DETAILED ACTION
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 .
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 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.
Status of Claims
Claims 1-4, 7-14, and 17-20 remain pending, and are rejected.
Claims 5-6 and 15-16 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 3/3/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 3/3/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 15-17 of the Applicant’s Remarks, arguments are made that the claims are not directed to an abstract idea because the claims recite a specific data-processing pipeline that includes computing attribute-specific impurity and gain metrics using a randomized decision-tree algorithm, ranking and filtering high-dimensional attribute data based on those computed metrics, and using the resulting reduced attribute set to drive subsequent vector-based similarity processing. Arguments are also made that the random forest tree-based algorithm is used to calculate impurity values (Gini impurity) for attributes and to compute Gini Gain values to rank and select attributes, including providing explicit equations and worked templates, and uses locality sensitive hashing to assign the same hash value to very similar feature vectors and returns candidate vectors having the same hash value as seed vectors to determine approximate nearest neighbors. On pages 18-19, the Applicant argues that even if the claims involve an abstract idea, the claims integrate the abstract idea into a practical application, such as by reciting various additional elements to recite a specific technical mechanism for selecting a reduced attribute set using random forest-based impurity and gain computations and threshold-based filtering, and then using that reduced attribute set to drive subsequent vector-based similarity operations including locality sensitive hashing, approximate nearest-neighbor identification, and distance-based similarity scoring and filtering. The use of the Gini index and Gini Gain increases computational efficiency by reducing the attribute data to the most relevant attributes. The Applicant argues that the claims recite and apply specific computer-implemented mechanisms that change how the system process, filters, and compares high-dimensional customer-attribute data to identify similar users.
On pages 20-21, arguments are made that the claims amount to significantly more than the abstract idea by defining how the computer performs the claimed attribute selection and similarity determination in a particular way, and the amended claims recite specific mechanisms that constrain and structure the computer’s processing rather than merely stating a desired result. On pages 22-23, the Applicant argues that the Examiner does not meet the requirements of the Berkheimer Memo in providing that the additional elements are well-understood, routine, and conventional.
Examiner respectfully disagrees. The claims are directed to determining recommendations to provide customers using a seed customer identifier, and how to calculate and score various aspects of the feature vectors and customer identifiers to determine those recommendations, which is a sales activity. The specific data-processing pipeline by computing attribute-specific impurity and gain metrics using a randomized decision-tree algorithm, ranking and filtering high-dimensional attribute data based on those computed metrics, and using the resulting reduced attribute set to drive subsequent vector-based similarity processing are merely applied to the abstract idea for the use of various algorithms to calculate the scores for the abstract idea, and how any specific machine learning functionality is changed or improved is not recited or disclosed in the application. The other elements, such as the random forest tree-based algorithm to calculate impurity values (Gini impurity) for attributes and to compute Gini Gain values to rank and select attributes, including provide explicit equations and worked templates, and use locality sensitive hashing to assign the same hash value to very similar feature vectors and return candidate vectors having the same hash value as seed vectors to determine approximate nearest neighbors is also merely applying generic methods to the abstract idea to perform calculations. The claims are not directed to how any machine learning algorithm works, but only applies the generic methods to the abstract idea to perform calculations. Furthermore, the claims do not integrate the abstract idea into a practical application as they only apply these methods to perform calculations, and any improvement to processing speed is only from having to process less data of the abstract idea. How the processor or computer functions remains unchanged, and the increased speed would only be within the use of the abstract idea, because a more efficient algorithm for the abstract idea itself. Only how the abstract idea of generating values for attribute data to generate a list of product identifiers using a seed customer and candidate customers is changed, and implemented with a computer. Therefore, the claims also do not provide significantly more than the abstract idea as any underlying technology of the computer is unchanged. With regard to the arguments regarding the Berkheimer Memo, the Examiner does not argue that the claims are well-understood, routine, or conventional, but that generic methods are applied to the abstract idea. Additionally, neither the claims nor the specification discuss the technical details or how the methods function, such as the random forest tree-based mechanisms, except disclosing some mathematical equations of the abstract idea, such as in specification paragraph [0081] disclosing the calculation of the Gini impurity using probability of product purchasers, which is an abstract idea as mathematical concepts.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
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 1-4, 7-14, and 17-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-4, 7-10, and 20 are directed to a system, which is an apparatus. Claims 11-14 and 17-19 are directed to a method, which is a process. Therefore, claims 1-4, 7-14, and 17-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of associating candidate customer identifiers to a product identifier:
receiving a request to generate a list of one or more product identifiers mapped to one or more candidate customer identifiers;
retrieving seed data associated with the one or more product identifiers;
retrieving seed data associated with the one or more product identifiers;
generating a seed customer identifier list based on the retrieved seed data;
retrieving attribute data associated with a plurality of candidate customer identifiers by tracking user action history data associated with the plurality of customer identifiers;
inputting the seed customer identifier list and attribute data into a first engine;
generating a plurality of decision trees associated with a plurality of values, wherein the plurality of decision trees are generated in a randomized manner using a random forest algorithm, wherein each value corresponds to an attribute of the attribute data, and wherein generating the plurality of decision tree comprises, for each attribute of the attribute data, computing a Gini impurity score based on seed customers and non-seed customers, and computing a Gini Gain value for the attribute;
ranking the attribute data according to the Gini Gain value for each attribute;
reducing, based on the ranking, the attribute data to one or more attributes by removing each attribute having a Gini Gain value below a predetermined value threshold;
generating a plurality of feature vectors based on the selected one or more attributes, wherein the plurality of feature vectors include at least one candidate feature vector associated with the plurality of candidate customer identifiers and at least one seed feature vector associated with the seed customer identifier list;
determining a hash value for each feature vector by applying a locality sensitive hashing algorithm to the feature vector;
determining approximate nearest neighbor feature vectors based on the determined hash value;
calculating at least one similarity score for a set of feature vector pairs based on the determined approximate nearest neighbor feature vectors, wherein each feature vector pair includes a candidate feature vector and a seed feature vector;
generating a set of customer identifier pairs based on the at least one similarity score;
determining, for each candidate customer identifier, a propensity score for each product identifier by calculating a number of seed customer identifiers determined to be similar to the candidate customer identifier out of a total number of seed customer identifiers;
sorting the one or more product identifiers based on the propensity score for each product identifier to determine a recommendation rank;
generating the list of one or more product identifiers mapped to one or more candidate customer identifiers based on the recommendation rank, the generated set of customer identifier pairs, the seed customer identifier list, and one or more predetermined rules.
The recited limitations above set forth the process for associating candidate customer identifiers to a product identifier. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors, etc.). The claims recite steps for retrieving seed data to determine candidate customer identifiers and calculating a similarity to determine customer identifier pairs (see specification [0002-0003] disclosing the problem of figuring out the right audience to target to maximize sales of select products), which is a sales and marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
one or more memory devices storing instructions;
one or more processors configured to execute the instructions to perform operations;
a machine learning model;
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite memory devices and processors, these elements are recited with a very high level of generality, and merely recited in performing the steps of the abstract idea. The specification also does not address the processors specifically or provide any detail to any particular device, except that it is configured to execute instructions, such as in paragraphs [0067] or [0091]). The memory devices are also not disclosed with any specificity except to generally store instructions, and are disclosed as being any of hard disks, CD ROM, other forms of RAM or ROM, USB media, etc. in specification paragraph [0096]. It is evident that the additional elements are recited at a very high level of generalization and only serve to implement the abstract idea on a computing device. The machine learning is also disclosed with a high level of generality, such as in specification paragraph [0067], which discloses the machine learning as being a combination of a Random Forest machine learning algorithm and Locality Sensitive Hashing algorithm. Paragraph [0081] discloses the Gini impurity score, but merely provides mathematical equations regarding the abstract idea in calculating the score. The specification does not provide any further detail of the machine learning, and it is evident that the instant application is not directed to any machine learning improvement or functionality, but only applies generic machine learning algorithms to perform calculation for the abstract idea.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concepts (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in representative claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 11 (method): Claim 11 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 11 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 20 (system): Claim 20 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 20 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-4, 7-10, 12-14, and 17-19 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm for associating candidate customer identifiers to a product identifier. Thus, each of claims 2-4, 7-10, 12-14, and 17-19 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-4, 7-10, 12-14, and 17-19 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-4, 7-10, 12-14, and 17-19 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged (e.g. analytics database (claim 5); an interactivity window (claim 7); API calls (claim 9)); however, the additional elements of claims 2-4, 7-10, 12-14, and 17-19 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-4, 7-10, 12-14, and 17-19 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-4, 7-10, 12-14, and 17-19 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-4, 7-10, 12-14, and 17-19 do not add “significantly more” to the abstract idea.
Subject Matter Free of Prior Art
The following is a restatement of the reasons for indicating subject matter free of the prior art that was previously mailed on 12/3/2025.
Claims 1-4, 7-14, and 17-20 are determined to have overcome the prior art of rejection and are free of the prior art, however the claims remain rejected under 35 USC 101, as set forth above.
Claims 1-4, 7-14, and 17-20 are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 recites the claimed features of:
generating, from the machine learning model, a plurality of decision trees associated with a plurality of values, wherein the plurality of decision trees are generated in a randomized manner using a random forest algorithm, wherein each value corresponds to an attribute of the attribute data, and wherein generating the plurality of decision tree comprises, for each attribute of the attribute data, computing a Gini impurity score based on seed customers and non-seed customers, and computing a Gini Gain value for the attribute;
reducing, based on the ranking, the attribute data to one or more attributes by removing each attribute having a Gini Gain value below a predetermined value threshold;
The closest prior art was found to be as follows:
Renaud (US 20220076279 A1) discloses [0385] – “The term ‘user’ refers to a member of a specific population, such as participants of social networks. Information 220 characterizing a first plurality of tracked users of the universe of users 210 is acquired and the first plurality of tracked users is segmented accordingly into clusters 240 of users where members of a cluster have close characteristics which are distinct from characteristics of all other clusters. Information 230 relevant to behaviour (conduct) of a second plurality of tracked users of the universe 210 of users is acquired. The first plurality of users and the second plurality of users may overlap or even coincide. While a user belongs to a single cluster based on the user's characterizing information (hence the clusters of users do not have common users), users of different clusters may have common behaviour (conduct) attributes as illustrated in FIGS. 25 to 27. User-behaviour information 230 and the membership of the clusters 240 of users may be used to determine clusters-behaviour relationships”.
Choi (US 20200034587 A1) discloses [0012] – “calculating the recommendation score for the subset of digital assets includes, for each user of a plurality of users of the content distribution system, calculating a similarity score for a particular user, selecting a number of users as similar users to the target user based on the similarity scores for each of the plurality of users, and calculating a recommendation score for the subset of digital assets that are not installed on a client device of the target user. The similarity score can be calculated by calculating a weighted sum of a first dot product of a vector of installation data corresponding to the particular user with a vector for the target user and a second dot product of a vector of usage data corresponding to the particular user with a vector of usage data for the target user”.
Liu (US 20210326674 A1) discloses [0117] – “The attention mechanism is used for calculating the similarities between the target user vector and a plurality of seed user vectors corresponding to candidate recommendation content, determine a degree of interest of the target user in the candidate recommendation content according to the similarities, predict a probability that the target user is interested in one piece of candidate recommendation content by comparing similarities of interest between the target user and a plurality of seed users, and use a group of seed user vectors to represent an interest feature of one piece of candidate recommendation content, thereby effectively improving accuracy and reliability of content prediction; [0262] – “Gini coefficient: RALM is intended to mitigate the Matthew Effect, so that this embodiment uses the Gini coefficient to measure click-through distribution of all candidate content in the recommendation system. A relatively high Gini coefficient indicates that the system consumes a relatively large amount of long-tail materials and has a relatively good distribution capability”.
NPL Reference U (see PTO-892 Reference U mailed on 1/28/2025) discloses a system for recommendations that mine data to classify products and opinions to discover communities for market campaigns.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
generating, from the machine learning model, a plurality of decision trees associated with a plurality of values, wherein the plurality of decision trees are generated in a randomized manner using a random forest algorithm, wherein each value corresponds to an attribute of the attribute data, and wherein generating the plurality of decision tree comprises, for each attribute of the attribute data, computing a Gini impurity score based on seed customers and non-seed customers, and computing a Gini Gain value for the attribute;
reducing, based on the ranking, the attribute data to one or more attributes by removing each attribute having a Gini Gain value below a predetermined value threshold;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 1-4, 7-14, and 17-20 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 7:30 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/8/2026
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