DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/26/2026 has been entered.
Claims 1-24 are cancelled.
Claims 25-44 are pending.
Response to Arguments
Applicant’s arguments with respect to the rejections previously made and the amended claims filed on 1/26/2026 have been fully considered. In view of the claim amendments, the rejections are being updated accordingly.
Double Patenting
The double patenting rejections are maintained and the reason is set forth in the rejections below. See updated rejections for detail.
35 USC 101 Rejections
Applicant’s arguments have been fully considered.
In response to the arguments, it is submitted that the amended claims
recite a process comprising detecting behavior features indicate degrees of interest of matches between entities, determining a probability of relevance, dividing features values into regions with value partitioning, assigning each region of the partitioned regions with a value to define a function with mathematical symbols (T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri), training a ranking model using features defined by profile data and probability of relevant as well as minimizing a total loss, and ranking a potential match by applying the ranking model. The process also comprising the applying is based on vectors and that the ranking model minimizes a total loss based on a gradient descent method as recited in the dependent claims.
The claimed process is similar to a method of mathematic relationships directed to a series of mathematic relationships involving degrees of interest, probability of relevance, feature value division, usage of mathematical symbols to represent a partitioned region, total loss calculation and ranking data element.
E.g. the degree of interest is direct to mathematical relationship that indicates a quantity of interest by an entity. Similarly, the probability of probability of relevance, feature value division, usage of mathematical symbols to represent a partitioned region, total loss calculation and ranking are all directed to using mathematics involving numbers and numeric operations.
Plus, the claims explicitly recite mathematic relationships based on “value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri”, which explicitly supported by supported by para [0052] of the specification that the defined function by partitioning clearly involves a mathematical formula, which further emphasize that the claimed process is directed to a series of mathematic relationships.
In addition, the claimed steps with mathematic relationships do not render the claims to a practical application because the claimed process does not show how performing a series of mathematic relationships would direct the claimed process to a particular useful application.
Additionally, the element of “to rank a potential match” is directed to non-functional descriptive material that describes that intended outcome when the machine-learning model is applied, which not impose a meaningful limit on the judicial exception since the ranking step is not functionally involved to any practical application or outcome. Merely reciting the intended purpose as claimed would not integrate the abstraction idea into practical application.
Also, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. behavioral features, profiles, click feedback) are directed to types of information, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claims do not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such a computer readable storage medium, a memory, processor) in the claim amount to no more than usage of a generic computing system having a generic computing components, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Thus, for at least the reasoning above, the pending claims are not patent eligible.
35 USC 103 Rejections
Applicant’s arguments--which are primarily directed to the newly added limitations of “partitioning… assigning… training…entity” recited in claim 25 is not being disclosed by the cited prior art-- have been fully considered.
In response to the arguments, it is submitted that the newly added limitations are being properly addressed along with other limitations in the claim. The reason is set forth in the updated rejections; see rejection below for detail.
Furthermore, it is submitted that all limitations in pending claims--including those not specifically argued--are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 25-44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. U.S. Patent No. 9,449,282 (U.S. Application No 12/829,152).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-18 of U.S. Patent No. 9,449,282 anticipate or render obvious each limitation of claims of the instant application as demonstrated in the table below.
Claims 25-31 of instant application recite similar limitations as claim 32-44, hence claims 25-31 are being used as representative for demonstration in the table below.
Similarly, claims 1-6 of U.S. Patent No. 9,449,282 recite similar to limitations as claims 7-18. Hence claims 1-6 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 9,449,282
25. A method for a dating service including a plurality of candidate profiles, the plurality of candidate profiles including a profile of a first entity and a profile of a second entity, the method comprising:
detecting a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity;
28. The method as recited in claim 25, wherein the first behavioral feature pertains to a view of the profile of the first entity, and the second behavioral feature pertains to a view of the profile of the second entity.
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features; and
partitioning a space of feature values into regions R9, j=1, 2, ... , J;
assigning each of the regions a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri;
training a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs, and(ii) by minimizing a total loss, at least in part based on the first entity and the second entity: and
applying the ranking model to rank a potential match for a fourth entity.
26. The method as recited in claim 25, wherein the applying is based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity.
1. A method for ranking unlabeled matches…the method comprising:
transmitting…messages and profile views directed toward one of a second set of members of the dating website…receiving…behavioral features, the behavioral features comprising: a first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website…a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website…(Col. 14, lines 6-55: detecting behavior features for potential matches, the first density of profile views includes the first and second behavioral features indicating at least one way interest of first and second entities (e.g. a set second of members) by the third entity (e.g. first member), pertain viewing profile);
determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features….(Col. 14, lines 58-67: the behavioral features used for probability of relevance determination of potential matches represented by the label matches are pertain to view of profiles as shown above);
training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features…the ranking function…(Col. 15, lines 43-67: the boosted regression tree is partitioned into regions represented by respective node, each node is assigned with an identification value corresponding to the claimed assigned value. Also a ranking model is being trained via boosted tree based on different data, including features, entities and profile to rank at least one match using the different types of data);
calculating, by the web server, a rank for each of the unlabeled matches based on the probability of relevance of each of the unlabeled matches to generate a set of ranked matches… ranked matches to the first mobile device associated with the first member of the dating website (Col. 16, lines 8-14: applying ranking model to calculating rank for potential match represented by the unlabeled match).
27. The method as recited in claim 26, wherein the ranking model ranks the potential match for the fourth entity, based at least in part on a vector of the fourth entity and at least one of the regions.
30. The method as recited in claim 25, wherein the ranking model is trained to predict features that correlate with relevance.
training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features…the ranking function…(Col. 15, lines 43-67: training boosted regression trees including using features vectors indicating the features being applied, including feature of profile as shown above since vector is being used in learning).
Claim 6 recites applying features in the boosted regression trees (Col. 16, lines 66-67, Col. 7, lines 1-14).
29. The method as recited in claim 25, wherein the first and second behavioral features do not indicate click feedback from the first entity, the second entity, or the third entity.
receiving…the behavioral features comprising…a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website… determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features…(Col. 14, lines 36-67: features of other entities representing by members are being used in the determination),
Claims 2-6 reciting different feature of the first and second entities being used in probability of relevance determination (Co. 16, lines 15-65)
receiving…the behavioral features comprising…profile views….messages sent...exchange phone number…one another (Col. 14, lines 25-57: those behavior features do not provide click feedback).
applying a machine-learned ranking model to rank a potential match for a fourth entity, wherein the ranking model is trained (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs, and
(ii) by minimizing a total loss, at least in part based on the first entity and the second entity.
31. The method as recited in claim 25, wherein the ranking model minimizes the total loss, based on a gradient descent method.
training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features…the ranking function…ranked matches(Col. 15, lines 43-67, Col. 16, lines 1-10: applying and training ranking model such as the boosted regression trees based on probability to predict features that correlate, such as the ranking or behavioral features as described in claims 2-5, Col. 16, lines 15-65);
6. The method as recited in claim 1, wherein training the boosted regression trees comprising….summing the reduction in the loss function….matches (Col. 17, lines 1-10: reduction in loss).
As demonstrated in the table above, U.S. Patent No. 9,449,282 discloses or renders obvious all the features of the claims of the instant application.
Claims 25-44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14of U.S. Patent No. U.S. Patent No. 10,380,158 (U.S. Application No 15/231,181).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-14 of U.S. Patent No. 10,380,158 anticipate or render obvious each limitation of claims of the instant application as demonstrated in the table below.
Claims 25-31 of instant application recite similar limitations as claim 32-44, hence claims 25-31 are being used as representative for demonstration in the table below.
Similarly, claims 1-5 of U.S. Patent No. 10,380,158 recite similar to limitations as claims 6-14. Hence claims 1-5 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 10,380,158
25. A method for a dating service including a plurality of candidate profiles, the plurality of candidate profiles including a profile of a first entity and a profile of a second entity, the method comprising:
28. The method as recited in claim 25, wherein the first behavioral feature pertains to a view of the profile of the first entity, and the second behavioral feature pertains to a view of the profile of the second entity.
29. The method as recited in claim 25, wherein the first and second behavioral features do not indicate click feedback from the first entity, the second entity, or the third entity.
detecting a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity;
determining a probability of relevance of determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features; and
training a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs, and(ii) by minimizing a total loss, at least in part based on the first entity and the second entity: and
applying the ranking model to rank a potential match for a fourth entity.
1. A method comprising:
…retrieving….wherein the behavioral features comprise: click feedback from either of the dating profile and the candidate dating profile, a first duration of profile views initiated by the dating profile toward the candidate dating profile, and a second duration of profile views initiated by the candidate dating profile toward the dating profile (Col. 14, lines 15-32: different types of behavioral features are being detected for respective unlabeled match when determined that the unlabeled matches lack behavioral features, including profile viewing each behavioral feature indicates at least one way interest in another entity represented by the profile. Since there are multiple matches, each with a pair, hence there are different entities including the first, second and third in matches of the dating website); and
identifying relevant …wherein each of the labeled matches is labeled with a probability of relevance based on the behavioral features of the two dating profiles, wherein: the probability of relevance … to the first dating profile (Col. 14, lines 33-58: determining a probability of relevance for each match based on the behavior features that are related to view of profile on a dating service of the dating website as described above);
Claim 5. The method of claim 1, wherein the probability of relevance is one for each relevant match and the probability of relevance is zero for each non-relevant match.
generating…a ranking model that correlates the probability of relevance to ranking features …. wherein the ranking model is configured … relevance (Col. 14,lines 58-63: asserting parameter of the raking model when generating the ranking model using the probability of relevance of the matches. The ranking model is a trained model);
Claim 4. The method of claim 1….ranking function…based on one or more regression trees (Col. 15,lines 24-26: regression trees are directed to a machine learned model, which is a trained mode for ranking data).
calculating the probability of relevance of each of the unlabeled matches…into the ranking model…calculating a ranking or each of the unlabeled matches…(Col. 14, lines 64-67, Col. 15, lines 1-9: rank calculation is being preformed by applying the ranking model to rank a potential match for the fourth entity represented by a unlabeled match).
26. The method as recited in claim 25, wherein the applying is based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity.
27. The method as recited in claim 26, wherein the ranking model ranks the potential match for the fourth entity, based at least in part on a vector of the fourth entity and at least one of the regions.
2. The method of claim 1, further comprising… wherein the ranking features comprise a vector of attributes from the dating profile and the candidate dating profile.
3.The method of claim 1, further comprising… wherein the ranking features comprise a vector of attributes from the dating profile and the candidate dating profile.
(Col. 15, lines 15-23: apply feature vector represented by the vector of attribute indicating the features of the entity represented by the profiles in the applying to rank. The feature vectors together make up a feature space. Hence each feature represents a portion of the feature space, such that the feature space is being partitioned by a function, and that each region of the space is assigned with a value).
retrieve unlabeled matches…attributes…identifying relevant …wherein each of the labeled matches is labeled with a probability of relevance based on the behavioral features of the two dating profiles…(Col. 14, lines 20-32 & 43-55: the probability of relevance is determine based on features/attributes of the entities).
retrieve unlabeled matches…attributes…identifying…did not send a message (Col. 14, lines 48-60: provide no feedback).
partitioning a space of feature values into regions R9, j=1, 2, ... , J;
assigning each of the regions a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri.
30. The method as recited in claim 25, wherein the ranking model is trained to predict features that correlate with relevance.
31. The method as recited in claim 25, wherein the ranking model minimizes the total loss, based on a gradient descent method.
4. The method of claim 1….ranking function…based on one or more regression trees (Col. 15,lines 24-26 the boosted regression tree is partitioned into regions represented by respective node, each node is assigned with an identification value corresponding to the claimed assigned value. Also the one or more regression trees is correspond to a gradient decent method).
As demonstrated in the table above, U.S. Patent No 10,380,158 discloses or renders obvious all the features of the claims of the instant application.
Claims 25-44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. U.S. Patent No. 11,989,220 (U.S. Application No 16/538,090).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-15 of U.S. Patent No. 11,989,220 anticipate or render obvious each limitation of claims of the instant application as demonstrated in the table below.
Claims 25-31 of instant application recite similar limitations as claim 32-44, hence claims 25-31 are being used as representative for demonstration in the table below.
Similarly, claims 1-5 of U.S. Patent No. 11,989,220 recite similar to limitations as claims 6-15. Hence claims 1-5 are being used as representative for demonstration in the table below.
Instant Application
U.S. Patent No. 11,989,220
25. A method for a dating service including a plurality of candidate profiles, the plurality of candidate profiles including a profile of a first entity and a profile of a second entity, the method comprising:
detecting a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity;
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features; and
training a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs, and(ii) by minimizing a total loss, at least in part based on the first entity and the second entity: and
applying the ranking model to rank a potential match for a fourth entity.
partitioning a space of feature values into regions R9, j=1, 2, ... , J;
assigning each of the regions a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri;
26. The method as recited in claim 25, wherein the applying is based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity.
27. The method as recited in claim 26, wherein the ranking model ranks the potential match for the fourth entity, based at least in part on a vector of the fourth entity and at least one of the regions.
1. A method, comprising:
detecting a first behavioral feature for a first potential match for a first entity and a second behavioral feature for a second potential match for a second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity;
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features, wherein the first behavioral feature pertains to a view of a profile of the first entity on a dating service, and the second behavioral feature pertains to a view of a profile of the second entity on the dating service, the profile of the first entity and the profile of the second entity included in a plurality of candidate profiles on the dating service;
training a machine-learned ranking model (i) using a subset of features defined by the candidate profiles only and the probability of relevance of each of the first and second potential matches as inputs, and (ii) by minimizing a total loss, at least in part based on the first entity and the second entity; and
applying the ranking model to rank a potential match for a fourth entity, based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity, wherein the applying is performed at least in part by partitioning a space of feature values into regions (The e feature vectors together make up a feature space. Hence each feature represents a portion of the feature space, such that the feature space is being partitioned by a function, and that each region of the space is assigned with a value).
28. The method as recited in claim 25, wherein the first behavioral feature pertains to a view of the profile of the first entity, and the second behavioral feature pertains to a view of the profile of the second entity.
2. The method as recited in claim 1, wherein the determining is based at least in part on features of the first entity or the second entity.
29. The method as recited in claim 25, wherein the first and second behavioral features do not indicate click feedback from the first entity, the second entity, or the third entity.
3. The method as recited in claim 1, wherein the first and second behavioral features do not provide click feedback from the first entity, the second entity, or the third entity.
30. The method as recited in claim 25, wherein the ranking model is trained to predict features that correlate with relevance.
4. The method as recited in claim 1, wherein the training is to predict features that correlate with relevance.
31. The method as recited in claim 25, wherein the ranking model minimizes the total loss, based on a gradient descent method.
5. The method as recited in claim 1, wherein the ranking model minimizes the total loss, based on a gradient descent method.
As demonstrated in the table above, U.S. Patent No 11,989,220 discloses or renders obvious all the features of the claims of the instant application.
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 25-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The amended claims 25-44 recite a process comprising detecting behavior features indicate degrees of interest of matches between entities, determining a probability of relevance, dividing features values into regions with value partitioning, assigning each region of the partitioned regions with a value to define a function with mathematical symbols (T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri), training a ranking model using features defined by profile data and probability of relevant as well as minimizing a total loss, and ranking a potential match by applying the ranking model. The process also comprising the applying is based on vectors and that the ranking model minimizes a total loss based on a gradient descent method as recited in the dependent claims.
The claimed process is similar to a method of mathematic relationships, which is one of the groupings of abstract ideas according to Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance since the claimed steps are directed a series of mathematic relationships involving degrees of interest, probability of relevance, feature value division, usage of mathematical symbols to represent a partitioned region, total loss calculation and ranking data element.
E.g. the degree of interest is direct to mathematical relationship that indicates a quantity of interest by an entity. Similarly, the probability of probability of relevance, feature value division, usage of mathematical symbols to represent a partitioned region, total loss calculation and ranking are all directed to using mathematics involving numbers and numeric operations.
Plus, the claims explicitly recite mathematic relationships based on “value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri”, which explicitly supported by supported by para [0052] of the specification that the defined function by partitioning clearly involves a mathematical formula, which further emphasize that the claimed process is directed to a series of mathematic relationships.
In addition, the claimed steps with mathematic relationships do not render the claims to a practical application because the claimed process does not show how performing a series of mathematic relationships would direct the claimed process to a particular useful application.
Additionally, the element of “to rank a potential match” is directed to non-functional descriptive material that describes that intended outcome when the machine-learning model is applied, which not impose a meaningful limit on the judicial exception since the ranking step is not functionally involved to any practical application or outcome. Merely reciting the intended purpose as claimed would not integrate the abstraction idea into practical application.
Also, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. behavioral features, profiles, click feedback) are directed to types of information, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claims do not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such a computer readable storage medium, a memory, processor) in the claim amount to no more than usage of a generic computing system having a generic computing components, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Thus, for at least the reasoning above, the pending claims are not patent eligible.
Examiner Comments
The term “for” in the claims (e.g. claims 25, 32, 38) indicates intended use; Minton v. Nat ’l Ass ’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003) “whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.” Examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) “adapted to” or “adapted for” clauses; (B) “wherein” clauses; and (C) “whereby” clauses. Therefore intended use limitations are not required to be taught, see MPEP 2103, 2106 Section II(C), MPEP 2111.04 [R-3]).
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 25-44 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Hueter et al (Pub No US 2009/024862, hereinafter Hueter) in view of Liu et al (Pub No. US 2009/0106222, hereinafter Liu).
Heuter and Lin are cited in the previous office action.
With respect to claim 25, Hueter discloses a method for a dating service including a plurality of candidate profiles, the plurality of candidate profiles including a profile of a first entity and a profile of a second entity (abstract, [0027-0028], Fig 5: a method for a dating service application with multiple profiles), the method comprising:
detecting a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity (elements on indicating degree of one way interest appears to be directed to non-functional descriptive material for not functionally impacting the structure of the claim because the steps in the claim would be performed the same regardless of the indicated degree of one-way interest ; [0012-0014], [0023-0024], [0043],Fig 2-5: detect behavioral features based on a subject of an active entity’s behaviors toward other subjects representing other entities for respective potential match. The detected behavioral features including and not limited to explicit and inferred/derived behavior features of the active entity that represents the 3rd entity. The explicit behavioral features include explicit input or feedbacks of relevancy indicating a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Inferred/derived behavior features include implicit feedback—including and not limited to non-selection of search results--which also indicate a degree of a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Each search result represents an entity. Hence a 1st and 2nd entities are being represented by two search results among the plurality of search results being presented. Since the plurality of search results being presented and the active entity are being tracked, hence a first behavior feature for a first potential match and a 2nd behavior features for a second potential match are being detected upon explicit and inferred/derived behaviors of the active entity);
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features ([0012-0015], [0020-0024], [0031], [0043], Fig 2-5: determine probability of relevance based behavioral features when training a learning method and/or when determine relevance of one or more matches, which are based on the behavior features in view of input or lack of input of the 3rd entity. Also the scores such as the relevance/behavioral score may be corresponding to the probability of relevance as well since it is also based on the behavioral features as well);
partitioning a space of feature values into regions ([0042-0044], [0046-0047], Fig 4: the partitioning a space of presented search results representing the feature values into at least two 2 regions according to feedback, e.g. a region of selected result, and a region of unselected region, and/or a region of relevant and a region of not relevant);
assigning each of the regions a value ([0042-0044], [0046-0047], Fig 4: assigning each region a value, e.g. a value of selected or unselected, and/or a value of relevant or not relevant);
training a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs ([0030-0031],[0042-0046], Fig 4-6: training a ranking model at least in part is based on profile features and the probability of relevance as the profiles features are being used matching processing to generate return results representing the entities including the 1st and 2nd entities as described in [0030] & [0042], and then used to update entity profiles based on feedback for the subsequent matches that includes a potential match for a fourth entity indicated by subsequent search results as described in [0046]));
applying the ranking model to rank a potential match for a fourth entity [0030-0031],[0042-0046], Fig 4-6: apply ranking model to rank potential matches-- such as match in the subsequent search for the 4th entity of the potential matches which is the intended use entity of the ranking model application and does not necessary carry any patentable weight).
Hueter does not explicitly discloses R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions;
training the machine-learned ranking model by minimizing a total loss, at least in part based on the first entity and the second entity as claimed.
However, Liu discloses training a machine-learned ranking model by minimizing a total loss, at least in part based on a first entity and a second entity ([0021-0023]: training a machine learning ranking model represented by a ranking function by minimizing a total loss a first and second entities with a usage of a loss function); and
partitioning a space of feature values into regions and assigning each region a value that involve usage of different symbol values such as j=1, 2, ...n(i) ([0020-0022], [0041], [0055-0061]: partition a space represented by a list of features vectors each with an assigned vales, such that the math equation is defined using different mathematical symbol values to rank potential matches)
Since (i) Hueter further discloses the training of ranking model adopts function to minimize squared error, and (ii) Hueter and Liu are from the same field of because both are directed to determine matches between entities based on associated feature indicators, it would have been obvious to one skilled in the art at the time of the invention to modify the error computation & ranking model application of Hueter and incorporate the total loss computation & ranking model application of Liu in ranking model training of teachings of Hueter for minimizing loss and to rank potential match as claimed. The motivation to combine is to provide intelligently rank matches resulting highly relevant information provision for users (Hueter, [0001]; Liu, [0003]).
Liu does not explicitly disclose R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions as claimed.
However, this difference is only found in the nonfunctional descriptive data materials that describe (i) data labels of R9, j=1, 2, ... , J for the partitioned regions, (ii) a data value assigned to each of the regions, and (iii) an intended use of region value assignment that is directed to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions. Neither of data label (or data value) nor the intended use is functionally involved in the steps recited. All the steps in the claims (e.g. detecting, determining, partitioning, assigning, training, and apply) would be performed the same regardless of what the data labels for the regions and region values are. Similarly, all steps in the claims would be performed the same regardless of intended use of the region value assignment. None of impact any of the functionalities of the claimed steps as all the steps would be performed the same to achieve the same outcome.
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to (i) use any data label for the partitions and (ii) assign any value to each region for any usage because such data material does not functionally relate to the steps in the method claimed. Also, it is because the subjective interpretation of the data does not patentably distinguish the claimed invention
With respect to claim 32, Hueter discloses a non-transitory, computer-readable medium storing thereon program instructions for performing operations for a dating service including a plurality of candidate profiles, the plurality of candidate profiles including a profile of a first entity and a profile of a second entity (Abstract, [0001], [0027-0028], Fig 5: operations of a method for a dating service application with multiple profiles), the operations comprising:
detecting a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity (elements on indicating degree of one way interest appears to be directed to non-functional descriptive material for not functionally impacting the structure of the claim because the steps in the claim would be performed the same regardless of the indicated degree of one-way interest ; [0012-0014], [0023-0024], [0043],Fig 2-5: detect behavioral features based on a subject of an active entity’s behaviors toward other subjects representing other entities for respective potential match. The detected behavioral features including and not limited to explicit and inferred/derived behavior features of the active entity that represents the 3rd entity. The explicit behavioral features include explicit input or feedbacks of relevancy indicating a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Inferred/derived behavior features include implicit feedback—including and not limited to non-selection of search results--which also indicate a degree of a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Each search result represents an entity. Hence a 1st and 2nd entities are being represented by two search results among the plurality of search results being presented. Since the plurality of search results being presented and the active entity are being tracked, hence a first behavior feature for a first potential match and a 2nd behavior features for a second potential match are being detected upon explicit and inferred/derived behaviors of the active entity);
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features ([0012-0015], [0020-0024], [0031], [0043], Fig 2-5: determine probability of relevance based behavioral features when training a learning method and/or when determine relevance of one or more matches, which are based on the behavior features in view of input or lack of input of the 3rd entity. Also the scores such as the relevance/behavioral score may be corresponding to the probability of relevance as well since it is also based on the behavioral features as well);
partitioning a space of feature values into regions ([0042-0044], [0046-0047], Fig 4: the partitioning a space of presented search results representing the feature values into at least two 2 regions according to feedback, e.g. a region of selected result, and a region of unselected region, and/or a region of relevant and a region of not relevant);
assigning each of the regions a value ([0042-0044], [0046-0047], Fig 4: assigning each region a value, e.g. a value of selected or unselected, and/or a value of relevant or not relevant);
training a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs ([0030-0031],[0042-0046], Fig 4-6: training a ranking model at least in part is based on profile features and the probability of relevance as the profiles features are being used matching processing to generate return results representing the entities including the 1st and 2nd entities as described in [0030] & [0042], and then used to update entity profiles based on feedback for the subsequent matches that includes a potential match for a fourth entity indicated by subsequent search results as described in [0046]));
applying the machine-learning ranking model to rank a potential match for a fourth entity [0030-0031],[0042-0046], Fig 4-6: apply ranking model to rank potential matches-- such as match in the subsequent search for the 4th entity of the potential matches which is the intended use entity of the ranking model application and does not necessary carry any patentable weight).
Hueter does not explicitly discloses R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions;
training the machine-learned ranking model by minimizing a total loss, at least in part based on the first entity and the second entity as claimed.
However, Liu discloses training a machine-learned ranking model by minimizing a total loss, at least in part based on a first entity and a second entity ([0021-0023]: training a machine learning ranking model represented by a ranking function by minimizing a total loss a first and second entities with a usage of a loss function); and
partitioning a space of feature values into regions and assigning each region a value that involve usage of different symbol values such as j=1, 2, ...n(i) ([0020-0022], [0041], [0055-0061]: partition a space represented by a list of features vectors each with an assigned vales, such that the math equation is defined using different mathematical symbol values to rank potential matches)
Since (i) Hueter further discloses the training of ranking model adopts function to minimize squared error, and (ii) Hueter and Liu are from the same field of because both are directed to determine matches between entities based on associated feature indicators, it would have been obvious to one skilled in the art at the time of the invention to modify the error computation & ranking model application of Hueter and incorporate the total loss computation & ranking model application of Liu in ranking model training of teachings of Hueter for minimizing loss and to rank potential match as claimed. The motivation to combine is to provide intelligently rank matches resulting highly relevant information provision for users (Hueter, [0001]; Liu, [0003]).
Liu does not explicitly disclose R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions as claimed.
However, this difference is only found in the nonfunctional descriptive data materials that describe (i) data labels of R9, j=1, 2, ... , J for the partitioned regions, (ii) a data value assigned to each of the regions, and (iii) an intended use of region value assignment that is directed to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions. Neither of data label (or data value) nor the intended use is functionally involved in the steps recited. All the steps in the claims (e.g. detecting, determining, partitioning, assigning, training, and apply) would be performed the same regardless of what the data labels for the regions and region values are. Similarly, all steps in the claims would be performed the same regardless of intended use of the region value assignment. None of impact any of the functionalities of the claimed steps as all the steps would be performed the same to achieve the same outcome.
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to (i) use any data label for the partitions and (ii) assign any value to each region for any usage because such data material does not functionally relate to the steps in the method claimed. Also, it is because the subjective interpretation of the data does not patentably distinguish the claimed invention
With respect to claim 38, Hueter discloses an apparatus (Abstract, [0001]), comprising:
a processor; and a memory including instructions, the processor, upon executing the instructions ([0001]), to
detect a first behavioral feature for a first potential match for the first entity and a second behavioral feature for a second potential match for the second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity (elements on indicating degree of one way interest appears to be directed to non-functional descriptive material for not functionally impacting the structure of the claim because the steps in the claim would be performed the same regardless of the indicated degree of one-way interest ; [0012-0014], [0023-0024], [0043],Fig 2-5: detect behavioral features based on a subject of an active entity’s behaviors toward other subjects representing other entities for respective potential match. The detected behavioral features including and not limited to explicit and inferred/derived behavior features of the active entity that represents the 3rd entity. The explicit behavioral features include explicit input or feedbacks of relevancy indicating a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Inferred/derived behavior features include implicit feedback—including and not limited to non-selection of search results--which also indicate a degree of a degree of at least one-way interest by the 3rd entity toward search results being presented to the 3rd entity. Each search result represents an entity. Hence a 1st and 2nd entities are being represented by two search results among the plurality of search results being presented. Since the plurality of search results being presented and the active entity are being tracked, hence a first behavior feature for a first potential match and a 2nd behavior features for a second potential match are being detected upon explicit and inferred/derived behaviors of the active entity);
determine a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features ([0012-0015], [0020-0024], [0031], [0043], Fig 2-5: determine probability of relevance based behavioral features when training a learning method and/or when determine relevance of one or more matches, which are based on the behavior features in view of input or lack of input of the 3rd entity. Also the scores such as the relevance/behavioral score may be corresponding to the probability of relevance as well since it is also based on the behavioral features as well);
partition a space of feature values into regions ([0042-0044], [0046-0047], Fig 4: the partitioning a space of presented search results representing the feature values into at least two 2 regions according to feedback, e.g. a region of selected result, and a region of unselected region, and/or a region of relevant and a region of not relevant);
assign each of the regions a value ([0042-0044], [0046-0047], Fig 4: assigning each region a value, e.g. a value of selected or unselected, and/or a value of relevant or not relevant);
train a machine-learned ranking model (i) using a subset of features defined by the plurality of candidate profiles and the probability of relevance of each of the first and second potential matches as inputs ([0030-0031],[0042-0046], Fig 4-6: training a ranking model at least in part is based on profile features and the probability of relevance as the profiles features are being used matching processing to generate return results representing the entities including the 1st and 2nd entities as described in [0030] & [0042], and then used to update entity profiles based on feedback for the subsequent matches that includes a potential match for a fourth entity indicated by subsequent search results as described in [0046]));
perform an application of the ranking model to rank a potential match for a fourth entity [0030-0031],[0042-0046], Fig 4-6: apply ranking model to rank potential matches-- such as match in the subsequent search for the 4th entity of the potential matches which is the intended use entity of the ranking model application and does not necessary carry any patentable weight).
Hueter does not explicitly discloses R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions;
train the machine-learned ranking model by minimizing a total loss, at least in part based on the first entity and the second entity as claimed.
However, Liu discloses train a machine-learned ranking model by minimizing a total loss, at least in part based on a first entity and a second entity ([0021-0023]: training a machine learning ranking model represented by a ranking function by minimizing a total loss a first and second entities with a usage of a loss function); and
partitioning a space of feature values into regions and assigning each region a value that involve usage of different symbol values such as j=1, 2, ...n(i) ([0020-0022], [0041], [0055-0061]: partition a space represented by a list of features vectors each with an assigned vales, such that the math equation is defined using different mathematical symbol values to rank potential matches)
Since (i) Hueter further discloses the training of ranking model adopts function to minimize squared error, and (ii) Hueter and Liu are from the same field of because both are directed to determine matches between entities based on associated feature indicators, it would have been obvious to one skilled in the art at the time of the invention to modify the error computation & ranking model application of Hueter and incorporate the total loss computation & ranking model application of Liu in ranking model training of teachings of Hueter for minimizing loss and to rank potential match as claimed. The motivation to combine is to provide intelligently rank matches resulting highly relevant information provision for users (Hueter, [0001]; Liu, [0003]).
Liu does not explicitly disclose R9, j=1, 2, ... , J for the regions; a value <Di to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions as claimed.
However, this difference is only found in the nonfunctional descriptive data materials that describe (i) data labels of R9, j=1, 2, ... , J for the partitioned regions, (ii) a data value assigned to each of the regions, and (iii) an intended use of region value assignment that is directed to define a function T(u, v), where T(u, v) = (Di if feature vector fu,v E Ri for each of the regions. Neither of data label (or data value) nor the intended use is functionally involved in the steps recited. All the steps in the claims (e.g. detecting, determining, partitioning, assigning, training, and apply) would be performed the same regardless of what the data labels for the regions and region values are. Similarly, all steps in the claims would be performed the same regardless of intended use of the region value assignment. None of impact any of the functionalities of the claimed steps as all the steps would be performed the same to achieve the same outcome.
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to (i) use any data label for the partitions and (ii) assign any value to each region for any usage because such data material does not functionally relate to the steps in the method claimed. Also, it is because the subjective interpretation of the data does not patentably distinguish the claimed invention
With respect to claim 26, 33 and 39, the combined teachings of Hueter and Liu further discloses wherein the applying is based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity (the limitations are directed to non-functional descriptive material on describe what the applying is, and not necessary what the applying does, which may not carry patentable weight; Hueter, [0042-0044], [0046-0047], Fig 4: the applying of ranking is performed in part by partitioning a space of presented search results representing the feature values into 2 regions according to feedback. One region being selected and the region being unselected that being used to derive relevance scores and weights (E.g. the selected are consider high relevance and higher weight when the non-selected are consider low-relevance and low weight) in user/entity profiling, which is being used in the ranking model application for subsequent matches to rank subsequent search results. Also, object vector cluster techniques are being used in matching and ranking of entities: region of interest and a region of non-interest; Liu; [0020-0022], [0041]:the applying is based on feature vectors that indicate the respective features,).
With respect to claim 27, 34, and 40, the combined teachings of Hueter and Liu further discloses wherein the ranking model ranks the potential match for the fourth entity, based at least in part on a vector of the fourth entity and at least one of the regions (the limitation is directed to non-functional descriptive material on describe what the ranking model is intended for, which may not carry patentable weight; Hueter, [0042], [0046-0047]: retrieve source and target vectors--which include a vector the 4th entity as the 4th entity is one of the targets –in the matching and ranking of entities/objects based on at least one of the regions, such as the relevance and weight of the selected search results in the profile, or region of interest as set forth by the vector clustering techniques are being used; Liu; [0020], [0041]: ranking model ranks based on vectors).
With respect to claims 28, 35 and 41, the combined teachings of Hueter and Liu further disclose wherein the first behavioral feature pertains to a view of the profile of the first entity, and the second behavioral feature pertains to a view of the profile of the second entity (elements appears to be directed to non-functional descriptive material for not functionally impacting the structure of the claims and the profile is merely a type of data, the behavior features are pertain or related of the profile which are not necessary views of the profiles, hence the steps in the claim would be performed the same regardless of the type of data being used and which may not carry patentable weight; Hueter [0012-0014], [0023-0024], [0043], Fig 2-5: the behavioral features, such as and not limited to explicit feedbacks of relevancy, selection, and non-selection of results representing the entities in the potential matches, are pertain to a view of a profile, as the result presented may be directed to profile of the respective entity of a dating service since the apparatus is applicable to dating service application as described in [0028]; Liu, [0020-0025]: the features pertain to different type of attribute, which is merely a type of data of the items correspond to the profile).
With respect to claims 29, 36 and 42, the combined teachings of Hueter and Liu further discloses wherein the first and second behavioral features do not indicate click feedback from the first entity, the second entity, or the third entity ( the term “or” indicates that only one of the listed--the first entity, the second entity, or the third entity-- is needed to read on the limitation; Hueter, [0012-0014], [0023-0024], [0043], Fig 2-5: when the 1st and 2nd behavioral features are being inferred/derived behavior features such as non-selection of search results by the 3rd entity when search results are being presented to the 3rd entity. Each search result represents an entity. The non-selections of two results representing the first and second behavioral features. Also the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity as presented in the independent claims 45 & 52 & 59 have indicated that the first and second behavioral features are from the 3rd entity, hence the first and second behavioral features do not provide click feedback from the first entity and the second entity since neither behavioral features are from the first and second entities. Even if they the first and second entities do provide behavioral features, those behavioral features are not limited to click feedback because behavioral features include inferred/derived behavior features as stated above).
With respect to claims 30 and 43, the combined teachings of Hueter and Liu further discloses wherein the ranking model is trained to predict features that correlate with relevance (the limitation is directed to non-functional descriptive material on describe what the ranking model is, which may not carry patentable weight; Hueter, [0031], [0043], [0046], Fig 3-5; Liu, [0020-0023], [0041]: training ranking model based on the probability of relevance to predict features/matches).
With respect to claims 31, 37 and 44, the combined teachings of Hueter and Liu further discloses wherein the ranking model minimizes the total loss, based on a gradient descent method (Liu, [0021], [0041]: minimize the total loss using neural network and gradient decent as optimization algorithm).
Conclusion
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/MICHELLE N OWYANG/Primary Examiner, Art Unit 2168