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 .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-20 are currently pending and have been considered below.
Claim Objections
Claim 19 is objected to because of the following informality: Claim 19, line 2 recites “he” and it should be – the --. Appropriate correction is required.
Claim 20 is objected to because of the following informality: Claim 20, line 1 recites “A non-transitory computer-readable medium stoting” and it should be – A non-transitory computer-readable medium storing --. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-14 are directed to a method (i.e., a process). Claims 15-19 are directed to a system (i.e. a machine). Claim 20 is directed to a non-transitory computer-readable medium (i.e. an article of manufacture). Thus, each of these claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1 recites: A method for analyzing and selecting compatible relationship pairings between users in a relationship analytics platform, the method comprising the steps of: determining the analytic score of a plurality of users within the relationship analytics platform; determining the analytic score difference between a plurality of users with the relationship analytics platform; determining the relational compatibility between a plurality of users within the relationship analytics platform; generating analytic compatibility pairings between a plurality of users within the relationship analytics platform; and determining the ranking of at least two analytic compatibility pairings between a plurality of users within the relationship analytics platform. Independent claim 15 recites: A system for analyzing and selecting compatible relationship pairings between users in a relationship analytics platform, the method comprising the steps of:
receiving analytic score data of at least two users within the relationship analytics platform; identifying the analytic score difference between at least two users with the relationship analytics platform; identifying the relational compatibility between at least two users within the relationship analytics platform; generating analytic compatibility pairings between at least two users within the relationship analytics platform; and generating the ranking of at least two analytic compatibility pairings within the relationship analytics platform.
Independent claim 20 recites: receive analytic score data of at least two users within the relationship analytics platform; identify the analytic score difference between at least two users with the relationship analytics platform; identify the relational compatibility between at least two users within the relationship analytics platform; generate analytic compatibility pairings between at least two users within the relationship analytics platform; and generate the ranking of at least two analytic compatibility pairings within the relationship analytics platform.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of:
Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As the claims discuss analyzing and selecting compatible relationship pairings between users, determining the analytic score of a plurality of users within the relationship analytics platform; and determining the relational compatibility between a plurality of users, which is one of certain methods of organizing human activity.
Mathematical concepts (mathematical relationships, mathematical formulas or equations and mathematical calculations (as independent claim 1 recites: “determining the analytic score“, “determining the analytic score difference”, “determining the relational compatibility”, “generating analytic compatibility pairings”, “determining the ranking of at least two analytic compatibility pairings”; independent claim 15 recites: “analytic score data”, “identifying the analytic score difference”, “identifying the relational compatibility”, “generating analytic compatibility pairings”, “generating the ranking of at least two analytic compatibility pairings”; independent claim 20 recites: “receive analytic score data”, “identify the analytic score difference”, “generate analytic compatibility pairings”, “generate the ranking of at least two analytic compatibility pairings”)).
Dependent claims add additional limitations, for example: (claim 2) whereby the analytic score and relational compatibility are numerical (claim 3) whereby the numerical representations of the analytic score and relational compatibility may be determined by a series of computations derived from empirical data, user input data, and quantifiable metric data, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of organizing human activity and mathematical concepts but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – processor(s), data storage, a non-transitory computer-readable medium. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds 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, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, dependent claims have been fully analyzed to determine whether there are additional limitations recited that amount to significantly more than the abstract idea. The claims recite additional limitations, for example: (claim 2) whereby the analytic score and relational compatibility are numerical (claim 3) whereby the numerical representations of the analytic score and relational compatibility may be determined by a series of computations derived from empirical data, user input data, and quantifiable metric data. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to an abstract idea. The claims are ineligible. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over OZOKA et al. (US 2020/0356884 A1), hereinafter “Ozoka”, over Herbst et al. (US 2017/0300935 A1), hereinafter “Herbst”.
Regarding Claim 1, Ozoka teaches a method for analyzing and selecting compatible relationship pairings between users in a relationship analytics platform, the method comprising the steps of: (Ozoka, para 0002 and 0014, teaches a method to predict an affinity between a pair of user profiles based on behavioral data analytics; para 0015 teaches a behavioral analytics platform to identify matches);
determining the analytic … within the relationship analytics platform; (Ozoka, para 0032, the behavioral analytics platform may use a scoring system (e.g., with relatively high scores and/or relatively low scores) to identify and/or classify users as being associated with one or more behavioral categories, sub-categories, and/or the like.);
determining the analytic score difference between a plurality of users with the relationship analytics platform; (Ozoka, para 0043, a first user profile containing one or more attributes or sets of attributes that are within a threshold difference of one or more attributes or sets of attributes in a second user profile.);
determining the relational compatibility between a plurality of users within the relationship analytics platform; (Ozoka, para 0018, determine an affinity between a particular pair of users (e.g., a likelihood that the pair of users are potentially compatible));
generating analytic compatibility pairings between a plurality of users within the relationship analytics platform; and (Ozoka, para 0019, the behavioral analytics platform may identify potential matches for a user of a particular user device (e.g., potential romantic partners selected by a suitable algorithm, such as a machine learning algorithm), and filter or rank the potential matches based on whether the behavioral categories in which the potential matches are classified are similar, dissimilar, compatible, incompatible, complementary, and/or the like.);
determining the ranking of at least two analytic compatibility pairings between a plurality of users within the relationship analytics platform (Ozoka, para 0044, the behavioral analytics platform may filter and/or rank the potential matches).
Ozoka does not appear to explicitly teach and in the same field of endeavor Herbst teaches score of a plurality of users (See at least Herbst, Abstract, teaches a computing system for matching daters; para 0015 and Figure 4, teaches a particular rater's rating and an Individual Score). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ozoka with score of a plurality of users as taught by Herbst with the motivation to have a dating system that builds on the positive aspects of real-life dating (Herbst, para 0007).The Ozoka invention now incorporating the Herbst invention, has all the limitations of claim 1.
Regarding Claim 2, Ozoka, now incorporating Herbst, teaches the method of claim 1, whereby the analytic score and relational compatibility are numerical (Ozoka, para 0045, the behavioral analytics platform may represent the affinity between the behavioral categories and/or sub-categories associated with different users according to a numeric value).
Regarding Claim 3, Ozoka, now incorporating Herbst, teaches The method of claim 2, whereby the numerical representations of the analytic score and relational compatibility may be determined by a series of computations derived from empirical data, user input data, and quantifiable metric data (Ozoka, para 0074, input component includes a component to receive information, such as via user input; para 0039, the behavioral analytics platform may use a supervised multi-label classification technique to train the data model. (Examiner notes is a series of computations) For example, as a first step, the behavioral analytics platform may map attributes associated with certain behavioral categories (Examiner notes empirical data); As a third step, the behavioral analytics platform may determine a Hamming Loss Metric (Examiner notes quantifiable metric data) relating to an accuracy of a label).
Regarding Claim 4, Ozoka, now incorporating Herbst, teaches The method of claim 2, whereby the numerical representation of the analytic score and relational compatibility may be received as at least one sequence of data, wherein said sequence of data may be maintained in a historical database (Ozoka, para 0025, para 0045, teaches numeric value; para 00047, teaches from a storage device, historical user activity data contained in a first user profile and historical user activity data contained in a second user profile).
Regarding Claim 5, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby an analytic score of the plurality of users within the relationship analytics platform may be determined by at least one categorical data input the plurality of users (See at least Ozoka, para 0003, categories to classify the historical user activity data).
Regarding Claim 6, Ozoka, now incorporating Herbst, teaches The method of claim 5, whereby the at least one categorical data input is user self- assessment data. (Ozoka, para 0020, the behavioral analytics platform may enable the user to provide questionnaire responses).
Regarding Claim 7, Ozoka, now incorporating Herbst, teaches The method of claim 5, whereby the at least one categorical data input is user self- awareness data. (Ozoka, para 0020, the behavioral analytics platform may enable the user to provide certain self-reported personal details and preferences, which may be validated, augmented, or otherwise processed in connection with user activity data related to one or more transactions performed by the user; para 0048, if a user indicates that he/she loves to read and travel, but the transactional data contains few records of the user purchasing books, visiting libraries, conducting transactions in areas away from the user's home, and/or the like, the behavioral analytics platform may determine that the user has (intentionally or unintentionally) provided misleading or inaccurate self-reported information.).
Regarding Claim 8, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby the analytic score divergence between a plurality of users may be determined by the calculation of the numerical difference between the analytic score of at least one user as compared to the analytic score of the remaining individual users (Ozoka, para 0043, attributes that are within a threshold difference; para 0045, from 0 to 100, where 0 is used to represent completely incompatible users and 100 is used to represent perfectly compatible user; users may be classified in multiple different categories and/or sub-categories and a weight may be assigned to indicate a degree to which behavioral tendencies of the users align with particular categories and/or sub-categories).
Regarding Claim 9, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby relational compatibility between a plurality of users may be determined by the divergence from the preferred numeric representation of relational compatibility an individual user and the analytic score. (Ozoka, para 0045, from 0 to 100, where 0 is used to represent completely incompatible users and 100 is used to represent perfectly compatible user (Examiner notes 100 is preferred numeric representation)).
Regarding Claim 10, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby the analytic compatibility pairings between a plurality of users within the relationship analytics platform may be generated from the selection of users having a pre-determined numerical representation of divergence in analytic score and relational compatibility (Ozoka, para 0014, artificial intelligence techniques (e.g., machine learning, deep learning, and/or the like) to predict an affinity between a pair of user profiles based on behavioral data analytics.)
Regarding Claim 11, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby the generated analytic compatibility pairings between a plurality of users within the relationship analytics platform may be searched by an individual user. (Ozoka, para 0042, provide an online dating service and the match request may be in a context that relates to searching for potential romantic partners).
Regarding Claim 12, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby the ranking (Ozoka, para 0044, the behavioral analytics platform may filter and/or rank the potential matches) of at least two analytic compatibility pairings between a plurality of users may be determined by calculating the analytic score difference, (Ozoka, para 0043, a first user profile containing one or more attributes or sets of attributes that are within a threshold difference) the relational compatibility of at least two analytic compatibility pairings, (Ozoka, para 0045, from 0 to 100, where 0 is used to represent completely incompatible users and 100 is used to represent perfectly compatible users) and the calculation of metric-based quantifiable data of at least one analytic compatibility (Ozoka, para 0039, the behavioral analytics platform may use a supervised multi-label classification technique to train the data model; the behavioral analytics platform may determine a Hamming Loss Metric (Examiner notes quantifiable data)) pairing as compared to at least one other analytic compatibility pairing (Ozoka, para 0015, the behavioral analytics platform may identify one or more matches for the particular user based on affinities between the behavioral categories, sub-categories, and/or the like associated with the particular user and behavioral categories, sub-categories, and/or the like in which other users are classified).
Regarding Claim 13, Ozoka, now incorporating Herbst, teaches The method of claim 1, whereby the analytic score and analytic score difference may be recalculated following the categorization of user-driven numerical input data, thereby regenerating analytical compatibility pairings and the ranking of at least two analytic compatibility pairings between a plurality of users within the relationship analytics platform (Ozoka, para 0034, teaches behavioral analytics platform may perform a training operation when generating the data model based on the observed user behaviors; para 0049, if users that communicate with one another meet one another in the real world, enter into a short-term or long-term relationship, and/or the like, the users may report this information to the behavioral analytics platform to enable the behavioral analytics platform to further update the data models to indicate whether certain behavioral categories, sub-categories, tendencies, and/or the like are complementary, compatible, incompatible, and/or the like.)
Regarding Claim 14, Ozoka, now incorporating Herbst, teaches The method of claim 12, whereby the user selects the preferred analytic compatibility pairing (Ozoka, para 0020, the behavioral analytics platform may enable the user to establish a user profile (e.g., providing certain self-reported personal details, preferences)
Regarding Claim 15, Ozoka teaches a system for analyzing and selecting compatible relationship pairings between users in a relationship analytics platform, the method comprising the steps of: one or more processors; and a data storage coupled to one or more processors, having instructions stored thereon which, when executed by at least one processor, causes the one or more processors to perform operations comprising: (Ozoka, para 0002 and 0014, teaches a method to predict an affinity between a pair of user profiles based on behavioral data analytics; Figure 3 and para 0071-0072, teaches processor(s) and storing data);
receiving analytic … within the relationship analytics platform; (Ozoka, para 0032, the behavioral analytics platform may use a scoring system (e.g., with relatively high scores and/or relatively low scores) to identify and/or classify users as being associated with one or more behavioral categories, sub-categories, and/or the like.);
identifying the analytic score difference between at least two users with the relationship analytics platform; (Ozoka, para 0043, a first user profile containing one or more attributes or sets of attributes that are within a threshold difference of one or more attributes or sets of attributes in a second user profile.); identifying the relational compatibility between at least two users within the relationship analytics platform; (Ozoka, para 0018, determine an affinity between a particular pair of users (e.g., a likelihood that the pair of users are potentially compatible));
generating analytic compatibility pairings between at least two users within the relationship analytics platform; and (Ozoka, para 0019, the behavioral analytics platform may identify potential matches for a user of a particular user device (e.g., potential romantic partners selected by a suitable algorithm, such as a machine learning algorithm), and filter or rank the potential matches based on whether the behavioral categories in which the potential matches are classified are similar, dissimilar, compatible, incompatible, complementary, and/or the like.);
generating the ranking of at least two analytic compatibility pairings within the relationship analytics platform (Ozoka, para 0044, the behavioral analytics platform may filter and/or rank the potential matches).
Ozoka does not appear to explicitly teach and in the same field of endeavor Herbst teaches score data of at least two users (See at least Herbst, Abstract, teaches a computing system for matching daters; para 0015 and Figure 4, teaches a particular rater's rating and an Individual Score). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ozoka with score data of at least two users as taught by Herbst with the motivation to have a dating system that builds on the positive aspects of real-life dating (Herbst, para 0007).The Ozoka invention now incorporating the Herbst invention, has all the limitations of claim 15.
Regarding Claim 16, Ozoka, now incorporating Herbst, teaches The system of claim 15, whereby the numerical representations of the analytic score and relational compatibility may be received as at least one sequence of data, wherein said sequence of data may be maintained in a historical database (Ozoka, para 00047, teaches from a storage device, historical user activity data contained in a first user profile and historical user activity data contained in a second user profile).
Regarding Claim 17, Ozoka, now incorporating Herbst, teaches The system of claim 16, wherein the system is configured to classify the at least one sequence of data and the system is respectively trained to use the at least one sequence of data as a training data set (Ozoka, para 0039, the behavioral analytics platform may use a supervised multi-label classification technique to train the data model.)
Regarding Claim 18, Ozoka, now incorporating Herbst, teaches The system of claim 15, whereby the system is configured to compute the divergence between the numerical representation and a second set of at least one other sequence of data, wherein the second set of at least one other sequence of data comprises the analytic score difference and relational compatibility of at least two users (Ozoka, para 0025, the behavioral analytics platform may be configured to identify behavior-oriented attributes in transactions that are included in the user activity data and map the behavior-oriented elements to a behavior-oriented feature space in which behavioral data is represented as behavior vectors or behavior sequences; para 0045, the behavioral analytics platform may represent the affinity between the behavioral categories and/or sub-categories associated with different users according to a numeric value (e.g., in a particular range, such as from 0 to 100, where 0 is used to represent completely incompatible users and 100 is used to represent perfectly compatible users).
Regarding Claim 19, Ozoka, now incorporating Herbst, teaches The system of claim 18, whereby the system analyzes the computed divergence between he numeric representation and the second set of at least one other sequence of data, and generates a compatibility pairing between at least two users within the relationship analytics platform based on the computed divergence (Ozoka, para 0025, the behavioral analytics platform may be configured to identify behavior-oriented attributes in transactions that are included in the user activity data and map the behavior-oriented elements to a behavior-oriented feature space in which behavioral data is represented as behavior vectors or behavior sequences; para 0043, the behavioral analytics platform may identify a set of potential matches for a requesting user based on various attributes in the user profile associated with the requesting user and attributes in one or more other user profiles that are determined to be similar, compatible, complementary, and/or the like).
Regarding Claim 20, Ozoka teaches a non-transitory computer-readable medium stoting program that causes a processor to: (Ozoka, para 0004, a non-transitory computer-readable medium; Figure 3 and para 0071-0072, teaches processor(s));
receive analytic … within the relationship analytics platform; (Ozoka, para 0032, the behavioral analytics platform may use a scoring system (e.g., with relatively high scores and/or relatively low scores) to identify and/or classify users as being associated with one or more behavioral categories, sub-categories, and/or the like.);
identify the analytic score difference between at least two users with the relationship analytics platform; (Ozoka, para 0043, a first user profile containing one or more attributes or sets of attributes that are within a threshold difference of one or more attributes or sets of attributes in a second user profile.);
identify the relational compatibility between at least two users within the relationship analytics platform; (Ozoka, para 0018, determine an affinity between a particular pair of users (e.g., a likelihood that the pair of users are potentially compatible));
generate analytic compatibility pairings between at least two users within the relationship analytics platform; and (Ozoka, para 0019, the behavioral analytics platform may identify potential matches for a user of a particular user device (e.g., potential romantic partners selected by a suitable algorithm, such as a machine learning algorithm), and filter or rank the potential matches based on whether the behavioral categories in which the potential matches are classified are similar, dissimilar, compatible, incompatible, complementary, and/or the like.);
generate the ranking of at least two analytic compatibility pairings within the relationship analytics platform (Ozoka, para 0044, the behavioral analytics platform may filter and/or rank the potential matches).
Ozoka does not appear to explicitly teach and in the same field of endeavor Herbst teaches score data of at least two users (See at least Herbst, Abstract, teaches a computing system for matching daters; para 0015 and Figure 4, teaches a particular rater's rating and an Individual Score). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ozoka with score data of at least two users as taught by Herbst with the motivation to have a dating system that builds on the positive aspects of real-life dating (Herbst, para 0007).The Ozoka invention now incorporating the Herbst invention, has all the limitations of claim 20.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Potarca, Gina “The demography of swiping right. An overview of couples who met through dating apps in Switzerland”, PLOS ONE, December 30, 2020, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243733
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached on Monday - Friday 8:30am - 5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
/R.R.N./Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629