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 .
Priority
This instant Application claims priority to a European Application PCT/US23/10585 filed on 01/11/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2025 and 10/27/2025 were filed after the mailing date of the Non-Provisional Patent Application on 10/21/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
DETAILED ACTION
This Office Action is in response to a Non-Provisional Patent Application filed on 10/21/2024. In the application, claims 1-20 have been received for consideration and have been examined.
Specification
Applicant’s submitted specification has been reviewed and found to be in compliance.
Drawings
Applicant’s submitted drawings have been reviewed and found to be in compliance.
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 Idea without significantly more analyzed according to MPEP 2106.
Step 1: The independent claims 1, 8 and 15 do fall into one of the four statutory categories of “a method”, “a system”, and “one or more non-transitory computer storage media” claims. Nevertheless, the claims still are considered as Abstract Idea (i.e., Mental process) for the following prongs and reasons.
Step 2A: Prong 1: The limitations of the independent claims 1, 8, and 15 recite the abstract idea of:
“receiving a request for generation of training data using data corresponding to a plurality of client profiles corresponding to a content platform, wherein the training data is for training a contextual model that is used to identify digital components to provide to a client device (Mental process: a human administrator receives request for generation of training data using client profiles);
identifying, using a set of configuration files and based on the received request, a key and corresponding value type for extraction from the data corresponding to the plurality of client profiles (Mental process: the human administrator identifies a key and corresponding value type for extraction from the data of corresponding client profiles);
extracting, for each client profile and from the data corresponding to the plurality of client profiles, data for the identified key and corresponding data for the identified value type (Mental process: the human administrator extracts data as per the identified key and corresponding data for the identified value type);
aggregating the data for the identified key and the corresponding data for the identified value type, to obtain an aggregated dataset including the key and an aggregated value type obtained by aggregating the data for the identified value type for the plurality of client profiles (Mental process: the human administrator aggregates/combines the extracted data set);
determining that the aggregated dataset satisfies a set of validation criteria (Mental process: the human administrator determines from the aggregated data set if the dataset matches a set of validation criteria); and
in response to determining that the aggregated dataset satisfies the set of validation criteria, providing the aggregated dataset as the training data to a training pipeline for training the contextual model (Mental process: based on the determination, the human administrator utilizes the aggregated data set to train a contextual model)”.
The claim generically recites the concept of a human administrator tasked to receive data, analyze the data for validation, and based on the analysis, aggregate the data in order to utilize it to train a contextual model.
The above limitations are steps which clearly fall into the Mental Process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) bucket which under its broadest reasonable interpretation, covers performance of the limitations in the human mind and / or with pen and paper.
As mentioned above, the steps of claim can be performed by a single or plurality of human security administrators. The claim merely recites a high-level, conceptual framework for organizing data or analyzing context without providing a specific, technical improvement to computer functionality.
Step 2A: Prong 2: The judicial exception (i.e., data using data corresponding to a plurality of client profiles corresponding to a content platform, a set of configuration files, and a key and corresponding value type for extraction from the data) are not integrated into a practical application. In particular, the claims do not recite any additional element to perform beyond routine steps. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.5(a) II).
In this particular case, the additional elements of the claims are:
“A computer-implemented method” (claim 1);
“A system” (claim 8);
“One or more non-transitory computer storage media” (claim 15).
Recitation of these additional elements do not improve the functioning of the computer or to any other technology or technical field.
The additional elements are recited at a high-level of generality (i.e., as generic terms performing generic computer functions) in the instant specification (PgPub instant spec. [0031-0032], & [0036] discloses implementations of the subject matter and the functional operations described in this specification can be implemented in hardware, software, firmware, or any combination thereof unless specifically described as being implemented in a specific manner) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims do not reflect improvement in the technology. Further, mere automated instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claims are not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the above identified additional elements amount to no more than mere instructions to apply the exception using general purpose computer.
To support this factual conclusion, the examiner takes Official Notice that one of the ordinary skills in the art, before the effective filing date of the claimed invention, would have found processors and/or software well-known and routine in technology that involves computers (PgPub instant spec. [0031-0032], & [0036]) such that it amounts no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the examiner asserts that the above noted elements, when considered individually or in combination, do not constitute as “significantly more” than the abstract idea.
Dependent claims 2-7, 9-14 and 16-20 recite a method, system and one or more non-transitory computer storage media claims and hence falls into one of the statutory categories and therefore passes step 1 analysis. However, under step 2, 2A & 2B analysis, the claims fail to recite any limitations that create a difference in the 101 analyses as indicated for claims 1, 8 and 15, because dependent claims merely recite steps which fall under a mental process where human users can perform the steps of these dependent claims and thus dependent claims are found patent ineligible.
Overall analysis of the claims 1-20 demonstrates that limitations are directed to a mental process performable by a human being in their head using a pen and paper in a methodical and orderly manner. Therefore, the claims recite an abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 11, and 18 recites
“wherein the data privacy policy specifies that the aggregated dataset contains data from a minimum number of client profiles; and
wherein determining that the data privacy policy is satisfied comprises determining that that the aggregated dataset contains more than the minimum number of client profiles”.
Examiner find the limitations of identified claims to recite contradictory statements. The first limitation recites ‘… aggregated dataset contains data from a minimum number of client profiles’ whereas second limitation recites ‘… aggregated dataset contains more than the minimum number of client profiles’.
In first limitation, it mentions the data to be minimum number of client, however, in second limitation it mentions the data to be more than the minimum of client. These limitations are self-contradictory to meet the satisfaction of data privacy policy.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-8, 12-15, and 19-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Walters et al., (US20200302234A1).
Regarding claim 1, Walters discloses:
A computer-implemented method comprising:
receiving a request for generation of training data using data corresponding to a plurality of client profiles corresponding to a content platform ([0170] In some embodiments, optimization system 105 may be configured to concurrently perform multiple tasks in parallel. For example, a user may request to concurrently generate a CNN model using credit card transactions as training data; [0128] In step 902, optimization system 105 may receive a user request to generate a new model),
wherein the training data is for training a contextual model that is used to identify digital components to provide to a client device ([0170] using credit card transactions as training data and a random forest using credit score records as training data; [0128] The user request may also include a target model type and user parameters, such as the desired accuracy. For example, the user request may include a ZIP folder with user data and a selected target model type, such as an RNN selection);
identifying, using a set of configuration files (i.e., data profile) and based on the received request, a key and corresponding value type (i.e., data such as ZIP folder, an address in online resources) for extraction from the data corresponding to the plurality of client profiles ([0038] Data profiler 110 may include one or more computing systems configured to perform operations consistent with extracting features from a dataset and/or comparing data profiles to assess similarity between datasets; [0041] Data profiler 110 may correlate datasets and generate a similarity score that may be later used to facilitate the identification of minimum data requirements. Data profiler 110 may also generate an identification result based on the information received from the client device request and transmit the information to client devices 150 about the data profile of the user dataset; [0128] The user request may also include a target model type and user parameters, such as the desired accuracy. For example, the user request may include a ZIP folder with user data and a selected target model type, such as an RNN selection. Alternatively, the request may include an address in a database for the user data. For example, the request may include an address in databases 180 that stores the user data or may include an address in online resources 140 that store the user data; [0129] In step 904, optimization system 105 may generate a user data profile for the user data. In some embodiments, data profiler 110 may identify key features of the user dataset to generate a profile);
extracting, for each client profile and from the data corresponding to the plurality of client profiles, data for the identified key and corresponding data for the identified value type ([0061] Data feature extraction module 234 may extract features from a received dataset or a normalized dataset to generate a data profile. In some embodiments, features may be extracted from a dataset by applying a pre-trained neural network);
aggregating the data for the identified key and the corresponding data for the identified value type, to obtain an aggregated dataset including the key and an aggregated value type obtained by aggregating the data for the identified value type for the plurality of client profiles ([0027] in some embodiments, the system may have the ability to correlate datasets based on their data profiles to make estimations based on similarity of training datasets. For example, the system may employ a series of statistical analysis to determine a data profile that is most closely related to the user data that is used to generate a new model; [0090] database processor 540 may perform the correlations between already stored and new data based on aggregation modules. The aggregation modules may identify a data schema of the reference datasets; generate vectors and a data index, store the data index in database memory 510 and associating the data index with a sample vector);
determining that the aggregated dataset satisfies a set of validation criteria ([0071] Accuracy estimator 348 may be implemented in software or hardware configured to evaluate the accuracy of a model. For example, accuracy estimator 348 may estimate the accuracy of a model, generated by model builder 346, by using a validation dataset. In some embodiments, the validation dataset may be a portion of a training dataset, that was not used to generate the identification model; [0131] In step 910, to quantify the similarity between datasets, optimization system 105 may associate the user datasets with at least one of the data categories based on vectorized distances. For example, optimization system 105 may compare vectors associated with the different data profiles. Then, as described in further detail in connection with FIG. 10, the distance between vectors or their correlations may be computed to determine which one is the closest data category for the user data; [0133] In step 915, optimization system 105 may compare the user dataset with the minimum number of samples for the target model); and
in response to determining that the aggregated dataset satisfies the set of validation criteria, providing the aggregated dataset as the training data to a training pipeline for training the contextual model ([0133] If the user dataset exceeds the minimum number of samples (step 915: yes), optimization system 105 may continue to step 916 and generate the target model requested by the user by generating a user model of the target model type. For example, model generator 120 may be instructed to generate the target model using the user dataset).
Regarding claim 8, it is a system claim and recites similar subject matter as claim 1 and therefore rejected under similar ground of rejection.
Regarding claim 15, it is a non-transitory computer media claim and recites similar subject matter as claim 1 and therefore rejected under similar ground of rejection.
Regarding claim 5, Walters disclose:
The computer-implemented method of claim 1, wherein determining that the aggregated dataset satisfies the set of validation criteria for training data comprises:
determining, for the identified key, that a distribution of data of the aggregated value satisfies a pre-determined data distribution for the identified value type ([0071] Accuracy estimator 348 may be implemented in software or hardware configured to evaluate the accuracy of a model. For example, accuracy estimator 348 may estimate the accuracy of a model, generated by model builder 346, by using a validation dataset. In some embodiments, the validation dataset may be a portion of a training dataset, that was not used to generate the identification model; [0131] In step 910, to quantify the similarity between datasets, optimization system 105 may associate the user datasets with at least one of the data categories based on vectorized distances. For example, optimization system 105 may compare vectors associated with the different data profiles. Then, as described in further detail in connection with FIG. 10, the distance between vectors or their correlations may be computed to determine which one is the closest data category for the user data; [0133] In step 915, optimization system 105 may compare the user dataset with the minimum number of samples for the target model).
Regarding claim 12, it is a system claim and recites similar subject matter as claim 5 and therefore rejected under similar ground of rejection.
Regarding claim 19, it is a non-transitory computer media claim and recites similar subject matter as claim 5 and therefore rejected under similar ground of rejection.
Regarding claim 6, Walters disclose:
The computer-implemented method of claim 1, further comprising:
obtaining additional aggregated data for another value type corresponding to the identified key; and combining the additional aggregated data for another value type with the training data that is provided to the training pipeline ([0027] in some embodiments, the system may have the ability to correlate datasets based on their data profiles to make estimations based on similarity of training datasets. For example, the system may employ a series of statistical analysis to determine a data profile that is most closely related to the user data that is used to generate a new model; [0090] database processor 540 may perform the correlations between already stored and new data based on aggregation modules. The aggregation modules may identify a data schema of the reference datasets; generate vectors and a data index, store the data index in database memory 510 and associating the data index with a sample vector; [0133] If the user dataset exceeds the minimum number of samples (step 915: yes), optimization system 105 may continue to step 916 and generate the target model requested by the user by generating a user model of the target model type. For example, model generator 120 may be instructed to generate the target model using the user dataset).
Regarding claim 13, it is a system claim and recites similar subject matter as claim 6 and therefore rejected under similar ground of rejection.
Regarding claim 20, it is a non-transitory computer media claim and recites similar subject matter as claim 6 and therefore rejected under similar ground of rejection.
Regarding claim 7, Walters disclose:
The computer-implemented method of claim 1, wherein aggregating the data for the identified value type comprises performing a summation, average, or histogram-based operation with respect to the data for the identified value type (Walters: [0136] & [0138]).
Regarding claim 14, it is a system claim and recites similar subject matter as claim 7 and therefore rejected under similar ground of rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-4, 9-11, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Walters et al., (US20200302234A1) in view of Zhang et al., (US20130254152A1).
Regarding claim 2, Walters disclose:
The computer-implemented method of claim 1, wherein the aggregating to obtain the aggregated dataset ([0027] in some embodiments, the system may have the ability to correlate datasets based on their data profiles to make estimations based on similarity of training datasets. For example, the system may employ a series of statistical analysis to determine a data profile that is most closely related to the user data that is used to generate a new model; [0090] database processor 540 may perform the correlations between already stored and new data based on aggregation modules. The aggregation modules may identify a data schema of the reference datasets; generate vectors and a data index, store the data index in database memory 510 and associating the data index with a sample vector)
Walters fails to disclose:
determining that a data privacy policy is satisfied.
However, Zhang discloses:
determining that a data privacy policy is satisfied ([0057] During operation 402, the client device can filter the amount of data that is sent to the population-modeling server by only selecting a portion of the user's contextual information. The client device can select, for example, a subset of the contextual data that was used to generate the aggregated data about the user, or can select the contextual information associated with certain activities. As another example, the client device can select information based on privacy settings set by the user that indicate which types of information the user is willing to share. The privacy settings can be obtained directly from the user, or can be obtained from privacy settings that a user has set within an online social network).
It would have been obvious to an ordinary skill in the art before the effective filing date of the claimed invention to modify the system for determining data requirements to generate machine-learning models and include the data which is selected under satisfied privacy settings, as disclosed by Zhang.
The motivation to select aggregate data under satisfied privacy policy is to ensure certain conditions that must be met in order for a set of aggregated data to be accepted.
Regarding claim 9, it is a system claim and recites similar subject matter as claim 2 and therefore rejected under similar ground of rejection.
Regarding claim 16, it is a non-transitory computer media claim and recites similar subject matter as claim 2 and therefore rejected under similar ground of rejection.
Regarding claim 3, the combination of Walters and Zhang discloses:
The computer-implemented method of claim 2,
wherein the data privacy policy specifies a maximum number of data points for a particular value type corresponding to a particular key (Zhang: [0056] The aggregated data can also include a statistical distribution for the contextual information or activities detected by the client device. This statistical distribution may indicate the distribution of occurrence frequencies during a given time interval, such as the mean or median number of times that the user has visited a certain location during a week, as well as the standard deviation); and
wherein determining that the data privacy policy is satisfied comprises determining that the number of the data points for the identified value type satisfies the maximum number of data points ([0056] This statistical distribution can also indicate a distribution of time intervals for the detected occurrences, such as the mean or median time duration for each detected context or activity, and the standard deviation).
It would have been obvious to an ordinary skill in the art before the effective filing date of the claimed invention to modify the system for determining data requirements to generate machine-learning models and include the data which is selected under satisfied privacy settings, as disclosed by Zhang.
The motivation to select aggregate data under satisfied privacy policy is to ensure certain conditions that must be met in order for a set of aggregated data to be accepted.
Regarding claim 10, it is a system claim and recites similar subject matter as claim 3 and therefore rejected under similar ground of rejection.
Regarding claim 17, it is a non-transitory computer media claim and recites similar subject matter as claim 3 and therefore rejected under similar ground of rejection.
Regarding claim 4, the combination of Walters and Zhang discloses:
The computer-implemented method of claim 2,
wherein the data privacy policy specifies that the aggregated dataset contains data from a minimum number of client profiles; and wherein determining that the data privacy policy is satisfied comprises determining that that the aggregated dataset contains more than the minimum number of client profiles (Walters: [0136] In step 1002, optimization system 105 may identify key locations or key features in different dimensions of a dataset. For example, optimization system 105 may take one of the data categories, as categorized in step 702 (FIG. 7), and identify peak locations in each dimension; [0137] In step 1004, optimization system 105 may determine a cluster based on the key locations or features to generate multi-dimensional vectors. For example, optimization system 105 may evaluate the number of peaks in each dimension and set the cluster number of clusters as the maximum number of peaks for each dimension).
Regarding claim 11, it is a system claim and recites similar subject matter as claim 4 and therefore rejected under similar ground of rejection.
Regarding claim 18, it is a non-transitory computer media claim and recites similar subject matter as claim 4 and therefore rejected under similar ground of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED M AHSAN whose telephone number is (571)272-5018. The examiner can normally be reached 8:30 AM - 6:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amir Mehrmanesh can be reached at 571-270-3351. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED M AHSAN/Primary Examiner, Art Unit 2491