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 .
This office action is in response to the claimed amendment filed on September 22, 2025, in which claims 1-20 are presented for further examination.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of a new ground of rejection necessitated by amendment.
Applicant asserted that “adjusting the ranking for a low-follower entity of the plurality of entities using the rate distribution and the probability distribution” is related to an improvement to a technology or technical field and integrate the alleged abstract idea into a practical application. After further reviewed applicant’s arguments in light of the original specification, it is conceivable that the claimed “adjusting the ranking for a low-follower entity of the plurality of entities using the rate distribution and the probability distribution” is identified as abstract idea. A person of ordinary Skill in the art (POSITA) would mathematically use a rate and probability distribution to adjust the ranking for a low-follower entity to allow entities with lower numbers of followers to feature more prominently in entity selection and ranking. Such a limitation would not integrate into a practical application and improve the functioning of a computer or technical field to render the claim eligible under 35 USC 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
At Step 1:
With respect to subject matter eligibility under 35 USC 101, it is determined that the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
At Step 2A, Prong One:
The limitation “generating a reward score for an entity of a plurality of entities” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the claimed “generating”, in the context of the claims encompasses one can manually with the aid of pen and paper create a reward score for an entity.
The limitation “determining a rate distribution for the entity comprising a mean and a variance wherein the mean and the variance are determined using the reward score and a number of times the entity has been selected for ranking” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation in light of the specification, covers a mathematical relationship. One can using a mean and a variance determine a rate distribution for the entity.
The limitation “generating a sampled rate value for the entity by sampling the rate distribution” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language, “generating”, in the context of the claim encompasses one can manually with the aid of pen and paper generate a sampled rate value for the entity.
The limitation “generating a probability score for a pair of the entity and a user based on the sampled rate value” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language, “generating”, in the context of the claim encompasses one can manually with the aid of pen and paper generate a probability score for a pair of the entity.
The limitation “determining a probability distribution for the pair using the probability score” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language, “determining”, in the context of these claims encompasses one can manually with the aid of pen and paper determine a probability distribution for the pair using the probability score.
The limitation “generating a sampled probability value for the pair by sampling the probability distribution” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “generating”, in the context of the claim encompasses one can manually with the aid of pen and paper generate a sampled probability value for the pair by sampling the probability distribution.
The limitation “rank the plurality of entities using the sampled probability value” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is, other than reciting “using the trained machine learning model”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “rank”, in the context of the claim encompasses one can manually with the aid of pen and paper rank the plurality of entities using a trained a machine learning model.
The limitation “determining a ranking for the plurality of entities using the trained machine learning model” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is, other than reciting “using the trained machine learning model”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, language “determining”, in the context of the claim encompasses one can manually with the aid of pen and paper determine a ranking for the plurality of entities using the trained machine learning model.
The limitation “adjusting the ranking for a low-follower entity of the plurality of entities using the rate distribution and the probability distribution” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “adjusting”, in the context of the claim encompasses one can manually with the aid of pen and paper adjust the ranking for a low-follower entity of the plurality of entities.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea.
At Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements “training the machine learning model to rank the plurality of entities using the sampled probability value” and “presenting a presentation list using the adjusted ranking for the low-follower entity and a space allocation on a presentation page of an application software”.
That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation "trained a machine learning model by performing…" the above identified mental processes and the "determining" above being performed "using the machine learning model" is at best generally linking the abstract idea to the particular field of use or technological environment of machine learning (see MPEP 2106.05(h), and/or akin to using machine learning as a mere tool (2106.05(f)). No specific type of machine learning processing or techniques are recited in the claim itself, the steps performed in this "training" are entirely mentally performable processes as explained above, and the specification describes this machine learning in the claim in terms of using generic ML models.
The limitation “presenting a presentation list using the adjusted ranking for the low-follower entity and a space allocation on a presentation page of an application software” represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05 (g)).
That the recommendation is generated “application software " recites insignificant extra-solution activity such as mere outputting of the result. Mere presentation or output of a mental process generated recommendation does meaningfully limit the abstract idea nor provide integration into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (machine learning i.e. ML) as a tool are carried over and do not provide significantly more.
With respect to the “presenting” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), " iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93" and "i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 1 is directed to an abstract idea. The remaining independent claim 11 falls short the 35 USC 101 requirement under the same rationale.
The dependent claims 2-5 and 12-15 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 2 recites “determining observed rewards for the entity; and generating the reward score using the observed rewards”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 3 recites “receiving a master set of entities, wherein a master entity of the master set of entities has a number of followers; and generating the plurality of entities by filtering the master set of entities based on the number of followers”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving a master set of entities, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 4 recites “generating the reward score for the entity and an attribute of a plurality of attributes by: generating an attribute reward score for the entity based on the attribute, wherein the attribute reward score comprises at least one of a follow score, a utility score, or a create score, wherein the mean and the variance are determined using the attribute reward score and the number of times the entity has been selected for ranking”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 5 recites “wherein generating the reward score for the entity further comprises: determining the attribute of the plurality of attributes, wherein the attribute comprises at least one of: entity, title location, industry, or skills”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 12 recites “determining observed rewards for the entity; and generating the reward score using the observed rewards”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 13 recites “receiving a master set of entities, wherein a master entity of the master set of entities has a number of followers; and generating the plurality of entities by filtering the master set of entities based on the number of followers”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving a master set of entities, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 14 recites “generating the reward score for the entity and an attribute of a plurality of attributes by: generating an attribute reward score for the entity based on the attribute, wherein the attribute reward score comprises at least one of a follow score, a utility score, or a create score, wherein the mean and the variance are determined using the attribute reward score and the number of times the entity has been selected for ranking”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 15 recites “wherein generating the reward score for the entity further comprises: determining the attribute of the plurality of attributes, wherein the attribute comprises at least one of: entity, title location, industry, or skills”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
At Step 2A, Prong One:
The limitation “generating a reward score for an entity of a plurality of entities , wherein the reward score comprises at least one of a follow score, a utility score, or a create score” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the claimed “generating”, in the context of the claims encompasses one can manually with the aid of pen and paper create a reward score for an entity.
The limitation “determining a rate distribution for the entity, wherein the rate distribution comprises a mean determined using the reward score and a variance” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation in light of the specification, covers a mathematical relationship. One can using a mean and a variance determine a rate distribution for the entity.
The limitation “generating a sampled rate value for the entity by sampling the rate distribution” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language, “generating”, in the context of the claim encompasses one can manually with the aid of pen and paper generate a sampled rate value for the entity.
The limitation “rank the plurality of entities using the sampled probability value” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is, other than reciting “using the trained machine learning model”, nothing in the claim element precludes the steps from practically being performed in a human mind.. For example, the language “rank”, in the context of the claim encompasses one can manually with the aid of pen and paper rank the plurality of entities using a trained a machine learning model.
The limitation “determining a ranking for the plurality of entities using the trained machine learning model” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is, other than reciting “using the trained machine learning model”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, language “determining”, in the context of the claim encompasses one can manually with the aid of pen and paper determine a ranking for the plurality of entities using the trained machine learning model.
The limitation “adjusting the ranking for a low-follower entity of the plurality of entities using the rate distribution and the probability distribution” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the language “adjusting”, in the context of the claim encompasses one can manually with the aid of pen and paper adjust the ranking for a low-follower entity of the plurality of entities.
The limitation “determining a presentation list using the adjusted ranking for the low-follower entity and a space allocation on a presentation page of an application software system” in claims 6 and 16, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is, other than reciting “of an application software system”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, language “determining”, in the context of the claim encompasses one can manually with the aid of pen and paper determine a presentation list using the adjusted ranking for the low-follower entity.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea.
At Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements “training the machine learning model to rank the plurality of entities using the sampled probability value” and “presenting a presentation list using the adjusted ranking for the low-follower entity and a space allocation on a presentation page of an application software”.
That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation "trained a machine learning model by performing…" the above identified mental processes and the "determining" above being performed "using the machine learning model" is at best generally linking the abstract idea to the particular field of use or technological environment of machine learning (see MPEP 2106.05(h), and/or akin to using machine learning as a mere tool (2106.05(f)). No specific type of machine learning processing or techniques are recited in the claim itself, the steps performed in this "training" are entirely mentally performable processes as explained above, and the specification describes this machine learning in the claim in terms of using generic ML models.
That the recommendation is generated “application software " recites insignificant extra-solution activity such as mere outputting of the result. Mere presentation or output of a mental process generated recommendation does meaningfully limit the abstract idea nor provide integration into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (machine learning i.e. ML) as a tool are carried over and do not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 6 is directed to an abstract idea. The remaining independent claim 16 falls short the 35 USC 101 requirement under the same rationale.
The dependent claims 7-10 and 17-20 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 7 recites “determining observed rewards for the entity; and generating the reward score using the observed rewards”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 8 recites “the first and second managed directories are associated with one or more virtual machines configured as an application cluster receiving a master set of entities, wherein a master entity of the master set of entities has a number of followers; and generating the plurality of entities by filtering the master set of entities based on the number of followers”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving a master set of entities, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 9 recites “wherein the application cluster is configured to store data at the file system generating the reward score for the entity and an attribute of a plurality of attributes by: generating an attribute reward score for the attribute, wherein the attribute reward score comprises at least one of a follow score, a utility score, or a create score, wherein the mean and the variance are determined using the attribute reward score and a number of times the entity has been selected for ranking”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing data at the file system generating the reward score for the entity, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 10 recites “wherein generating the reward score for the entity further comprises: determining the attribute of the plurality of attributes, wherein the attribute comprises at least one of: entity, title location, industry, or skills”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 17 recites “determining observed rewards for the entity; and generating the reward score using the observed rewards”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 18 recites “the first and second managed directories are associated with one or more virtual machines configured as an application cluster receiving a master set of entities, wherein a master entity of the master set of entities has a number of followers; and generating the plurality of entities by filtering the master set of entities based on the number of followers”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving a master set of entities, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 19 recites “wherein the application cluster is configured to store data at the file system generating the reward score for the entity and an attribute of a plurality of attributes by: generating an attribute reward score for the attribute, wherein the attribute reward score comprises at least one of a follow score, a utility score, or a create score, wherein the mean and the variance are determined using the attribute reward score and a number of times the entity has been selected for ranking”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing data at the file system generating the reward score for the entity, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 20 recites “wherein generating the reward score for the entity further comprises: determining the attribute of the plurality of attributes, wherein the attribute comprises at least one of: entity, title location, industry, or skills”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0382530 (involved in performing unequal sampling are provided. In one technique, multiple scores generated by a prediction model are identified, each score corresponding to a different entity of multiple entities. Multiple buckets are determined, each bucket corresponding to a different range of scores. Each entity is assigned to a bucket based on the score corresponding to the entity. A probability distribution function is generated based on the scores and a number of scores belonging to each bucket. For each entity, a probability of sampling the entity is determined based on the probability distribution function and a score corresponding to the entity. A subset of the entities are sampled based on the probability determined for each entity.)
US20220414343 (involved in storing a first machine learning model, which performs hybrid collaborative filtering based on similarities in predicted intents and user characteristics between users. A second machine learning module is an unsupervised model. A cloud based control circuitry receives a user action at a user interface, and generates a data vector based on the user action. The control circuitry determines a probability rating for probable intents based on actual intents of the subset of users during a time period, and determines a weighted average of another probability rating and the former probability rating.)
US20220067816 (involved in retrieving from a data source, data indicating a user's sequence of behaviors. The machine learning (ML) model is trained based on the user's sequence of behaviors. The abandonment probability score for the user is generated based on the user's sequence of behaviors and the trained ML model, where the abandonment probability score includes an average abandonment probability score of generated scores from more than one of a variant abandonment model, a fear-uncertainty-death (FUD) model, and a RISK model. The abandonment probability score is sent to the data source to be included in the data indicating the user's sequence of behavior.)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/JEAN M CORRIELUS/
Primary Examiner, Art Unit 2159 November 6, 2025