first stance. Para. 56, “assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”.).
As to claim 7, the claim is rejected for the same reasons as claim 1 above. In addition, Qiao discloses wherein the first stance is determined by providing each user of the first plurality of first users a survey question and wherein the first stance is one of an answer to the survey question and a stance derived from the answer to the survey question (Para. 25, “Client devices 106, and in particular personal digital assistants, can include hardware and/or software that enable voice interaction with the client devices 106. For example, the client devices 106 can include a microphone through which users can submit audio (e.g., voice) input, such as commands, search queries, browsing instructions, smart home instructions, and/or other information.”. Para. 100, “To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.”, where feedback indicates a survey question. Thus, the first stance is determined by providing each user of the first plurality of first users a survey question and wherein the first stance is one of an answer to the survey question and a stance derived from the answer to the survey question.).
As to claim 8, the claim is rejected for the same reasons as claim 1 above. In addition, Qiao discloses providing a second correlation engine; in a training mode of operation (Para. 52, “the machine learning models implemented by the evaluation apparatus 170 can be trained on a training dataset that can include information about the respective user groups of the users and user-specific information. This allows the machine learning models to learn the complex relationships between the different user-specific information (attributes) and the corresponding user groups.”.), providing to the second correlation engine for each of a second plurality of second users, a plurality of first available data about each second user of the second plurality of second users and a second stance of each second user relating to a second query, the second stance different from the first stance of the first plurality of first users (Fig. 3, Para. 53, “the first set of data for the user groups can include user group attributes such as the number of users in the user groups, the geographical locations of the client devices 106 of the users of the user groups, a set (or a list of URLs) of digital contents that are commonly accessed by the users of the user groups etc. The first set of data can further include the distribution of users based on certain user characteristics such as gender, income group, educational qualification that may have been obtained or predicted by other machine learning models implemented by the content distribution system 110.”. Para. 56, the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination, i.e., a first stance of each first user relating to a first query. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest, i.e., a plurality of first available data about each first user of the first plurality of first users. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals. Thus, in a training mode of operation, providing to the second correlation engine for each of a second plurality of second users, a plurality of first available data about each second user of the second plurality of second users and a second stance of each second user relating to a second query, the second stance different from the first stance of the first plurality of first users.); and
in an operational mode providing to the second correlation engine a plurality of available data relating to a second new user (Fig. 3, Para. 59, “the evaluation apparatus 170 can implement two machine learning models where the first machine learning model is based solely on a training dataset that includes the first set of data that is related to the multiple user groups. This enables the first machine learning model to predict labels that generalize the selection of digital components across all users of each user group. On the flip side, the second machine learning model can be trained on a training dataset that includes the first set of data that is related to the multiple user groups and the second set of data that includes user-specific data thereby enabling the second machine learning models to predict labels that are specific to each of the users of the user groups. The predicted user specific labels can then be used to select digital components that can provide a better user-experience.”. Para. 85, “The digital component distribution system 110 predicts user interest of the user of client device 106 (440). For example, after selecting the particular task-specific machine learning model, the digital component distribution system 110 can provide as input to the selected task-specific machine learning model, the first set of data of the user group to which the client device 106 of the user is a member and/or the user's online activity. The selected task-specific machine learning model processes the input and generates as output a label indicating user interest or classifies the user into one of the predetermined classes that also indicates user interest.”. Thus, in an operational mode providing to the second correlation engine a plurality of available data relating to a second new user.), the second correlation engine providing a second output estimated response to the second query for the second new user (Para. 30, “The event data can also include a search query that was submitted from the client device 106 to obtain a search results page and/or data specifying search results and/or textual, audible, or other visual content that is included in the search results.”. Thus, the second correlation engine providing a second output estimated response to the second query for the second new user.).
As to claim 9, the claim is rejected for the same reasons as claim 8 above. In addition, Qiao discloses wherein the second plurality of available data and the first plurality of available data comprise data from a same data store (Para. 43, “the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes.”. Thus, the second plurality of available data and the first plurality of available data comprise data from a same data store.).
As to claim 10, the claim is rejected for the same reasons as claim 9 above. In addition, Qiao discloses wherein the first stance is approximately converse to the second stance (Para. 45, “if the user starts the browsing session by searching for shoes and visiting multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which includes all users who have visited websites related to shoes. Assume that there are 100 users who have previously visited websites related to shoes. When the user is assigned to the user group "shoe", the total number of users included in the user group increases to 101. However after some time if the user searches for hotels and visits multiple webpages of different hotels or travel agencies, the user can be removed from the previously assigned user group "shoe" and reassigned to a different user group "hotel" or "travel". In such a case, the number of users in the user group "shoe", reduces back to 100 given if no other user was added or removed from the particular user group.”. Thus, the first stance is approximately converse to the second stance.).
As to claim 11, the claim is rejected for the same reasons as claim 10 above. In addition, Qiao discloses wherein the second stance is automatically determined for each of the second plurality of second users (Para. 45, “if the user starts the browsing session by searching for shoes and visiting multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which includes all users who have visited websites related to shoes. Assume that there are 100 users who have previously visited websites related to shoes. When the user is assigned to the user group "shoe", the total number of users included in the user group increases to 101. However after some time if the user searches for hotels and visits multiple webpages of different hotels or travel agencies, the user can be removed from the previously assigned user group "shoe" and reassigned to a different user group "hotel" or "travel". In such a case, the number of users in the user group "shoe", reduces back to 100 given if no other user was added or removed from the particular user group.”. Thus, the second stance is automatically determined for each of the second plurality of second users.).
As to claim 12, the claim is rejected for the same reasons as claim 11 above. In addition, Qiao discloses in an operational mode providing to the second correlation engine available data relating to the first new user, the second correlation engine providing a second output estimated response to the second query for the first new user (Fig. 3, Para. 43, “the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes.”. Para. 85, “The digital component distribution system 110 predicts user interest of the user of client device 106 (440). For example, after selecting the particular task-specific machine learning model, the digital component distribution system 110 can provide as input to the selected task-specific machine learning model, the first set of data of the user group to which the client device 106 of the user is a member and/or the user's online activity. The selected task-specific machine learning model processes the input and generates as output a label indicating user interest or classifies the user into one of the predetermined classes that also indicates user interest.”. Thus, in an operational mode providing to the second correlation engine available data relating to the first new user, the second correlation engine providing a second output estimated response to the second query for the first new user.).
As to claim 13, the claim is rejected for the same reasons as claim 12 above. In addition, Qiao discloses comparing the first output estimated response to the first query for the first new user and the second output estimated response to the first query for the first new user to provide a comparison result (Para. 63, “The machine learning model 200 in order to learn the relationship between the true classes and each of the corresponding input training samples, compares the predicted classes with the label of the corresponding training sample of the training dataset using a loss function.”. Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”. Para. 45, “if the user starts the browsing session by searching for shoes and visiting multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which includes all users who have visited websites related to shoes. Assume that there are 100 users who have previously visited websites related to shoes. When the user is assigned to the user group "shoe", the total number of users included in the user group increases to 101. However after some time if the user searches for hotels and visits multiple webpages of different hotels or travel agencies, the user can be removed from the previously assigned user group "shoe" and reassigned to a different user group "hotel" or "travel". In such a case, the number of users in the user group "shoe", reduces back to 100 given if no other user was added or removed from the particular user group.”. Thus, comparing the first output estimated response to the first query for the first new user and the second output estimated response to the first query for the first new user to provide a comparison result.).
As to claim 14, the claim is rejected for the same reasons as claim 13 above. In addition, Qiao discloses determining a reliability of first output estimated response to the first query for the first new user and the second output estimated response to the first query for the first new user in dependence upon the comparison result (Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”. Para. 45, “if the user starts the browsing session by searching for shoes and visiting multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which includes all users who have visited websites related to shoes. Assume that there are 100 users who have previously visited websites related to shoes. When the user is assigned to the user group "shoe", the total number of users included in the user group increases to 101. However after some time if the user searches for hotels and visits multiple webpages of different hotels or travel agencies, the user can be removed from the previously assigned user group "shoe" and reassigned to a different user group "hotel" or "travel". In such a case, the number of users in the user group "shoe", reduces back to 100 given if no other user was added or removed from the particular user group.”. Thus, determining a reliability of first output estimated response to the first query for the first new user and the second output estimated response to the first query for the first new user in dependence upon the comparison result.).
As to claim 15, Qiao discloses a method comprising: automatically determining a stance for a first group of users within each of a plurality of groups of users, at least one of the same stance and a correlated stance determined for members of each first group of users (Para. 23, “the techniques described in this document can generate a training dataset by combining semantic data of the user groups and user specific data, which can be anonymous user data such that no party can identify a particular user based on the user -specific data. After generating a machine learning model, the model can be used to predict digital components without using user-specific information. Instead, the input to the machine learning model can be a user group identifier that identifies a large group of users that includes the user as a member and possibly semantic data of the user group”. Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals”. Thus, determining a stance for a first group of users within each of a plurality of groups of users, at least one of the same stance and a correlated stance determined for members of each first group of users.);
using the determined stance as correct output values for training a plurality of correlation engines, each correlation engine trained with each determined stance from a group of the plurality of groups and data available and relating to users within the group of the plurality of groups for whom a same stance is detected; for each user within a population of users, executing each of the plurality of correlation engines to determine a plurality of output values, each output value relating to a different correlation engine (Para. 83, “Depending upon the first set of data and/or the user's online activity, the digital component distribution system 110 can determine which machine learning model (or a task-specific machine learning model) to select for predicting user interest. For example, if the user's online activity is less than a pre-specified threshold, the digital component distribution system 110 can determine to use only the first set of data to predict user interest. In other words, due to the scarcity of input data at the time of prediction, the digital component distribution system 110 can determine to use a machine leaning model that generalizes over all users in the user group. On the other hand, if the user's online activity is more than the pre-specified threshold, the digital component distribution system 110 can determine to use the first set of data to predict user interest and the user's online activity to predict user interest. In such a situation due to the availability of input data, the digital component distribution system 110 can determine to use a machine learning model that predicts user interest that is more personalized for the user”. Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. Thus, using the determined stance as correct output values for training a plurality of correlation engines, each correlation engine trained with each determined stance from a group of the plurality of groups and data available and relating to users within the group of the plurality of groups for whom a same stance is detected.); and
comparing output values from the plurality of output values one against another to determine at least one of a degree to which the output values relating to a particular user and a likelihood that the output values relating to a particular user are correct (Para. 38, “The distribution parameters can also require that the component request 112 include information specifying a particular geographic region (e.g., country or state) and/or information specifying that the component request 112 originated at a particular type of client device 106 (e.g., mobile device or tablet device) in order for the component item to be eligible for presentation. The distribution parameters can also specify an eligibility value (e.g., rank, score or some other specified value) that is used for evaluating the eligibility of the component item for selection/distribution/transmission”. Para. 45, “when the users starts a web browsing session and visits particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a group of users who have visited the same website or other websites that are contextually similar or are interested in the same item. However if the user visits another website and interacts with another type of item presented on the other website, the user is assigned to another group of users who have visited the other website or other websites that are contextually similar or are interested in the other item.”. Thus, comparing output values from the plurality of output values one against another to determine at least one of a degree to which the output values relating to a particular user and a likelihood that the output values relating to a particular user are correct.).
As to claim 16, the claim is rejected for the same reasons as claim 15 above. In addition, Qiao discloses wherein the at least one of a degree and a likelihood is a largest number of output values relating to a same stance to a total number of the plurality of output values (Para. 38, “The distribution parameters can also require that the component request 112 include information specifying a particular geographic region (e.g., country or state) and/or information specifying that the component request 112 originated at a particular type of client device 106 (e.g., mobile device or tablet device) in order for the component item to be eligible for presentation. The distribution parameters can also specify an eligibility value (e.g., rank, score or some other specified value) that is used for evaluating the eligibility of the component item for selection/distribution/transmission”. Thus, the at least one of a degree and a likelihood is a largest number of output values relating to a same stance to a total number of the plurality of output values.).
As to claim 17, the claim is rejected for the same reasons as claim 15 above. In addition, Qiao discloses wherein the same stance is automatically determined (Para. 68, “The evaluation apparatus 170 identifies a first set of data for the user group that includes attributes of the user group (310). For example, the evaluation apparatus 170 can identify the multiple user groups maintained and/or managed by the digital component distribution system 110 and a first set of data regarding each of the user groups among the multiple user groups. The first set of data for the user groups can include user group attributes such as the number of users in the user groups, the geographical locations of the client devices 106 of the users of the user groups, a set (or a list of URLs) of digital contents that are commonly accessed by the users of the user groups etc. The first set of data can further include the distribution of users based on certain user characteristics such as gender, income group, educational qualification that may have been obtained or predicted by other machine learning models implemented by the digital component distribution system 110.”. Thus, the same stance is automatically determined.).
As to claim 18, the claim is rejected for the same reasons as claim 17 above. In addition, Qiao discloses wherein the same stance is automatically determined based on a correlation of social media activity of a first user compared to social media activity of a user having a known stance (Para. 43, “the users can be assigned to user interest groups based on the digital content accessed by the user during a single browsing session, or over multiple browsing sessions during a specified time period. For example, when the user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a user group for users who have visited the same website or other websites that are contextually similar, or are interested in the same item. For example, if the user of the client device 106 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group "shoe," which can include all users who have visited websites related to shoes”. Para. 56, “assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals.”. Thus, the same stance is automatically determined based on a correlation of social media activity of a first user compared to social media activity of a user having a known stance.).
As to claim 19, Qiao discloses a method comprising: providing a first correlation engine; automatically determining a first stance of a first user in dependence upon users having a high likelihood of having a known stance (Para. 23, “the techniques described in this document can generate a training dataset by combining semantic data of the user groups and user specific data, which can be anonymous user data such that no party can identify a particular user based on the user -specific data. After generating a machine learning model, the model can be used to predict digital components without using user-specific information. Instead, the input to the machine learning model can be a user group identifier that identifies a large group of users that includes the user as a member and possibly semantic data of the user group”. Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. The evaluation apparatus 170 can use natural language processing (NLP) to process the digital contents accessed by the user based the user's online activity and the digital contents accessed by the user to generate labels that indicate user interest. In this example, the labels generated by the evaluation apparatus 170 can be "Destination1" indicating that the user is interested in travelling to a particular destination1. In this example, the evaluation apparatus 170 can also generate other labels that can indicate user interest in other travel destinations, hotels or car rentals”. Thus, automatically determining a first stance of a first user in dependence upon users having a high likelihood of having a known stance.);
using the first stance as known output data for training the first correlation engine; using publicly available data relating to the first user as training input data for training the first correlation engine (Para. 83, “Depending upon the first set of data and/or the user's online activity, the digital component distribution system 110 can determine which machine learning model (or a task-specific machine learning model) to select for predicting user interest. For example, if the user's online activity is less than a pre-specified threshold, the digital component distribution system 110 can determine to use only the first set of data to predict user interest. In other words, due to the scarcity of input data at the time of prediction, the digital component distribution system 110 can determine to use a machine leaning model that generalizes over all users in the user group. On the other hand, if the user's online activity is more than the pre-specified threshold, the digital component distribution system 110 can determine to use the first set of data to predict user interest and the user's online activity to predict user interest. In such a situation due to the availability of input data, the digital component distribution system 110 can determine to use a machine learning model that predicts user interest that is more personalized for the user”. Para. 56, “the evaluation apparatus 170 after obtaining the first set of data and the second set of data can perform a contextual analysis of the first set and the second set to generate one or more labels that indicates user interests. For example, assume that a user is interested in travelling and the user uses client device 106 to search and access digital content related to hotels and travel tickets to a particular destination. Further assume that based on the user activity the user is assigned to a user group "Travel" that includes multiple users that are interested in travelling. Thus, using the first stance as known output data for training the first correlation engine; using publicly available data relating to the first user as training input data for training the first correlation engine.); and
training the first correlation engine with the known output data and the training input data (Para. 52, “the machine learning models implemented by the evaluation apparatus 170 can be trained on a training dataset that can include information about the respective user groups of the users and user-specific information. This allows the machine learning models to learn the complex relationships between the different user-specific information (attributes) and the corresponding user groups”. Thus, training the first correlation engine with the known output data and the training input data.).
As to claim 20, the claim is rejected for the same reasons as claim 19 above. In addition, Qiao discloses wherein the publicly available data comprises social media feed data (Para. 22, “These techniques can include assigning the users to user groups when the users visit particular resources or perform particular actions at the resource (e.g., interact with a particular item presented on a web page or add the item to a virtual cart). These user groups are generally created in a manner such that each user group includes a sufficient number of users, such that no individual user can be identified.”. Para. 42, distribution parameters for digital component distribution may include user-specific information such as demographic information, user interests, and/or other information that can be used to personalize the user's online experience. In some situations, these characteristics and/or information regarding the user of the client device 106 is readily available. For example, content platforms, i.e., social media, such as the content server 104 or the search system 150 may allow the user to register with the content platform by providing such user-specific information. In another example, the content platform can use cookies to identify client devices, which can store information about the user's online activity and/or user-specific information. Thus, the publicly available data comprises social media feed data.).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sen et al. (US 2018/0218253 A1) teaches stance classification of multi-perspective consumer health information.
Lyle (US 2024/0378306 A1) teaches role-based large language model to enable security and accuracy.
Ahmadvand et al. (US 2022/0100806 A1) teaches responsive category prediction for user queries.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD SOLAIMAN BHUYAN whose telephone number is (571)272-7843. The examiner can normally be reached on Monday - Friday 9:00am-5:00pm EST.
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/Mohammad S Bhuyan/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
