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 .
Claim Objections
Claim 2 is objected to because claim 2 repeats the word “the” in the limitation (emphasized) “…the providing of the the initial match scores…”
Applicant is advised that should claim 13 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 26 is objected to under 37 CFR 1.75(c) as being in improper form because a of the multiple dependencies in claim 26. See MPEP § 608.01(n). That is, claim 26 is objected to as being dependent on multiple claims. Accordingly, claim 26 has not been further treated on the merits.
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.
Claims 1-25 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 8, and 17, claims 1, 8, and 17 are rejected because the claims limitations of “the network”. There is insufficient antecedent basis for these limitations. Please note, it appears claim 1 may have introduced the concept as “a matching network”.
Claim 8 is further rejected as indefinite because claim 8 recites “the matching network”. There is insufficient antecedent basis for this limitation.
Accordingly, claims 1, 8 and 17 are rejected as indefinite. Claims 2-7, 9-16 and 18-25 do not clarify this issue and accordingly are rejected due to their dependencies.
Claim 24 is further rejected as indefinite for two reasons. First, claim 24 is rejected as indefinite because claim 24 recites “the select logistics”. There is insufficient antecedent basis for this limitation.
Second, claim 24 is rejected as indefinite because the Specification appears to use the term “logistics” to mean “sex, age, height, weight, athleticism, ethnicity, race, profession, political affiliation”, etc., ¶[00129] of the Specification as filed while the accepted meaning is “the handling of the details of an operation.” The term is indefinite because the specification does not clearly redefine the term. Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). That is, claim 24 is not clear because it is not clear what the claim, when read in light of the specification, is intended to encompass. For the purposes of analyzing the claim, Examiner is interpreting the select logistics as encompassing demographic information and the like (e.g., the various features discussed in ¶[00129] of the Specification as filed).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-7 are directed to a process; and claims 8-25 are directed to a machine. Therefore, we proceed to Step 2.
Independent Claim 1
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 1 recites an abstract idea in the limitations (emphasized):
…creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user;
forming a representation of the Pvi for the user of the network based on the initial preference data from the user;
assembling an initial pool of candidate partners for the use from within the matching network based on the initial preference data from the user, each candidate in the initial pool meeting the set of criteria within an acceptable deviation threshold used by the method to select each candidate in the initial pool;
providing initial match scores to the user for selecting interactions to pursue, the initial match scores including a numerical assessment that measures the fit of each candidate to the set of criteria;
obtaining a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool;
developing a machine learning model with the plurality of sets of interaction data, the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input and functioning to produce a modeled preference data for the user as an output, the developing including selecting the machine learning model;
preparing the plurality of sets of interaction data for use in training the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model;
training the model with the training data, the developing including selecting training variables;
testing the model with the testing data to evaluate the utility of the output; and,
improving the performance of the machine learning model, the improving including adjusting the variables in the training process to improve the output of the model;
iterating the model stepwise from Pvi to a modeled, virtual ideal partner, Pvmn, where n ranges from 1 to T, and T is the total number of iterations of the model, the modifying including creating each Pvmn from modeled preference data output by the model at each iteration;
modifying the pool of candidate partners at each iteration using the modeled preference data for each Pvmn; and,
forming a representation of PvmT for the user of the network based on the modeled preference data from the user.
These limitations recite an abstract idea because these limitations encompass managing personal behavior or relationships or interactions between people. These limitations encompass managing personal behavior or relationships or interactions between people because these limitations essentially encompass matchmaking (i.e., identifying potential romantic partners for people based on their preferences, etc.). Claims that encompass managing personal behavior or relationships or interactions between people fall within the “Certain Methods of Organizing Human Activity”. Claim 1 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user;
forming a representation of the Pvi for the user of the network based on the initial preference data from the user;
assembling an initial pool of candidate partners for the use from within the matching network based on the initial preference data from the user, each candidate in the initial pool meeting the set of criteria within an acceptable deviation threshold used by the method to select each candidate in the initial pool;
providing initial match scores to the user for selecting interactions to pursue, the initial match scores including a numerical assessment that measures the fit of each candidate to the set of criteria;
obtaining a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool;
developing a machine learning model with the plurality of sets of interaction data, the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input and functioning to produce a modeled preference data for the user as an output, the developing including selecting the machine learning model;
preparing the plurality of sets of interaction data for use in training the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model;
training the model with the training data, the developing including selecting training variables;
testing the model with the testing data to evaluate the utility of the output; and,
improving the performance of the machine learning model, the improving including adjusting the variables in the training process to improve the output of the model;
iterating the model stepwise from Pvi to a modeled, virtual ideal partner, Pvmn, where n ranges from 1 to T, and T is the total number of iterations of the model, the modifying including creating each Pvmn from modeled preference data output by the model at each iteration;
modifying the pool of candidate partners at each iteration using the modeled preference data for each Pvmn; and,
forming a representation of PvmT for the user of the network based on the modeled preference data from the user.
The additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of input obtained from the user including initial preference data selected by the user and obtaining sets of interaction data from the user, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving data (i.e. receiving user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Second, the additional elements of developing, preparing, training, testing, improving and iterating the machine learning model to produce the modeled preference data, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited too broadly and generally (i.e., as a generic implementation of machine learning) such that it amounts to no more than mere instructions to apply the exception. Claim 1 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 is not patent eligible.
Independent Claim 8
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 8 recites an abstract idea in the limitations (emphasized):
…a processor; and a memory, the memory comprising:
a preference module on a non-transitory computer readable medium operable for receiving and storing initial preference data from a user of a networking community;
a pooling module on a non-transitory computer readable medium operable for assembling a pool of candidate partners for the user from within the matching network based on the initial preference data from the user, each candidate in the pool meeting the set of criteria within an acceptable deviation threshold used by the method to select each candidate in the pool;
an interaction database on a non-transitory computer readable medium operable for receiving and storing a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool;
a modeling engine on a non-transitory computer readable medium operable for developing a machine learning model with the plurality of sets of interaction data, the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input and functioning to produce a modeled preference data for the user as an output, the developing including
selecting the machine learning model;
preparing the plurality of sets of interaction data for use in training and testing the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model;
training the model with the training data, the training including selecting training variables;
testing the model with the testing data to evaluate the utility of the output;
improving the performance of the machine learning model, the improving including adjusting the variables in the training to improve the output of the model;
wherein, the system is configured for
creating a modified pool of candidate partners using the output of the model from the input of the plurality of sets of interaction data;
sending the modified pool of candidate partners to the pooling module for use in a next iteration of the system; and,
generating a modeled preference data for the user through the iterative application of the model to optimize the candidate partners for a user interaction; and,
the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including
gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user; and,
forming a representation of the Pvi for the user of the network based on the initial preference data from the user.
These limitations recite an abstract idea because these limitations encompass managing personal behavior or relationships or interactions between people. These limitations encompass managing personal behavior or relationships or interactions between people because these limitations essentially encompass matchmaking (i.e., identifying potential romantic partners for people based on their preferences, etc.). Claims that encompass managing personal behavior or relationships or interactions between people fall within the “Certain Methods of Organizing Human Activity”. Claim 8 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 8 recites the additional elements (emphasized):
…a processor; and a memory, the memory comprising:
a preference module on a non-transitory computer readable medium operable for receiving and storing initial preference data from a user of a networking community;
a pooling module on a non-transitory computer readable medium operable for assembling a pool of candidate partners for the user from within the matching network based on the initial preference data from the user, each candidate in the pool meeting the set of criteria within an acceptable deviation threshold used by the method to select each candidate in the pool;
an interaction database on a non-transitory computer readable medium operable for receiving and storing a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool;
a modeling engine on a non-transitory computer readable medium operable for developing a machine learning model with the plurality of sets of interaction data, the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input and functioning to produce a modeled preference data for the user as an output, the developing including
selecting the machine learning model;
preparing the plurality of sets of interaction data for use in training and testing the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model;
training the model with the training data, the training including selecting training variables;
testing the model with the testing data to evaluate the utility of the output;
improving the performance of the machine learning model, the improving including adjusting the variables in the training to improve the output of the model;
wherein, the system is configured for
creating a modified pool of candidate partners using the output of the model from the input of the plurality of sets of interaction data;
sending the modified pool of candidate partners to the pooling module for use in a next iteration of the system; and,
generating a modeled preference data for the user through the iterative application of the model to optimize the candidate partners for a user interaction; and,
the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including
gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user; and,
forming a representation of the Pvi for the user of the network based on the initial preference data from the user.
The additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the processor, memory, the preference module, pooling module, interaction database, and modeling engine on the non-transitory computer readable medium, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality such that it amounts to no more than mere instructions to apply the exception.
Second, the additional elements of receiving and storing a plurality of sets of interaction data and initial preference data obtained from the user, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving and storing data (i.e. receiving and storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Third, the additional elements of developing, preparing, training, testing, improving and iterating the machine learning model to produce the modeled preference data, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited too broadly and generally (i.e., as a generic implementation of machine learning) such that it amounts to no more than mere instructions to apply the exception. Claim 8 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 8 is not patent eligible.
Independent Claim 17
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 17 recites an abstract idea in the limitations (emphasized):
…a processor; and, a memory on a non-transitory computer readable medium, the memory including
a registration module on a non-transitory computer readable medium, the registration module operable for receiving user information and assigning a first anonymous identifier to the first user and a second anonymous identifier to the second user for use in the shared user environment;
a location engine on a non-transitory computer readable medium, the location engine operable for identifying the location of the first user and the location of the second user within the shared user environment;
an assessment module on a non-transitory computer readable medium, the assessment module operable for the first user to identify and assess the second user, and the second user to identify and assess the first user;
a matching module on a non-transitory computer readable medium, the matching module operable for the first user to identify a match with the second user and the second user to identify the match with the first user, the matching module notifying the first user and the second user of the match;
a data exchange module on a non-transitory computer readable medium, the data exchange module operable for the first user to communicate with the second user upon the identification of the match, and the second user to communicate with the first user upon the identification of the match; and,
a shared user database on a non-transitory computer readable medium, the shared user database operable for storing personal information, location information, and the anonymous identifiers of n users, in which n is a number of users ranging from 2 users to any number of users, of which at least the first user and the second user are in the shared user environment;
wherein, each of the first user and the second user are notified of the match by the matching module and have the option to communicate with the other user through the data exchange module; and,
the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including
gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user; and,
forming a representation of the Pvi for the user of the network based on the initial preference data from the user.
These limitations recite an abstract idea because these limitations encompass managing personal behavior or relationships or interactions between people. These limitations encompass managing personal behavior or relationships or interactions between people because these limitations essentially encompass matchmaking (i.e., identifying potential romantic partners for people based on their preferences, etc.). Claims that encompass managing personal behavior or relationships or interactions between people fall within the “Certain Methods of Organizing Human Activity”. Claim 17 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 17 recites the additional elements (emphasized):
… a processor; and, a memory on a non-transitory computer readable medium, the memory including
a registration module on a non-transitory computer readable medium, the registration module operable for receiving user information and assigning a first anonymous identifier to the first user and a second anonymous identifier to the second user for use in the shared user environment;
a location engine on a non-transitory computer readable medium, the location engine operable for identifying the location of the first user and the location of the second user within the shared user environment;
an assessment module on a non-transitory computer readable medium, the assessment module operable for the first user to identify and assess the second user, and the second user to identify and assess the first user;
a matching module on a non-transitory computer readable medium, the matching module operable for the first user to identify a match with the second user and the second user to identify the match with the first user, the matching module notifying the first user and the second user of the match;
a data exchange module on a non-transitory computer readable medium, the data exchange module operable for the first user to communicate with the second user upon the identification of the match, and the second user to communicate with the first user upon the identification of the match; and,
a shared user database on a non-transitory computer readable medium, the shared user database operable for storing personal information, location information, and the anonymous identifiers of n users, in which n is a number of users ranging from 2 users to any number of users, of which at least the first user and the second user are in the shared user environment;
wherein, each of the first user and the second user are notified of the match by the matching module and have the option to communicate with the other user through the data exchange module; and,
the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network based on initial preference data as input obtained from the user, the creating including
gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user; and,
forming a representation of the Pvi for the user of the network based on the initial preference data from the user.
The additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the processor, memory, non-transitory computer readable medium, registration module, location engine, assessment module, matching module, data exchange module, shared user database, and representation module, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality such that it amounts to no more than mere instructions to apply the exception.
Second, the additional elements of receiving user information and assigning anonymous identifiers, identifying the locations, storing the personal information etc., and obtaining and gathering initial preference data input from the user, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving and storing data (e.g. receiving and storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claim 17 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 17 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 17 is not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract die afro the following reasons.
Claim 2 recites the same abstract idea as the independent claim because the user setting an acceptable deviation is a part of the matchmaking process (i.e., identifying limitations or requirements in one’s preferences).
Claims 3-7, and 13-16 recite the additional elements of forming various representations of the ideal partners. These additional elements, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as generic processes of displaying data) such that it amounts to no more than mere instructions to apply the exception.
Claim 9 recites the additional elements of training and testing which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited too broadly and generally (i.e., as a generic implementation of machine learning) such that it amounts to no more than mere instructions to apply the exception.
Claim 10 recites the same abstract idea as the independent claim because excluding users based on preferences is a part of the matchmaking process (i.e., identifying potential partners based on preferences).
Claim 11 recites the additional elements of the assessment module on a non-transitory computer readable medium providing the match scores which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as a generic process of displaying data) such that it amounts to no more than mere instructions to apply the exception.
Claims 12 and 20 recite the additional elements of the avatar module creating avatars which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as a generic process of displaying data) such that it amounts to no more than mere instructions to apply the exception.
Claim 18 recites a second abstract idea, advertising, because claim 18 recites targeting advertisements to users. Examiner finds the presence of these two abstract ideas does not render the claim non-abstract, see MPEP 2106.04.I discussing Recognicorp, LLC v. Nintendo Co., Ltd., 855 F. 3d 1322, 1327 (stating combining “one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”).
Claim 18 further recites the additional elements of an advertising model which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as generic software) such that it amounts to no more than mere instructions to apply the exception.
Claims 19 and 21 recite the additional elements of the feedback module and ratings module receiving and recording feedback and assessing relative levels of interest which, as claimed, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving and storing data (e.g. receiving and storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 22 recites the same abstract idea as the independent claim because setting a governing level based is a part of the matchmaking process (i.e., identifying potential partners based on preferences).
Claim 22 further recites the additional elements of a governing model which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as generic software) such that it amounts to no more than mere instructions to apply the exception.
Claim 23 recites the same abstract idea as the independent claim because helping users locate other users level based is a part of the matchmaking process.
Claim 23 further recites the additional elements of a distribution model which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as generic software) such that it amounts to no more than mere instructions to apply the exception.
Claim 24 recites the additional elements of the intelligence module compiling logistics which, as claimed, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving and storing data (e.g. receiving and storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim 25 recites the additional elements of the mood module showing the mood which, as claimed, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of functionality (i.e., as a generic process of displaying data) such that it amounts to no more than mere instructions to apply the exception.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim(s) 17, 20, and 22-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Storment et al, US Pub. No. 2021/0065314, herein referred to as “Storment” in view of Margines, US Pub. No. 2013/0035912, herein referred to as “Margines”.
Regarding claim 17, Storment teaches:
a processor; and, a memory on a non-transitory computer readable medium, the memory including (processor, memory, non-transitory computer-readable media, ¶[0015]),
a registration module on a non-transitory computer readable medium, the registration module operable for receiving user information (user creates personal profile, ¶¶[0002], [0007])
and assigning a first anonymous identifier to the first user and a second anonymous identifier to the second user for use in the shared user environment (system is semi-anonymous because it does not provide each user’s contact information, ¶[0064]);
a location engine on a non-transitory computer readable medium, the location engine operable for identifying the location of the first user and the location of the second user within the shared user environment (users geographical location, ¶[0007]; see also ¶¶[0013], [0064] discussing tracking users’ locations);
an assessment module on a non-transitory computer readable medium, the assessment module operable for the first user to identify and assess the second user, and the second user to identify and assess the first user (users swipe left or right to indicate decision on selecting a match from presented options, ¶[0003]; see also ¶[0062] and Fig. 2 discussing user deciding to keep or unlink);
a matching module on a non-transitory computer readable medium, the matching module operable for the first user to identify a match with the second user and the second user to identify the match with the first user, the matching module notifying the first user and the second user of the match (sends notification link was created, ¶¶[0036], [0042]);
a data exchange module on a non-transitory computer readable medium, the data exchange module operable for the first user to communicate with the second user upon the identification of the match, and the second user to communicate with the first user upon the identification of the match (communication channel is opened between users, ¶¶[0036], [0042]);
and, a shared user database on a non-transitory computer readable medium, the shared user database operable for storing personal information, location information, and the anonymous identifiers of n users, in which n is a number of users ranging from 2 users to any number of users, of which at least the first user and the second user are in the shared user environment (creates profiles with various information and location, ¶[0007]; see also e.g., ¶[0036] and Fig. 2 discussing multiple users);
wherein, each of the first user and the second user are notified of the match by the matching module and have the option to communicate with the other user through the data exchange module (sends notification link was created and communication channel is opened between users, ¶¶[0036], [0042]).
However Storment does not teach but Margines does teach:
and, the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network (determines ideal partner for a particular individual, ¶[0038]; see also ¶[0055] and Fig. 3 discussing maximizing the ideal partner model)
based on initial preference data as input obtained from the user, the creating including gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user (receives attributes of partner from user, ¶[0111] and Fig. 5; see also e.g., ¶¶[0061]-[0064] and Figs. 4, 5 discussing receiving answers to survey questions and ¶¶[0031]-[0032] discussing social network);
and, forming a representation of the Pvi for the user of the network based on the initial preference data from the user (determines ideal partner for a particular individual, e.g., ¶¶[0038], [0073]; see also ¶[0055] and Figs. 3, 6, and 7 discussing maximizing the ideal partner model).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matching of Storment with the social matchmaking of Margines because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the matchmaking of Storment would likely be improved by determining the users’ ideal partners, i.e., as taught by Margines, and accordingly would have modified Storment to model ideal partners.
Regarding claim 20, the combination of Storment and Margines teaches all the limitations of claim 17 and Margines further teaches:
an avatar module, the avatar module instructing the processor to create avatars designed or selected by users of the system (profile includes photo, ¶[0128]; see also Fig. 13 showing profile pictures).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matching of Storment with the social matchmaking of Margines because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the matchmaking of Storment would likely be improved by determining the users’ ideal partners, i.e., as taught by Margines, and accordingly would have modified Storment to model ideal partners.
Regarding claim 22, the combination of Storment and Margines teaches all the limitations of claim 17 and Margines further teaches:
a governing module, the governing module instructing the processor to set a governing level of interest in a user's database before the system recognizes a mutual interest in a meeting (customized threshold for user for monitoring ideal partner, ¶[0085] and Fig. 9).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matching of Storment with the social matchmaking of Margines because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the matchmaking of Storment would likely be improved by determining the users’ ideal partners, i.e., as taught by Margines, and accordingly would have modified Storment to model ideal partners.
Regarding claim 23, the combination of Storment and Margines teaches all the limitations of claim 17 and Storment further teaches:
a distribution module, the distribution module instructing the processor to help users locate nodes of users of the system (provides search functionality for searching and parsing through users, ¶[0060]).
Regarding claim 24, the combination of Storment and Margines teaches all the limitations of claim 17 and Storment further teaches:
an intelligence module, the intelligence module instructing the processor to target and compile the select logistics of users of the system (profile information includes user first name, age, gender, personality type, employment, education level, etc., ¶[0007]).
Regarding claim 25, the combination of Storment and Margines teaches all the limitations of claim 17 and Storment further teaches:
a mood module, the mood module instructing the processor to show the mood of one or more users of the system to other users of the system (displays various user information, ¶[0007]. Please note, Examiner finds that the limitations specifying the particular data being displayed is mood data does not substantially further limit the scope of the claim because the type of information being displayed does not functionally alter or relate to the system and merely labeling the information does not patentably distinguish the claimed invention, see MPEP 2111.05).
Claim(s) 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Margines, US Pub. No. 2013/0035912, herein referred to as “Margines”, in view of Shoemaker et al., US Pat. No. 7,885,902, herein referred to as “Shoemaker”, in view of Storment et al, US Pub. No. 2021/0065314, herein referred to as “Storment”.
Regarding claim 1, Margines teaches:
creating an initial virtual, ideal partner, Pvi, for the user of the network (determines ideal partner for a particular individual, ¶[0038]; see also ¶[0055] and Fig. 3 discussing maximizing the ideal partner model)
based on initial preference data as input obtained from the user, the creating including gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user (receives attributes of partner from user, ¶[0111] and Fig. 5; see also e.g., ¶¶[0061]-[0064] and Figs. 4, 5 discussing receiving answers to survey questions and ¶¶[0031]-[0032] discussing social network);
forming a representation of the Pvi for the user of the network based on the initial preference data from the user (determines ideal partner for a particular individual, e.g., ¶¶[0038], [0073]; see also ¶[0055] and Figs. 3, 6, and 7 discussing maximizing the ideal partner model);
assembling an initial pool of candidate partners for the use from within the matching network based on the initial preference data from the user, each candidate in the initial pool meeting the set of criteria within an acceptable deviation threshold used by the method to select each candidate in the initial pool (recommends profiles similar to ideal partner, ¶[0077] based on threshold, ¶[0073] and Fig. 7; see also ¶[0093] and Fig. 11 discussing listing and ranking potential partners);
providing initial match scores to the user for selecting interactions to pursue, the initial match scores including a numerical assessment that measures the fit of each candidate to the set of criteria (displays potential partners and match scores, ¶¶[0100]-[0103] and Fig. 13)
developing a machine learning model to produce a modeled preference data for the user as an output, the developing including selecting the machine learning model (uses appropriate model, e.g., neural network, to identify matches, ¶[0043]);
modifying the pool of candidate partners at each iteration using the modeled preference data for each Pvmn (updates recommendations as profiles are updated, ¶[0114]).
However Margines does not teach but Shoemaker does teach:
obtaining a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool (receives feedback on dates and matches, Col. 10, ll. 41-56 and Fig. 4);
developing a machine learning model with the plurality of sets of interaction data (trains learning algorithm, e.g., Col. 5, ll. 6-18; Col. 10, ll. 21-33; see also Col. 9, ll. 32-40 discussing embodiment machine learning algorithm),
the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input (uses feedback to train learning algorism, Col. 10, l. 57 – Col. 11, l. 10)
and functioning to produce a modeled preference data for the user as an output, the developing including selecting the machine learning model (model produces hypotheses for candidate compatibilities, Col. 10, l. 21-56 and Fig. 4);
preparing the plurality of sets of interaction data for use in training the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model (uses feedback and recommendations that resulted in the interaction are used to train learning algorism, Col. 10, l. 57 – Col. 11, l. 10; see also Col. 11, ll. 30-51 discussing refining model)
training the model with the training data, the developing including selecting training variables (trains learning algorithm, e.g., Col. 5, ll. 6-18; Col. 10, ll. 21-56);
and, improving the performance of the machine learning model, the improving including adjusting the variables in the training process to improve the output of the model (refines model, Col. 11, ll. 30-51);
iterating the model stepwise from Pvi to a modeled, virtual ideal partner, Pvmn, where n ranges from 1 to T, and T is the total number of iterations of the model, the modifying including creating each Pvmn from modeled preference data output by the model at each iteration (process is iterative, Fig. 4 and Col. 10, l. 57 – Col. 11, l. 9);
forming a representation of PvmT for the user of the network based on the modeled preference data from the user (updates hypotheses to refine future recommendations, Col. 10, ll. 41-56 and Fig. 4).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
However the combination of Margines and Shoemaker does not teach but Storment does teach:
testing the model with the testing data to evaluate the utility of the output (tests machine learning model using a control data set, ¶[0018])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking with learning-based recommendations of Margines and Shoemaker with the testing of Storment because the use of a known technique to improve similar devices is obvious, see MPEP 2143.I.C. That is, one of ordinary skill would have recognized that when training machine learning models, e.g., as in Margines and Shoemaker, it is useful to test the trained model, i.e., as in Storment.
Regarding claim 2, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
wherein the providing of the the initial match scores is limited to candidates that meet an acceptable deviation selected by the user (customized threshold for user for monitoring ideal partner, ¶[0085] and Fig. 9).
Regarding claim 3, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
wherein the representation of Pvi is a data representation (provides data on ideal partner, Fig. 9 and ¶¶[0082]-[0084]).
Regarding claim 4, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
wherein the representation of Pvi is a graphical representation that includes an image of Pvi (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
Regarding claim 5, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
wherein the representation of PvmT is a data representation (provides data on ideal partner, Fig. 9 and ¶¶[0082]-[0084]).
Regarding claim 6, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
wherein the representation of PvmT is a graphical representation that includes an image of PvmT (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
Regarding claim 7, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 1 and Margines further teaches:
the representation including an image of each Pvmn (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
However Margines does not teach but Shoemaker does teach
wherein the method includes forming a representation of each Pvmn for the user of the network at each iteration (updates hypotheses to refine future recommendations, Col. 10, ll. 41-56 and Fig. 4).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Regarding claim 8, Margines teaches:
a processor; and a memory, the memory comprising (processor and memory, ¶¶[0029], [0132] and Fig. 1):
a preference module on a non-transitory computer readable medium operable for (computer-readable storage medium, ¶[0132] and claim 17)
receiving and storing initial preference data from a user of a networking community (receives and stores attributes of partner from user, ¶¶[0107], [0111] and Fig. 5; see also e.g., ¶¶[0061]-[0064] and Figs. 4, 5 discussing receiving answers to survey questions and ¶¶[0031]-[0032] discussing social network);
a pooling module on a non-transitory computer readable medium operable for assembling a pool of candidate partners for the user from within the matching network (recommends profiles similar to ideal partner, ¶[0077] based on threshold, ¶[0073] and Fig. 7; see also ¶[0093] and Fig. 11 discussing listing and ranking potential partners);
based on the initial preference data from the user, each candidate in the pool meeting the set of criteria (determines ideal partner for a particular individual, e.g., ¶¶[0038], [0073], based on user input, ¶[0068]);
within an acceptable deviation threshold used by the method to select each candidate in the pool (recommends profiles similar to ideal partner, ¶[0077] based on threshold, ¶[0073]);
a modeling engine on a non-transitory computer readable medium operable for developing a machine learning model; (uses appropriate model, e.g., neural network, to identify matches, ¶[0043]); selecting the machine learning model (uses appropriate model, e.g., neural network, to identify matches, ¶[0043]);
wherein, the system is configured for creating a modified pool of candidate partners using the output of the model from the input of the plurality of sets of interaction data (updates recommendations as profiles are updated, ¶[0114]);
the system further including a representation module on a non-transitory computer readable medium operable for creating an initial virtual, ideal partner, Pvi, for the user of the network (determines ideal partner for a particular individual, ¶[0038]; see also ¶[0055] and Fig. 3 discussing maximizing the ideal partner model)
based on initial preference data as input obtained from the user the creating including gathering initial preference data from the user of the network, the initial preference data including a set of criteria selected by the user (receives attributes of partner from user, ¶[0111] and Fig. 5; see also e.g., ¶¶[0061]-[0064] and Figs. 4, 5 discussing receiving answers to survey questions);
and forming a representation of the Pvi for the user of the network based on the initial preference data from the user (determines ideal partner for a particular individual, e.g., ¶¶[0038], [0073]; see also ¶[0055] and Figs. 3, 6, and 7 discussing maximizing the ideal partner model).
However Margines does not teach but Shoemaker does teach:
an interaction database on a non-transitory computer readable medium operable for receiving and storing a plurality of sets of interaction data from the user, each set in the plurality of sets representing the user's assessment of an actual interaction with a respective candidate in the initial pool (receives and stores feedback on dates and matches, Col. 10, ll. 41-56 and Fig. 4);
a modeling engine on a non-transitory computer readable medium operable for developing a machine learning model with the plurality of sets of interaction data (trains learning algorithm, e.g., Col. 5, ll. 6-18; Col. 10, ll. 21-33; see also Col. 9, ll. 32-40 discussing embodiment machine learning algorithm),
the machine learning model suitable for accepting the plurality of sets of interaction data from the user as an input (uses feedback to train learning algorism, Col. 10, l. 57 – Col. 11, l. 10)
and functioning to produce a modeled preference data for the user as an output (model produces hypotheses for candidate compatibilities, Col. 10, l. 21-56 and Fig. 4),
the developing including preparing the plurality of sets of interaction data for use in training and testing the model, the preparing including splitting the data into training data for training the model, and testing data for testing the model (uses feedback and recommendations that resulted in the interaction are used to train learning algorism, Col. 10, l. 57 – Col. 11, l. 10; see also Col. 11, ll. 30-51 discussing refining model);
training the model with the training data, the training including selecting training variables (trains learning algorithm, e.g., Col. 5, ll. 6-18; Col. 10, ll. 21-56);
improving the performance of the machine learning model, the improving including adjusting the variables in the training to improve the output of the model (refines model, Col. 11, ll. 30-51);
sending the modified pool of candidate partners to the pooling module for use in a next iteration of the system (provides recommendations to date seeker, Co. 10, ll. 34-40 and Fig. 4)
generating a modeled preference data for the user through the iterative application of the model to optimize the candidate partners for a user interaction (process is iterative, Fig. 4 and Col. 10, l. 57 – Col. 11, l. 9).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
However the combination of Margines and Shoemaker does not teach but Storment does teach:
testing the model with the testing data to evaluate the utility of the output (tests machine learning model using a control data set, ¶[0018])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking with learning-based recommendations of Margines and Shoemaker with the testing of Storment because the use of a known technique to improve similar devices is obvious, see MPEP 2143.I.C. That is, one of ordinary skill would have recognized that when training machine learning models, e.g., as in Margines and Shoemaker, it is useful to test the trained model, i.e., as in Storment.
Regarding claim 9, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Shoemaker further teaches:
wherein the modeling engine trains and tests the model using interaction data from a plurality of users and a respective plurality of sets of interaction data from the plurality of users (uses feedback to train learning algorism, Col. 10, l. 57 – Col. 11, l. 10).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Regarding claim 10, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 9 and Margines further teaches:
wherein the pooling module is configured to function such that exclude the second user from the candidate pool of the first user when the candidate partners for the second user do not include the first user; and, exclude the first user from the candidate pool of the second user when the candidate partners for the first user do not include the second user (filters suers based on user specified parameters, ¶[0081]).
Regarding claim 11, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Margines further teaches:
an assessment module on a non-transitory computer readable medium operable for providing initial match scores to the user for selecting interactions to pursue, the initial match scores including a numerical assessment that provides a measure-of-fit of each candidate to the set of criteria (displays potential partners and match scores, ¶¶[0100]-[0103] and Fig. 13).
Regarding claim 12, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Margines further teaches:
an avatar module, the avatar module instructing the processor to create avatars designed or selected by users of the system (profile includes photo, ¶[0128]; see also Fig. 13 showing profile pictures).
Regarding claim 13, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Margines further teaches:
wherein the representation module is configured for forming an image of the Pvi for the user based on the initial preference data (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
Regarding claim 14, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Margines further teaches:
wherein the representation module is configured for forming an image of the Pvi for the user based on the initial preference data (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
Regarding claim 15, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 8 and Margines further teaches:
wherein the representation module is configured for forming an image of the Pvmn (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
However Margines does not teach but Shoemaker does teach:
where n ranges from 1 to T, and T is the total number of iterations of the model (process is iterative, Fig. 4 and Col. 10, l. 57 – Col. 11, l. 9);
the modifying including creating each Pvmn from modeled preference data output by the model at each iteration (updates hypotheses to refine future recommendations, Col. 10, ll. 41-56 and Fig. 4).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Regarding claim 16, the combination of Margines, Shoemaker and Storment teaches all the limitations of claim 15 and Margines further teaches:
wherein the representation module is configured for forming an image of PvmT (provides data on ideal partner as bar graph, ¶[0078] and Fig. 8).
Claim(s) 18, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Storment in view of Margines, further in view of Shoemaker.
Regarding claim 18, the combination of Storment and Margines teaches all the limitations of claim 17 and Margines further teaches
an advertising module (recommends products to users, ¶[0026]).
However the combination of Storment and Margines does not teach but Shoemaker
the advertising module instructing the processor to identify particular user location data and activity tracking data, and to target advertising to those particular users (recommends products based on user profile data, Col. 13, l. 45 – Col. 14, l. 14).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Storment and Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Storment and Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Regarding claim 19, the combination of Storment and Margines teaches all the limitations of claim 17 and does not teach but Shoemaker does teach:
a feedback module, the feedback module instructing the processor to receive and record feedback by one or more users of the system (receives and stores feedback on dates and matches, Col. 10, ll. 41-56 and Fig. 4).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Storment and Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Storment and Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Regarding claim 21, the combination of Storment and Margines teaches all the limitations of claim 17 and does not teach but Shoemaker does teach:
a ratings module, the ratings module instructing the processor to measure or assess relative levels of interest in other users (receives and stores feedback on dates and matches, Col. 10, ll. 41-56 and Fig. 4).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the social matchmaking of Storment and Margines with the learning-based recommendations of Shoemaker because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the machine learning based recommendations of Storment and Margines would likely be improved by refining the model using feedback from participants, i.e., as taught by Shoemaker and accordingly would have modified Margines to incorporate the teachings of Shoemaker.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Levy, Jon, Devin Markell, and Moran Cerf. "Polar similars: Using massive mobile dating data to predict synchronization and similarity in dating preferences." Frontiers in Psychology 10 (2019): 2010. Teaches a similar system
Huang et al, US Pub. No. 2019/0080012 teaches a similar system
Thompson et al, US Pub. No. 2004/0210661 teaches a similar system
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626