DETAILED ACTION
The present application, filed on 11/7/2023 is being examined under the AIA first inventor to file provisions.
The following is a FINAL Office Action in response to Applicant’s amendments filed on 7/24/2026.
a. Claims 1, 14, 20 are amended
Overall, claims 1-20 are pending and have been considered below.
General Remark
The certified of the priority document has been received on 12/30/2023
Claim Rejections - 35 USC § 101
35 USC 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 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-13 are directed to a computer implemented method, claims 14-19 are directed to a system, and claims 20 are directed to computer executable instructions stored on a non-transitory storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 14, 20) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 (which is representative of independent claims 14, 20) recite an abstract idea, shown in bold below:
[A] An electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and having instructions stored thereon,
[B] determining, in a processor-based machine learning system, multiple clusters by clustering prompts in a training dataset, the processor-based machine learning system comprising a plurality of machine learning models including at least a language model, a reward model and a reinforcement learning model;
[C] determining, in the processor-based machine learning system and based on multiple cohesion levels of the multiple clusters, multiple sampling probabilities corresponding to the multiple clusters,
[D] wherein the cohesion levels indicate intra-cluster distances in the clusters;
[E] determining, in the processor-based machine learning system and, according to the multiple sampling probabilities, a target cluster for sampling; and
[F] constructing target training data by sampling target prompts from the target cluster.
[G] training the language model of the processor-based machine learning system utilizing the target training data, the reward model and the reinforcement learning model of the processor-based machine learning system, at least in part by:
[H] training the language model utilizing the target training data in a first phase of a multi-phase training process of the processor-based machine learning system;
[I] training the reward model in a second phase of the multi-phase training process of the processor-based machine learning system; and further
[J] training the language model utilizing the reinforcement model, in a third phase of the multi-phase training process of the processor-based machine learning system, at least in part based on rewards generated by the reward model;
[K] deploying the trained language model; and
[L] applying additional prompts as inputs to the trained language model to generate respective responses to the additional prompts.
Independent claim 1 (which is representative of independent claims 14, 20) recites: determining multiple clusters and their respective sampling probabilities ([B], [C]); determining a target cluster for sampling and using it for contracting a target training ([E], [F]), training the language model and the reward model ([G]-[J]); and deploying the model along with applying additional prompts, which, based on the claim language and in view of the application disclosure, represents a process aimed at: creating training data for reinforced learning machines and training the language model with the created data and training the language model with the created data.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
In addition, or alternatively, this is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing following rules or instructions, teaching. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is concluded that independent claim 1 (which is representative of independent claims 14, 20) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “a processor,” and “a memory” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the additional elements “wherein the cohesion levels indicate intra-cluster distances in the clusters” as applied to the cohesion level of the clusters, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea creating training data for reinforced learning machines and training the language model with the created data, and do not serve to integrate the identified abstract idea into a practical application.
Therefore, the additional claim elements of independent claim 1, (which is representative of independent claims 14, 20), evaluated individually, as well as a whole, as an ordered combination, do not integrate the identified abstract idea into a practical application and the claims are directed to the recited judicial exception.
Per Step 2B. Independent claim 1 (which is representative of claims independent 14, 20) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 14, 20 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2, which is representative of dependent claims 15, recites:
determining a distance between each prompt in the cluster and a centroid of the cluster; and
determining an intra-cluster distance of the cluster according to the distance.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 2 (which is representative of dependent claims 15) is deemed ineligible.
Dependent claim 4, which is representative of dependent claims 17, recites:
determining a sampling probability distribution according to a sampling probability corresponding to the target cluster; and
sampling the target prompts from the target cluster based on the sampling probability distribution.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 4 (which is representative of dependent claims 17) is deemed ineligible.
Dependent claim 5, which is representative of dependent claims 18, recites:
determining, based on the sampling probability distribution, a predetermined sampling quantity corresponding to the target cluster; and
sampling the predetermined sampling quantity of target prompts from the target cluster.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 5 (which is representative of dependent claims 18) is deemed ineligible.
Dependent claim 6, which is representative of dependent claims 19, recites:
sampling, based on the sampling probability distribution, the target prompts from each target cluster by means of uniform sampling.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 6 (which is representative of dependent claims 19) is deemed ineligible.
Dependent claim 7 recites:
determining embeddings corresponding to the prompts in the training dataset; and
clustering the prompts based on a similarity of the embeddings.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 7 is deemed ineligible.
Dependent claim 8 recites:
receiving a response result determined by a user for the target prompts; and
training a language model based on the response result and the target prompts.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The “receiving a response “element in this dependent claim is comparable to “receiving or transmitting data over a network, e.g., using the Internet to gather or provide data”, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (creating training data for reinforced learning machines and training the language model with the created data) into a practical application (see MPEP 2106.05(d) II)). The” training a language model” element is comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 8 is deemed ineligible.
Dependent claim 9 recites:
determining text hash values corresponding to the prompts in the training dataset; and
deduplicating the training dataset based on the text hash values.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to receiving/transmitting data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (creating training data for reinforced learning machines and training the language model with the created data) into a practical application (see MPEP 2106.05(f)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 9 is deemed ineligible.
Dependent claim 10 recites:
inputting the target prompts in the target training data to a language model,
wherein a predicted output result corresponding to the target prompts is output after being processed by the language model; and
updating the target training data by screening the target prompts according to the predicted output result.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to “receiving or transmitting data over a network, e.g., using the Internet to gather or provide data” “performing repetitive calculations”, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (creating training data for reinforced learning machines and training the language model with the created data) into a practical application (see MPEP 2106.05(d) II)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 10 is deemed ineligible.
Dependent claim 11 recites:
determining, based on the language model, multiple candidate output results corresponding to the target prompts screened out;
receiving a sorting sequence of the multiple candidate output results by a user; and
in response to the sorting,
training the language model by using the updated target training data.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 11 is deemed ineligible.
Dependent claim 12 recites:
determining a predetermined candidate quantity of first texts by using the language model;
respectively determining the predetermined candidate quantity of second texts corresponding to respective first texts,
wherein the second text is located after the first text; and
determining the multiple candidate output results based on multiple corresponding first texts and second texts.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 12 is deemed ineligible.
Dependent claim 13 recites:
determining information entropy corresponding to text information in the predicted output result;
determining a response certainty corresponding to the target prompts according to the information entropy; and
screening the target prompts based on the response certainty.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: creating training data for reinforced learning machines and training the language model with the created data. The elements in this dependent claim are comparable to expressing mathematical concepts like mathematical formulas or equations, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I). In addition, or alternatively, the elements are comparable to performance of limitations expressing teaching, following rules or instructions. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)). Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed.
Therefore, dependent claim 13 is deemed ineligible.
Dependent claims 3, which are representative of dependent claims 16, respectively, recite:
wherein the sampling probabilities are negatively correlated with the cohesion levels, and are positively correlated with the intra-cluster distances.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – the sampling probabilities – and as such, cannot change the nature of the identified abstract idea (creating training data for reinforced learning machines and training the language model with the created data), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 3 (which are representative of dependent claims 16) are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Examiner Remarks
No prior art rejection has been applied to the instant set of claims. The identified prior art does not disclose at least the following independent claims limitations:
determining, based on multiple cohesion levels of the multiple clusters, multiple sampling probabilities corresponding to the multiple clusters,
determining, according to the multiple sampling probabilities, a target cluster for sampling;
The prior art discloses elements of the claimed invention. The prior art of record does not disclose the unique distinct features that render the claims allowable. However, Examiner has determined that it would be impermissible hind-sight reasoning for a person of ordinary skill in the art to combine the individual elements disclosed in the prior-art in order to achieve Applicant's claimed invention.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20090210406 A1 Freire; Juliana et al. METHOD AND SYSTEM FOR CLUSTERING IDENTIFIED FORMS A method is provided for organizing a plurality of documents that include forms. An initial set of clusters is defined for the plurality of documents. The initial set of clusters is re-clustered based on similarity values calculated in multiple feature spaces. For example, a first feature space may be associated with a content of a document while a second feature space may be associated with a content of a form associated with the document. Each cluster has an associated centroid vector in each feature space that is used to represent the cluster. The similarity between the document and each cluster is calculated in both feature spaces. Each document is assigned to the cluster whose centroid is most similar. The cluster centroids may be recalculated and the process repeated until the cluster assignments become stable.
US 20240403651 A1 Deshmukh; Shripad Vilasrao et al. TRAJECTORY-BASED EXPLAINABILITY FRAMEWORK FOR REINFORCEMENT LEARNING MODELS The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.
US 20250111847 A1 Silverstein; Zachary A. et al. Long Running Language Model Thread Truncation Techniques for truncating long running language model conversation threads are provided. In one aspect, a language model thread truncation system includes: a language model; and a thread truncation module configured to obtain prompts and responses from a thread of user interactions with the language model during a conversation, cluster the prompts and responses based on their topical representation to create a cluster around a topic, and after a timing threshold has been reached, truncate the thread by removing the cluster from the thread if the current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwise retain the cluster in the thread. The reference value of the cluster can be determined based on individual reference scores for the prompts and responses in the cluster. A method for language model thread truncation is also provided.
US 20020161763 A1 Ye, Nong et al. Method for classifying data using clustering and classification algorithm supervised A method for classifying data involves receiving a set of training data from a physical process such as a computer network (20). The training data has attribute data and target data. The target data has a class label associated with the attribute data. Dummy clusters are derived from centroid coordinates of the training data associated with the class label (22). Distance measures are determined between the training data and a plurality of clusters which include the dummy clusters (24). Real clusters are created in the plurality of clusters if the training data is closest to a dummy cluster or a cluster having a class label different than the class label associated with the training data (26). A closest match between data to be classified and the plurality of clusters is identified (28) and the data is classified as the class label of the closest match from the plurality of clusters (30).
US 20230376797 A1 Zahn; Oliver et al. SYSTEM AND METHOD FOR SAMPLE EVALUATION In variants, a method for analog product determination can include: determining functional property feature values for a target and determining variable values for a prototype based on the functional property feature values for the target.
US 20250021842 A1 Bernstein; Alon Shlomo et al. MONITORING GENERATIVE MODEL QUALITY A system is disclosed that uses expert systems to monitor and evaluate quality in a large language model. The system can include a prompt library that associates prompts with areas of expertise. The system selects prompts from the library and evaluates first responses generated by a large language model for the set of prompts against second responses generated by a modified version of the large language model to the set of prompts. The evaluation uses expert systems associated with the areas of expertise for the set of prompts. If the system determines that the evaluation indicates a degradation criterion is met, the system may take remedial action. The system provides an effective way to evaluate and prevent the use of modified language models that do not meet the required standards.
US 20230237345 A1 Zahn; Oliver et al. SYSTEM AND METHOD FOR SAMPLE EVALUATION n variants, a method for analog product determination can include: determining functional property feature values for a target and determining variable values for a prototype based on the functional property feature values for the target.
US 10039016 B1 Larish; Bryan Christopher et al. Machine-learning-based RF optimization A method is provided for obtaining reference signal measurements over a structured interface to support RF optimization via machine learning. The method, performed by a network device, includes identifying a target cluster of cell towers for a radio access network (RAN); generating a model for collecting RAN measurements from mobile communication devices in the target cluster; and sending the model via a structured reference point to client applications on the mobile communication devices. The model may direct collection of and sending of the RAN measurements by the client applications. The method may further include receiving, via the structured reference point, the RAN measurements from the client applications based on the model; and aggregating the RAN measurements to represent aspects of the target cluster based on the model.
US 20230232187 A1 AL-QUTAMI; Tareq Aziz Hasan et al. MACHINE LEARNING LOCALIZATION METHODS AND SYSTEMS Machine learning method and systems for estimating a location of a target wireless device in an environment are disclosed. A machine learning method comprises: receiving a plurality of training received signal indictor data sets for discrete locations in the environment, each training received signal data set comprising received signal indicator values and corresponding wireless transmitter identifiers for wireless signals received by a test wireless device at a respective discrete location; generating feature vectors from the received signal indicator data sets; training a machine learning model using the feature vectors to obtain a trained machine learning model; receiving a target received signal data set from the target wireless device, the target received signal data set comprising signal indicator values and corresponding wireless transmitter identifiers for wireless signals received by the target wireless device; generating a target feature vector from the target received signal data set; and estimating a location of the target wireless device as a discrete location output by the trained machine learning model in response to the target feature vector.
US 20240013100 A1 Havel; Gunther et al. Machine-Learning Based Record Processing Systems Aspects described herein may allow unmatched records in databases be matched automatically. For example, a computing device may receive source records and target records to be matched together. The computing device may determine, based on a machine learning model and for each of the plurality of target records, a distance value between the respective target record, and a subset of the plurality of source records. A matched record that identifies the subset of the plurality of source records and a selected one or more target records may be generated. The matched record may be configured to update records in a database.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions.
Examiner respectfully disagrees.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “This is believed to be an incorrect interpretation of claim 1, particularly in view of the recent decision in Ex Parte Desjardins et al., No. 2024-000567 (PTAB Appeals Review Panel, September 26, 2025), which states that "[c]ategorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology," and further expressly states that improvements to how a machine learning model itself operates, including training of a machine learning model, represent improvements to computer functionality.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Thus, the rejection is proper and has been maintained.
Applicant submits “Accordingly, even if one assumes for purposes of argument only that claim 1 could somehow be construed as reciting an abstract idea, such claims are not directed to an abstract idea for reasons similar to those set forth in the above-cited Ex Parte Desjardins decision, as claim 1 clearly integrates any such abstract idea into a practical application that provides improvements in computer technology, and more particularly in the training of machine learning models.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
See response immediately above.
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
Examiner has reviewed and considered all of Applicant’s remarks. The rejection is maintained, necessitated by the fact that the rejection of the claims under 35 USC § 101 has not been overcome.
Conclusion
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 extension fee 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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Radu Andrei whose telephone number is 313.446.4948. The examiner can normally be reached on Monday – Friday 8:30am – 5pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Hayes can be reached at 571.272.6708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http:/www.uspto.gov/interviewpractice.
As disclosed in MPEP 502.03, communications via Internet e-mail are at the discretion of the applicant. Without a written authorization by applicant in place, the USPTO will not respond via Internet e-mail to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. 122. A paper copy of such correspondence will be placed in the appropriate patent application. The following is a sample authorization form which may be used by applicant:
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with me concerning any subject matter of this application by electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center information webpage. Status information for unpublished applications is available to registered users through Patent Center information webpage only.
To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov.
Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (in USA or CANADA) or 571-272-1000.
Any response to this action should be mailed to:
Commissioner of Patents and Trademarks
P.O. Box 1450
Alexandria, VA 22313-1450
or faxed to 571-273-8300
/Radu Andrei/
Primary Examiner, AU 3697
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