DETAILED ACTION
Status of Claims
This communication is a non-final action on the merits in response to the amendments and arguments filed on October 1, 2025. Claims 1-20 were canceled. Claims 21-29 were added. Claims 21-29 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on October 1, 2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 21-29 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Independent Claim 21 recites “server-side training a neural network” and a “server-side networking application,” yet Applicant’s specification fails to disclose that the networking application is a “server-side” application and that the training of the neural network is “server-side.”
Independent Claim 21 also recites “the neural network model further trained to achieve a network objective of the target user account,” yet Applicant’s specification fails to disclose that the neural network is trained to achieve a network objective of a user account.
Because the original disclosure does not support the identified limitations, one of ordinary skill in the art would not recognize the Applicant as in possession of the claimed invention at the time of filing. Therefore, Claim 21 is rejected under 35 U.S.C. 112(a). Because Claims 22-29 depend upon Claim 21, these claims are also rejected under 35 U.S.C. 112(a).
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.
Step 1
Claims 21-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a computer program per se (i.e., the descriptions or expressions of the programs). Such programs are not physical “devices” or “structures,” nor are they statutory processes, as they are not “acts” being performed. The claims are directed to disembodied data structures, which are not statutory (In re Warmerdam, No. 93-1294 (Fed. Cir. August 11, 1994)).
Claims 21-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1
Independent Claim 21 recites the following abstract ideas: “generate a networking recommendation based on client-side transmitted loss, comprising: a connection identifier ; the connection identifier having access to a plurality of user accounts, each of the plurality of user accounts comprising: a user profile; a network strategy comprising at least one of current connections, candidate connections, network objectives, networking tasks, networking events, networking introductions, candidate introductions, and an ability to invite users ; and a plurality of primary connections, each primary connection of the plurality of primary connections defining a primary relationship between a given user account and a different user account of the plurality of user accounts; determine whether user accounts of the plurality of user accounts would be a good networking match by: the user profile, the network strategy, and the plurality of primary connections of a target user account of the plurality of user accounts, and generate a target output; the user profile, the network strategy, and the plurality of primary connections of a second user account of the plurality of user accounts, and generate a candidate output; determining a matching score based on comparing the target output and the candidate output; and generating a networking recommendation based on determining that the matching score satisfies a matching score threshold; achieve a network objective of the target user account based on comparing the target output to the candidate output; transmit the networking recommendation to the target user account; receivea loss indicating a value of the networking recommendation; and the loss received
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to managing relationships or interactions between people including social activities (i.e., generate a networking recommendation based on client-side transmitted loss, comprising: a connection identifier; the connection identifier having access to a plurality of user accounts, each of the plurality of user accounts comprising: a user profile; a network strategy comprising at least one of current connections, candidate connections, network objectives, networking tasks, networking events, networking introductions, candidate introductions, and an ability to invite users; and a plurality of primary connections, each primary connection of the plurality of primary connections defining a primary relationship between a given user account and a different user account of the plurality of user accounts; determine whether user accounts of the plurality of user accounts would be a good networking match by: the user profile, the network strategy, and the plurality of primary connections of a target user account of the plurality of user accounts, and generate a target output; the user profile, the network strategy, and the plurality of primary connections of a second user account of the plurality of user accounts, and generate a candidate output; determining a matching score based on comparing the target output and the candidate output; and generating a networking recommendation based on determining that the matching score satisfies a matching score threshold; achieve a network objective of the target user account based on comparing the target output to the candidate output; transmit the networking recommendation to the target user account; receive a loss indicating a value of the networking recommendation), but for the recitation of generic computer components (i.e., a server-side networking application comprising a neural network model, in communication with respective client-side user devices, applying a natural language processing (NLP) module, and generating NLP output). If a claim limitation, under its broadest reasonable interpretation, relates to managing relationships or interactions between people including social activities, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a server-side networking application comprising a neural network model, in communication with respective client-side user devices, applying a natural language processing (NLP) module, and generating NLP output. The computer hardware is recited at a high level of generality (i.e., generic computer application and user devices receiving and transmitting information, and generic NLP module and neural network trained to determine and output information) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a server-side networking application comprising a neural network model, in communication with respective client-side user devices, applying a natural language processing (NLP) module, and generating NLP output) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claims 22-29 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-27 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (US-20160283500) in view of Poslavsky (US-20210182976) and Yang et al. (US-20180107665).
Claim 21
Han teaches the following limitations:
A networking application for server-side training a neural network model to generate a networking recommendation… comprising: a server-side networking application comprising a connection identifier and a neural network model, the server-side networking application in communication with respective client-side user devices of respective users of the server-side networking application ([0011] Social network application 122 is a software application providing a platform to a user to build social networks and social relationships among people who share interests, activities, backgrounds, or real-life connections. Social network application 122 can be a web-based service that allows a user to create a public profile, create a list of other users of a social network with whom to share connections, and to interact with the other users. A social network connection is a relationship between two users of a social network system, the connection allowing the users to share ideas, interests, and other items. The user created public profile may contain profile information such as identifying information, current activities, background information, and interests. Social network application 122 is a client-side application operating on user computing device 120, and allowing a user of user computing device 120 access to other users of a social network system via network 110; [0013] recommendation module 132 determines a mining engine with which to evaluate the user… Based on the mining engine evaluation, recommendation module 132 identifies one or more social network connections to the user. In an embodiment, recommendation module 132 is a plugin or an add-on to social network application 122; [0024] The text mining engine uses the data with supervised learning methods to train a model, for example, a decision tree, a deep neural network (DNN), etc.);
the connection identifier having access to a plurality of user accounts, each of the plurality of user accounts comprising: a user profile ([0003] a method, a computer program product, and a computer system for recommending one or more connections in a social network system. In the method, a computer retrieves user profile information for a user of a social network system; [0011] Social network application 122 is a software application providing a platform to a user to build social networks and social relationships among people who share interests, activities, backgrounds, or real-life connections. Social network application 122 can be a web-based service that allows a user to create a public profile, create a list of other users of a social network with whom to share connections, and to interact with the other users. A social network connection is a relationship between two users of a social network system, the connection allowing the users to share ideas, interests, and other items. The user created public profile may contain profile information such as identifying information, current activities, background information, and interests);
a network strategy comprising at least one of current connections, candidate connections, network objectives, networking tasks, networking events, networking introductions, candidate introductions, and an ability to invite users to the server-side networking application ([0011] Social network application 122 is a software application providing a platform to a user to build social networks and social relationships among people who share interests, activities, backgrounds, or real-life connections. Social network application 122 can be a web-based service that allows a user to create a public profile, create a list of other users of a social network with whom to share connections, and to interact with the other users. A social network connection is a relationship between two users of a social network system, the connection allowing the users to share ideas, interests, and other items. The user created public profile may contain profile information such as identifying information, current activities, background information, and interests; [0016] When initialized, recommendation module 132 retrieves user profile information from the user's profile or a user's personal page. In various embodiments, user profile information may include, for example, user identifying information, such as name, location, career position, and company or business unit, user status updates, user comments, including either comments on user shared items or comments on items shared by other users, and user shared items or photos. User shared items may include, for example, news articles, blog posts, website links, restaurant or theater reviews, and other such items. User profile information may include user interests or activities, or information on the user's connections, for example, who the user is connected to and to how many other users the user is connected); and
a plurality of primary connections, each primary connection of the plurality of primary connections defining a primary relationship between a given user account and a different user account of the plurality of user accounts ([0011] Social network application 122 can be a web-based service that allows a user to create a public profile, create a list of other users of a social network with whom to share connections, and to interact with the other users. A social network connection is a relationship between two users of a social network system, the connection allowing the users to share ideas, interests, and other items; [0016] User profile information may include user interests or activities, or information on the user's connections, for example, who the user is connected to and to how many other users the user is connected);
determine whether user accounts of the plurality of user accounts would be a good networking match by: applying a… module to the user profile, the network strategy, and the plurality of primary connections of a target user account of the plurality of user accounts, and generate a target… output ([0013] Recommendation module 132 evaluates a user's profile and other social network information to determine a social maturity level, or stage, of the user, the social maturity stage indicating whether the user is a new user, for example, one with few, if any, connections, an intermediate user, for example, one with many connections but each in the same business unit or location, or an experienced user, for example, one with many connections across business unit, country, age range, etc. Based on the determined social maturity value and user stage, recommendation module 132 determines a mining engine with which to evaluate the user. Each mining engine utilized by recommendation module 132 retrieves a plurality of information, for example, a profile mining engine retrieves structured, basic, profile information of the user. Based on the mining engine evaluation, recommendation module 132 identifies one or more social network connections to the user; [0016] Recommendation module 132 retrieves user profile information (202); [0017] Recommendation module 132 determines a user stage (204). Recommendation module 132 evaluates the retrieved user profile information to determine to which stage the user belongs, where the user stage may also be referred to as the user's social maturity. In an embodiment, recommendation module 132 groups the user into one of three stages based on a number of current connections, and a location of each current connection, the location either a physical location or a business organization location, or status);
applying the… module to the user profile, the network strategy, and the plurality of primary connections of a second user account of the plurality of user accounts, and generate a candidate… output ([0022] Recommendation module 132 determines a mining engine (206), for each stage, for evaluating a user. In embodiments of the present invention, recommendation module 132 identifies a profile mining engine for users in the first stage, such profile mining engine evaluating structured user profile information from the user's profile, and determining other users with matching, or similar, profile information, for example, business unit, team, organization, etc. For users in the second stage, recommendation module 132 identifies a network mining engine, the network mining engine retrieving one or more connections with connections in common with the user, using, for example, a contact list of the user. Recommendation module 132 identifies a text mining engine for users in the third stage, which retrieves text from other users and determines other users with similar interests and activities as the user; [0024] Recommendation module 132 performs operations according to the determined mining engine (208). In various embodiments, each mining engine identified is used to extract information from the retrieved user profile information, in order to determine one or more connections for the user. In an embodiment, the profile mining engine evaluates structured user profile information from the user's profile, and determines other users with matching, or similar, profile information. The structured profile information can be stored in database 134. In an embodiment, the network mining engine retrieves potential connections via the user's contact list, using one of a plurality of network mining methods, such as collaborative filtering, to find a second user with a maximum connections in common with the user. In an embodiment, the text mining engine identified for users in the third stage retrieves and collects a corpus of data from other users of the social network system, including, for example, status updates, user comments, user shared items, and communities in which the other user may be involved);
determining a matching… based on comparing the target… output and the candidate… output ([0025] Recommendation module 132 determines whether at least one social network connection is identified (decision step 210) … In some embodiments, recommendation module 132 may identify one or more social network connections for the user, and may determine to send one, or several, of the identified connections. Recommendation module 132 may rank the one or more connections, based on various criteria, including, for example, a closeness in location, a number of connections in common over a threshold number, or a strong similarity in interests versus a lower similarity in interests); and
generating a networking recommendation based on determining that the matching… satisfies a… threshold ([0025] Recommendation module 132 determines whether at least one social network connection is identified (decision step 210). If at least one social network connection is identified (decision step 210, “yes” branch), recommendation module 132 sends the at least one recommended social network connection to the user (212). Recommendation module 132, when the recommended connection is identified, sends the recommendation to the user, for example, as a message or alert in social network application 122. The recommendation may include a name of another user, or some other identifying information. In various embodiments, recommendation module 132 includes a list of reasons why the connection is recommended, for example, similar interests, connections in common, or similar location. In some embodiments, recommendation module 132 may identify one or more social network connections for the user, and may determine to send one, or several, of the identified connections. Recommendation module 132 may rank the one or more connections, based on various criteria, including, for example, a closeness in location, a number of connections in common over a threshold number, or a strong similarity in interests versus a lower similarity in interests);
the neural network model further trained to achieve a network objective of the target user account based on comparing the target… output to the candidate… output ([0024] Recommendation module 132 performs operations according to the determined mining engine (208). In various embodiments, each mining engine identified is used to extract information from the retrieved user profile information, in order to determine one or more connections for the user. In an embodiment, the profile mining engine evaluates structured user profile information from the user's profile, and determines other users with matching, or similar, profile information. The structured profile information can be stored in database 134. In an embodiment, the network mining engine retrieves potential connections via the user's contact list, using one of a plurality of network mining methods, such as collaborative filtering, to find a second user with a maximum connections in common with the user. In an embodiment, the text mining engine identified for users in the third stage retrieves and collects a corpus of data from other users of the social network system, including, for example, status updates, user comments, user shared items, and communities in which the other user may be involved. The text mining engine uses the data with supervised learning methods to train a model, for example, a decision tree, a deep neural network (DNN), etc. Recommendation module 132, via the text mining engine, uses the model to predict a user's interests, given the user's information, where the model is based on a plurality of other users' data);
the server-side networking application configured to transmit the networking recommendation to a client-side user device associated with the target user account ([0025] If at least one social network connection is identified (decision step 210, “yes” branch), recommendation module 132 sends the at least one recommended social network connection to the user (212));
the networking recommendation ([0025] If at least one social network connection is identified (decision step 210, “yes” branch), recommendation module 132 sends the at least one recommended social network connection to the user (212)); and
the server-side networking application configured to train the neural network model ([0024] The text mining engine uses the data with supervised learning methods to train a model, for example, a decision tree, a deep neural network (DNN), etc.)
However, Han does not explicitly teach the following limitations:
application for… training a… model… based on client-side transmitted loss,
the neural network model trained to determine whether user accounts of the plurality of user accounts would be a good networking match by: applying a natural language processing (NLP) module to… a target user account of the plurality of user accounts, and generate a target NLP output;
applying the NLP module to… a second user account of the plurality of user accounts, and generate a candidate NLP output;
determining a matching score based on comparing the target NLP output and the candidate NLP output; and
generating a networking recommendation based on determining that the matching score satisfies a matching score threshold;
the neural network model further trained… based on comparing the target NLP output to the candidate NLP output;
the server-side networking application configured to receive, from the client-side user device, a loss indicating a value of the… recommendation; and
the server-side networking application configured to train the… model based on the loss received from the client-side user device.
Poslavsky, in the same field of endeavor, teaches the following limitations:
the neural network model trained to determine whether user accounts of the plurality of user accounts would be a good networking match by: applying a natural language processing (NLP) module to… a target user account of the plurality of user accounts, and generate a target NLP output ([0077] A user Z of a social media network is planning an upcoming trip to Paris. Z searches for things to do in Paris, as well as airfare to get there. An artificial intelligence algorithm determines from these indications that Z is planning an upcoming trip and searches Z's friends on the social media network for someone who lives in Paris. When no friends were found to live in Paris, the artificial intelligence algorithm searches the friends of Z's friends, and locates two people who live in Paris, X and Y. The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network. Further, the artificial intelligence algorithm uses a computer vision algorithm to process photographs posted by Z, X, and Y to the social media network, where the processing determines a similarity of the activities Z, X, and Y are performing in the photographs. The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking; [0022] The term “artificial intelligence,” as used herein, generally refers to machine intelligence that includes a computer model or algorithm that may be used to make a recommendation or prediction, classify data, or otherwise take an action that maximizes the chance of achieving of one or more goals of the artificial intelligence. Artificial intelligence may be or include a machine learning algorithm. The machine learning algorithm may be a trained machine learning algorithm, e.g. a machine learning algorithm trained on historical data. Such a trained machine learning algorithm may be trained using supervised, semi-supervised, or unsupervised learning process. Examples of machine learning algorithms include neural networks, support vector machines, and reinforcement learning algorithms);
applying the NLP module to… a second user account of the plurality of user accounts, and generate a candidate NLP output ([0077] A user Z of a social media network is planning an upcoming trip to Paris. Z searches for things to do in Paris, as well as airfare to get there. An artificial intelligence algorithm determines from these indications that Z is planning an upcoming trip and searches Z's friends on the social media network for someone who lives in Paris. When no friends were found to live in Paris, the artificial intelligence algorithm searches the friends of Z's friends, and locates two people who live in Paris, X and Y. The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network. Further, the artificial intelligence algorithm uses a computer vision algorithm to process photographs posted by Z, X, and Y to the social media network, where the processing determines a similarity of the activities Z, X, and Y are performing in the photographs. The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking; [0022] The term “artificial intelligence,” as used herein, generally refers to machine intelligence that includes a computer model or algorithm that may be used to make a recommendation or prediction, classify data, or otherwise take an action that maximizes the chance of achieving of one or more goals of the artificial intelligence. Artificial intelligence may be or include a machine learning algorithm. The machine learning algorithm may be a trained machine learning algorithm, e.g. a machine learning algorithm trained on historical data. Such a trained machine learning algorithm may be trained using supervised, semi-supervised, or unsupervised learning process. Examples of machine learning algorithms include neural networks, support vector machines, and reinforcement learning algorithms);
determining a matching score based on comparing the target NLP output and the candidate NLP output ([0077] Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking. Based on the weights the artificial intelligence algorithm has given the various data, the artificial intelligence algorithm determines that Z and Y are more compatible than Z and X); and
generating a networking recommendation based on determining that the matching score satisfies a matching score threshold ([0047] The connecting may comprise using an artificial intelligence algorithm to determine a compatibility of the member and the user. The artificial intelligence algorithm may connect the user and the member if the compatibility is above a threshold value);
the neural network model further trained to achieve a network objective of the target user account based on comparing the target NLP output to the candidate NLP output ([0077] A user Z of a social media network is planning an upcoming trip to Paris. Z searches for things to do in Paris, as well as airfare to get there. An artificial intelligence algorithm determines from these indications that Z is planning an upcoming trip and searches Z's friends on the social media network for someone who lives in Paris. When no friends were found to live in Paris, the artificial intelligence algorithm searches the friends of Z's friends, and locates two people who live in Paris, X and Y. The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network. Further, the artificial intelligence algorithm uses a computer vision algorithm to process photographs posted by Z, X, and Y to the social media network, where the processing determines a similarity of the activities Z, X, and Y are performing in the photographs. The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking; [0022] The term “artificial intelligence,” as used herein, generally refers to machine intelligence that includes a computer model or algorithm that may be used to make a recommendation or prediction, classify data, or otherwise take an action that maximizes the chance of achieving of one or more goals of the artificial intelligence. Artificial intelligence may be or include a machine learning algorithm. The machine learning algorithm may be a trained machine learning algorithm, e.g. a machine learning algorithm trained on historical data. Such a trained machine learning algorithm may be trained using supervised, semi-supervised, or unsupervised learning process. Examples of machine learning algorithms include neural networks, support vector machines, and reinforcement learning algorithms);
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., using NLP to process information, such as users’ information to match users together, and performing an action, such as connecting users together, based on a score being above a threshold) into similar systems.
However, Han, in combination with Poslavsky, does not explicitly teach the following limitations:
application for… training a… model… based on client-side transmitted loss,
the server-side networking application configured to receive, from the client-side user device, a loss indicating a value of the… recommendation; and
the server-side networking application configured to train the… model based on the loss received from the client-side user device.
Yang, in the same field of endeavor, teaches the following limitations:
application for… training a… model… based on client-side transmitted loss ([0036] The potential recommendation identification module 204 can apply the trained machine learning model to determine potential recommendations for a page... an administrator can provide feedback relating to a recommendation presented to the administrator. Feedback by administrators can be used to train or retrain the machine learning model for determining potential recommendations),
the server-side networking application configured to receive, from the client-side user device, a loss indicating a value of the… recommendation ([0043] The section 320 can include a mechanism for an administrator to provide feedback regarding recommendations. For example, the section 320 can display a question 327 “Is this helpful” next to a recommendation, and the administrator can click “Yes” or “No.” As explained above, feedback from administrators regarding recommendations can be used to train and retrain machine learning models); and
the server-side networking application configured to train the… model based on the loss received from the client-side user device ([0036] an administrator can provide feedback relating to a recommendation presented to the administrator. Feedback by administrators can be used to train or retrain the machine learning model for determining potential recommendations).
This known technique is applicable to the system of Han, in combination with Poslavsky, as they both share characteristics and capabilities, namely, they are directed to social networking systems that provide recommendations using machine learning. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Yang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Yang to the teachings of Han, in combination with Poslavsky, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., training a ML model based on user feedback) into similar systems.
Claim 22
Yang further teaches the following limitations:
wherein the value rates how beneficial the networking recommendation was for the target user account on a scale ([0043] The section 320 can include a mechanism for an administrator to provide feedback regarding recommendations. For example, the section 320 can display a question 327 “Is this helpful” next to a recommendation, and the administrator can click “Yes” or “No.” As explained above, feedback from administrators regarding recommendations can be used to train and retrain machine learning models).
This known technique is applicable to the system of Han, in combination with Poslavsky, as they both share characteristics and capabilities, namely, they are directed to social networking systems that provide recommendations using machine learning. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Yang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Yang to the teachings of Han, in combination with Poslavsky, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., providing user feedback on a recommendation in a certain form, such as a rating scale) into similar systems.
Claim 23
Han further teaches the following limitations:
wherein the neural network model is trained to generate the networking recommendation based on text corresponding to the target user account and text corresponding to the second user account ([0024] Recommendation module 132 performs operations according to the determined mining engine (208). In various embodiments, each mining engine identified is used to extract information from the retrieved user profile information, in order to determine one or more connections for the user. In an embodiment, the profile mining engine evaluates structured user profile information from the user's profile, and determines other users with matching, or similar, profile information. The structured profile information can be stored in database 134. In an embodiment, the network mining engine retrieves potential connections via the user's contact list, using one of a plurality of network mining methods, such as collaborative filtering, to find a second user with a maximum connections in common with the user. In an embodiment, the text mining engine identified for users in the third stage retrieves and collects a corpus of data from other users of the social network system, including, for example, status updates, user comments, user shared items, and communities in which the other user may be involved. The text mining engine uses the data with supervised learning methods to train a model, for example, a decision tree, a deep neural network (DNN), etc. Recommendation module 132, via the text mining engine, uses the model to predict a user's interests, given the user's information, where the model is based on a plurality of other users' data; [0025] If at least one social network connection is identified (decision step 210, “yes” branch), recommendation module 132 sends the at least one recommended social network connection to the user (212)).
Claim 24
Poslavsky further teaches the following limitations:
wherein the target NLP output provides an understanding corresponding to the text corresponding to the target user account, wherein the candidate NLP output provides an understanding corresponding to the text corresponding to the second user account ([0077] The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network… The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking).
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., NLP output providing an understanding corresponding to text) into similar systems.
Claim 25
Poslavsky further teaches the following limitations:
wherein the understanding is a semantic representation ([0077] The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network… The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking).
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., NLP output providing an understanding corresponding to text, the understanding being a semantic representation) into similar systems.
Claim 26
Poslavsky further teaches the following limitations:
wherein the neural network model is trained to apply the NLP module to respective portions of the text corresponding to the target user account to generate multiple target NLP outputs, wherein the neural network model is trained to apply the NLP module to respective portions of the text corresponding to the second user account to generate multiple candidate NLP outputs ([0077] The artificial intelligence algorithm next compares Z's interests with those of X and Y by using a natural language processing (NLP) algorithm to read Z, X, and Y's posts on the social media network… The artificial intelligence algorithm then searches for similarities between Z, X, and Y's answers to various surveys and quizzes each have taken on the social media network. Aggregating all of this data, the artificial intelligence algorithm determines that Z and X both enjoy hiking and classical music but differ fundamentally on politics, while Z and Y have similar pollical leanings, as well as both enjoying cooking).
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., generating multiple NLP outputs) into similar systems.
Claim 27
Poslavsky further teaches the following limitations:
wherein the neural network model is trained to generate the networking recommendation to initiate an introduction between a user associated with the target user account and a user associated with the second user account ([0047] The connecting may comprise using an artificial intelligence algorithm to determine a compatibility of the member and the user. The artificial intelligence algorithm may connect the user and the member if the compatibility is above a threshold value; [0022] Artificial intelligence may be or include a machine learning algorithm. The machine learning algorithm may be a trained machine learning algorithm… Examples of machine learning algorithms include neural networks).
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., generating a networking recommendation to initiate an introduction) into similar systems.
Claim 29
Poslavsky further teaches the following limitations:
wherein the matching score represents a similarity from a network objective of the target user account and skills and information of the second user account ([0009] a method for connecting users of a social network, comprising: (a) receiving, from the social network, an indication that a first user intends to travel to a location; (b) determining that a second user of the social network lives in the location, wherein the first user and the second user are associated with each other on the social network (i) directly or (ii) through one or more other users of the social network, and wherein the second user has offered a service on the social network; and (c) connecting the first user and the second user on the social network to enable the second user to perform the service for the first user; [0047] The connecting may comprise using an artificial intelligence algorithm to determine a compatibility of the member and the user. The artificial intelligence algorithm may connect the user and the member if the compatibility is above a threshold value).
This known technique is applicable to the system of Han as they both share characteristics and capabilities, namely, they are directed to matching / connecting users, through machine learning, based on comparing their profile data. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Poslavsky would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Poslavsky to the teachings of Han would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., a matching score that represents similarity between matched users) into similar systems.
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (US-20160283500) in view of Poslavsky (US-20210182976) and Yang et al. (US-20180107665), and further in view of Nigam et al. (US-20220101159).
Claim 28
Han, in combination with Poslavsky and Yang, does not explicitly teach the following limitations:
wherein the matching score represents a likelihood that the user associated with the target user account and the user associated with the second user account would like to establish a primary relationship with one another.
Nigam, in the same field of endeavor, teaches the following limitations:
wherein the matching score represents a likelihood that the user associated with the target user account and the user associated with the second user account would like to establish a primary relationship with one another ([0051] FIG. 4 illustrates the architecture for identifying possible new connections for a target entity; [0053] The training of the additional machine-learned models includes determining one or more additional sets of features relevant to determining second-pass ranking scores for each of the candidate connections. For example, the additional machine-learned models 413 can be trained to provide adjustments to each of the first-pass ranking scores based on various factors, such as a probability that a connectee will attempt to establish the recommended connection with a connector, a probability that a connector will accept a request to establish the connection from the connectee).
This known technique is applicable to the system of Han, in combination with Poslavsky and Yang, as they both share characteristics and capabilities, namely, they are directed to providing recommendations for matching / connecting users. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Nigam would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Nigam to the teachings of Han, in combination with Poslavsky and Yang, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., a matching score that represents probability that users will match) into similar systems.
Response to Arguments
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1-2, 4, 6-12, 14, and 16-20:
Claims 1-2, 4, 6-12, 14, and 16-20 are now cancelled. New claims 21-29 do not cover an abstract idea because they are about specialized training of a neural network model. The specialized training is server-side training based on loss transmitted from the client side.
ARGUMENT 1: Examiner El-Chanti proposed that we add some language to the claims that relate to the neural network implementing an action so as to move towards overcoming the Section 101 rejection. We have done so in this response. We have added the following language: "the neural network model further trained to achieve a network objective of the target user account based on comparing the target NLP output to the candidate NLP output;". The action implemented by the claimed neural network model is "achievement of the network objective" of the target use account.
ARGUMENT 2: New claims 21-29 do not cover an abstract idea under Prong 2A, and for the sake of the argument, even if they do (which they do not), under Prong 2B, new claims 21-29 provide a practical application of the server-side training based on the client-side loss.
Step 2A, Prong One: regarding "Certain Methods of Organizing Human Activity," Example 39 of the 2019 PEG (now incorporated into the MPEP): a claim directed to "[a] computer- implemented method of training a neural network for facial detection" does not recite any of the judicial exceptions enumerated in the 2019 PEG. This claim was found eligible because it does not recite a judicial exception. Similar to Example 39, the present claims do not recite a judicial exception. For example, similar to the element "training the neural network in a first stage using the first training set" of Example 39, the claimed elements of "the server-side networking application configured to receive, from the client-side user device, a loss indicating a value of the networking recommendation, and the server-side networking application configured to train the neural network model based on the loss received from the client-side user device" do not set forth or describe any judicial exceptions.
Step 2A, Prong Two: The present claims generate accurate network recommendation by providing a networking application for server-side training a neural network model based on client-side transmitted loss. This is the practical application.
Step 2B: Finally, the claims are eligible under Step 2B because the claims recite an inventive concept that amounts to significantly more than the judicial exception itself (if any). The claims recite "significantly more" than an abstract idea because they effect an improvement in the technology and/or technical field. Additional elements may amount to an inventive concept when limitations include improvements to the functioning of a computer. MPEP § 2106.05(I)(A). In DDR Holdings v. Hotels.com, the Court of Appeals for the Federal Circuit explains that claimed solutions that are based in computer technology to solve specific problems in the realm of computer technology are patent eligible. 773 F.3d 1245, 1257 (Fed. Cir. 2014). In DDR, the claims were found to be patent-eligible in that the claims did not "merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet." Id. at 1257. These claims included additional features that ensured the claims were more than a drafting effort designed to monopolize the abstract idea. Id at 1259. The claims were found patent eligible because the claims addressed a problem that is particular to the Internet - that of retaining website visitors. Id at 1257. As noted by the Court, "the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." Id. Here, similar to the claims at issue in DDR, the claimed solution resolves a problem specific to the realm of computer technology, i.e., neural network training, and is thus patent eligible. Specifically, the claims are not merely recitation of a well-known business practice with a requirement to perform it on a computer, but rather, the claims resolve of problem inherent with computers, that is, a problem with natural language processing and understanding the complexity of network connections.
ARGUMENT 3: Examiner El-Chanti recently allowed a case whose claims are analogous to the new claims 21-29, and whose reasons for allowance are similarly applicable to the new claims 21-29. Examiner El-Chanti recently allowed application No.18/475,048. The claims at issue there and the reasons for allowance in the notice of allowance makes it very natural to allow the subject application. See excerpts of the claims and the notice of allowance from application No.18/475,048. Interestingly, our claims relate to more than social medial accounts, they go further by focusing on neural network training, which makes then even more allowable than application No.18/475,048. The similarity is that we both talk about text analysis of accounts; networking accounts in our case, social media accounts in the case of application No.18/475,048. The reasons for allowance by Examiner EI-Chanti in application No.18/475,048 also apply squarely to our claims. We have neural networks (analogous to artificial intelligence routines), network accounts (analogous to social media accounts), NLP module (analyzing user-generated content).
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
ARGUMENT 1: Regarding adding language that relates to the neural network implementing an “action,” this was in the context that the system needs to be performing an action beyond the abstract idea of simply outputting / displaying data i.e., providing a recommendation, and in the context of the example of Claim 3 of PEG Example 47, which recites training of a neural network, and where the last two limitations of the claim recite “dropping the one or more malicious network packets in real time; and blocking future traffic from the source address.” These claimed steps provide specific computer solutions that use the output from the ANN to provide security solutions to the detected anomalies. As indicated in paragraph six of the background, the system may “automatically” perform dropping of malicious network packets and blocking future traffic without the need for any action by a network administrator. Instead, the ANN may make decisions about whether a network packet is potentially malicious and take action to drop malicious network packets and block future traffic. In the present claims, the recitation of “the neural network model further trained to achieve a network objective of the target user account based on comparing the target NLP output to the candidate NLP output” is still just a recitation of generating, transmitting, and displaying information to a user. The specification does not explicitly disclose that the neural network is trained to achieve a network objective of a user account. Paragraphs [0034], [0046], and [0049] of the specification describe the networking objectives as goals that the user may add to their user account, and the achievement of a networking objective of a user is described as matching the user with another user that can help the user achieve the networking objective, and then generating a networking recommendation and transmitting a notification including the recommendation to the user.
ARGUMENT 2:
Step 2A, Prong One: The present claims are not analogous to Example 39. Example 39 does not recite a judicial exception; however, the present claims do recite a judicial exception. Regarding the receiving of a loss indicating a value of the networking recommendation and training the neural network based on the loss received from the user device, this further moves the claim into the abstract idea, specifically the Certain Methods of Organizing Human Activity grouping, as the claim requires a human to perform an action of providing the loss in order to train the neural network.
Step 2A, Prong Two: The generation of the recommendation is part of the abstract idea. The networking application is a particular technological environment that is generally linked to the abstract idea, and the training of a neural network model is recited in a generic manner, as a tool to implement the abstract idea. Further, as previously stated, the client-side transmitted loss further moves the claim into the abstract idea, as this is requiring a human to perform the step. Thus, the additional elements do not integrate the abstract idea into a practical application.
Step 2B: The claims do not effect an improvement in any technology or technical field. Regarding Applicant’s argument that the claimed solution resolves a problem specific to the realm of computer technology, i.e., neural network training, and the argument that the claims resolve a problem inherent with computers, that is, a problem with natural language processing and understanding the complexity of network connections, the specification does not provide any details of how the claimed invention provides any improvement to the functioning of computer technology, or neural network training, or NLP. As such, the claims do not recite additional elements that amount to significantly more than the abstract idea.
ARGUMENT 3: This is a moot argument as patent applications are examined on a case-by-case basis, and “similar” cases cannot be relied upon for 101 guidance when examining. Nevertheless, the claims of application no. 18/475,048 are not analogous to the present claims.
Regarding Applicant’s email on the Dec. 5, 2025 Desjardins Memo, while Examiner received the email, this was not a proper supplemental response to arguments. Nevertheless, to ensure compact prosecution, Examiner did consider the claims with respect to the Desjardins decision and subsequent memo. Desjardins recites an improvement to machine learning technology, while the present claims, if anything, only recite an improvement to the abstract idea itself.
Applicant’s Argument Regarding 35 USC 103 Rejections of Claims 1-2, 4, 6-12, 14, and 16-20: The previously cited references do not teach the limitations of the new claims.
Examiner’s Response: Applicant’s arguments have been considered but are moot in light of the new ground of rejection above.
Conclusion
The prior art made of record and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art, is listed on the enclosed PTO-892.
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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629