DETAILED ACTION
This action is in response to a filing filed on December 16th, 2024. Claims 1-20 have been examined in this application.
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 .
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 a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-10 is/are drawn to method (i.e., a process), and Claims 11-20 is/are drawn to system (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1: A computer-implemented method comprising:
training a machine learning (ML) algorithm with a selected set of materials that includes a proprietary portion and a public portion;
reviewing at least one material by the ML algorithm trained with the selected set of materials;
and outputting at least one result of the reviewing by the ML algorithm.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the independent claims is/are directed to the abstract idea, specifically the claim recites, the collection of information (e.g., training data), analysis of information (e.g., reviewing, evaluating, or classifying materials using the machine learning algorithm), and presentation of results (e.g., outputting decisions or recommendations). Such operations correspond to concepts that can be performed in the human mind or with pen and paper, such as evaluating materials based on known information and providing a conclusion, and therefore fall within the category of mental processes. Additionally, the claimed steps relate to evaluating content for compliance or decision-making purposes, which is a form of certain methods of organizing human activity, such as managing or reviewing information in a regulatory or business context. Accordingly, the claims recite an abstract idea. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
The dependent claims 2-10, and 12-20 do not recite a different abstract idea but instead further limit the abstract idea identified in the independent claims. Dependent claims (2-3 and 12-13) recite filtering materials and selecting at least one context, that correspond to organizing and refining information prior to analysis, which is part of the abstract process of evaluating information and can be performed mentally or conceptually. Thus, these claims remain within the mental processes category. Dependent claims (4-6 and 14-16) recite specify the nature of the data (e.g., proprietary vs. public data, life sciences industry, regulatory materials). Thus, these claims remain within the mental processes category. Dependent claims (7-8 and 17-18) recite particular types of content (e.g., advertisements, promotional communications) and providing supplemental material. Thus, these claims remain within the mental processes category. Dependent claims (9 and 19) recite specify the supplemental material as a package insert, which is merely a type of information being analyzed. Thus, these claims remain within the mental processes category. Dependent claims (10 and 20) recite providing a chat interface, which is a generic mechanism for presenting or receiving information and does not change the nature of the abstract idea. Accordingly, the dependent claims do not recite a different abstract idea but instead further define the same abstract idea using generic computer functionality, which falls within a judicial exception under 35 U.S.C. §101.
Independent claim(s) 11 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a machine learning (ML) algorithm, a processor and memory, etc. (Claims 1, and 11) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a machine learning (ML) algorithm, a processor and memory, etc. (Claims 1, and 11, and dependent claims 2-10, and 12-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., train, review, output, etc. steps performed by a machine learning (ML) algorithm, a processor and memory, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s), the step of “training a machine learning algorithm” is broadly recited and corresponds to data gathering or preparation, which is a pre-solution activity. Similarly, “outputting at least one result” corresponds to post-solution activity involving the presentation of results. The filtering of materials and provision of supplemental materials also represent data preparation and input gathering steps. Accordingly, these additional elements do not integrate the abstract idea into a practical application and constitute insignificant extra-solution activity (Independent Claims 1, and 11), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-10, and 12-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claims 1, and 11, and dependent claims 2-10, and 12-20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of selecting set of materials that includes a proprietary portion and a public portion, reviewing the selected set of materials, and outputting at least one result of the reviewing (Independent Claims 1 and 11), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea), i.e. data gathering, analysis, and outputting steps, which is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0077] acknowledges that “Users also have the option of choosing a chat agent persona of chat agent 510. Personas can vary based on functional scope, such as regulatory, legal, or marketing, or according to other characteristics. Generally speaking, personas may be developed according to any function, task, or characteristic of any one or any process involved in the development, review, and approval of promotional and other relevant materials. This can enable chat agent 510 to respond to user chat queries with information that is most relevant to the selected persona, provided in terminology or at level most suited for a selected persona, or otherwise tailored.” This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-10, and 12-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Specifically, claims 2-10, and 12-20 likewise fail to integrate the abstract idea into a practical application. The claims 2-10, and 12-20 do not recite a different abstract idea but instead further limit the abstract idea identified in the independent claims. Dependent claims (2-3 and 12-13) recite filtering materials and selecting at least one context, that correspond to organizing and refining information prior to analysis. Dependent claims (4-6 and 14-16) recite specify the nature of the data (e.g., proprietary vs. public data, life sciences industry, regulatory materials). Claims (7-8 and 17-18) recite particular types of content (e.g., advertisements, promotional communications) and providing supplemental material. Claims (9 and 19) recite specify the supplemental material as a package insert, which is merely a type of information being analyzed. Claims (10 and 20) recite providing a chat interface, which is a generic mechanism for presenting or receiving. None of these limitations meaningfully limit the abstract idea or integrate it into a practical application. Accordingly, the additional limitations of the dependent claims do not amount to significantly more than the abstract idea and therefore fail to provide an inventive concept under Step 2B. Because these elements do not solve a specific technical problem or offer a technical improvement over existing systems, they are viewed as merely "applying" the abstract idea on a generic computer, thus failing to provide a practical application that would render the claims patent-eligible, and therefore do not add an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
When viewed as an ordered combination, the additional elements of claims 2-10, and 12-20 merely instruct to implement the abstract idea using generic computer components to collect, store, represent, and display information. The claims do not recite any unconventional arrangement of elements, nor do they effect an improvement to computer functionality or another technical field and therefore fail to integrate the abstract concept into a practical application and it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
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 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 of this title, 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.
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 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 9-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20230317261 (“Vanggaard”) in view of U.S. Pub. 20230103911 (“Thakkar”).
As per claims 1 and 11, Vanggaard discloses, reviewing at least one material by the ML algorithm trained with the selected set of materials (Examiner interprets that “reviewing” broadly encompasses any machine learning-based processing of input data, including but not limited to analyzing, classifying, comparing, evaluating compliance, and generating recommendations or decisions. Under the broadest reasonable interpretation, reviewing does not require human review, but instead includes automated evaluation of input materials using trained models to determine characteristics, relationships, or compliance outcomes. Furthermore, such reviewing inherently involves applying learned patterns from the training data to new input data to produce a result.) (“The compliance decision is provided as an output from the computing device. A trained machine learning model (e.g., compliance recommendation engine 130 of FIG. 1) can recommend a compliance decision in response to the question or query. The machine learning model can be trained by a large number of prior decisions (e.g., from the decision precedent database 128), concepts and terms (e.g., from compliance vocabulary database 127), compliance regulation documents (e.g., from the compliance regulation document database 119), and compliance requirements (e.g., from the compliance requirement database 124) and insights (e.g., from the insight database 126). The training process involves initializing some random values for each of the training matrixes and attempting to predict the output of the input data using the initial random values. In the beginning, the error will be large, but by comparing the model's prediction with the correct output (e.g., labeled by SME 118, 120), the machine learning model is able to adjust the weights and biases values until having a good predicting model.”) (0061);
and outputting at least one result of the reviewing by the ML algorithm (Examiner further interprets “outputting” to include any form of providing results generated by the ML model, including decisions, recommendations, classifications, scores, or other indicators derived from the reviewing step.) (“the compliance recommendation engine 130 is a machine learning model, which was trained using a large number of prior compliance decisions. The compliance recommendation engine 130 can also be trained using the precedent 132 from the decision precedent database 128, the compliance requirements 123 from the compliance requirement database 124, and/or insights 134 from the insight database 126. During the training stage, an SME (e.g., SME 118, SME 120, or a different SME) confirms or adjusts a proposed decision 106 output by the compliance recommendation engine 130. The compliance recommendation engine 130 continuously learns from interactions with the SME. The compliance recommendation engine 130 can be a supervised machine learning model (e.g., convolutional neural network (CNN). For example, the compliance recommendation engine 130 can run a machine learning algorithm, such as Logistic Regression, Support Vector Machines (SVM), Naive Bayes, Decision Trees, Linear Regression, k Nearest Neighbors (kNN) technique, Random Forest, or Boosting algorithms (such as Gradient Boosting Machine, XGBoost, or LightGBM, etc.), etc.”) (0049).
Vanggaard specifically doesn’t disclose, training a machine learning (ML) algorithm with a selected set of materials that includes a proprietary portion and a public portion, however Thakkar discloses, method comprising: training a machine learning (ML) algorithm (“training or updates of the machine learning model 150, the method performs operations 306, 308, and 310. At operation 306, the method 300 includes obtaining the set of differentially private (DP) gradients 143 each generated based on processing corresponding private data 139. At operation 308, the method includes reshaping the set of DP gradients 143 based on the learned geometry 215. At operation 310, the method 300 includes training or updating the machine learning model 150 based on the reshaped set of DP gradients”) (0041, 0040) with a selected set of materials that includes a proprietary portion and a public portion (Examiner interprets the “selected set of materials” broadly as any dataset or collection of data used during training of a machine learning model. The “proprietary portion” is reasonably interpreted as data that is private, restricted, or associated with a specific entity, user, or client (e.g., private data 139), while the “public portion” is reasonably interpreted as data that is publicly accessible or obtained from public repositories (e.g., public data 160), i.e. use of both private data and public data during training constitutes the claimed combination of proprietary and public portions within a selected training dataset.) (“During training, a global ML engine 114 of the remote system 110 processes public data 160, using the global ML model 150, to generate predicted public output(s) 115. The public data 160 can be obtained from a datastore 121 (e.g., residing on the memory hardware 113) of public data 160. In some examples, the private data 139 and the public data 160 are derived from a common, similar, or same distribution of sources. The outputs 115 are referred to herein as predicted public outputs 115 to denote that they are generated based on the public data 160 not that they are necessarily publicly disclosed outside the remote system 110. However, the predicted public gradients 117 may be publicly exposed. The datastore 119 can include any data that is accessible by the remote system 110 including, but not limited to, public data repositories that include audio data, textual data, and/or image data, and private data repositories. Further, the datastore 119 can include data from different types of client devices 130 that have different device characteristics or components … These techniques can include filtering audio data to add or remove noise when the public data 160 is audio data, blurring images when the public data 160 is image data, and/or other techniques to manipulate the public data 160. This allows the remote system 110 to better reflect client data generated by a plurality of different client devices 130 and/or satisfy a need for a particular type of data”) (0032).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for reviewing at least one material by the ML algorithm trained with the selected set of materials and outputting at least one result of the reviewing by the ML algorithm, as taught by Vanggaard, training a machine learning (ML) algorithm with a selected set of materials that includes a proprietary portion and a public portion, as taught by Thakkar for the purpose to incorporate training using both proprietary and public datasets and to utilize supplemental contextual information for improving machine learning model performance, and further combining private and public datasets, allows to improves model robustness, reduces overfitting to limited proprietary datasets, and enhances generalization across broader data distributions.
As per claims 2 and 12, Vanggaard discloses, filtering the selected set of materials before the reviewing (Examiner interprets “filtering” broadly as any preprocessing or data preparation operation performed on input materials prior to analysis, including but not limited to tokenization, feature extraction, classification, categorization, normalization, and similarity comparison. Such operations refine or structure the input data to improve subsequent machine learning processing. Under this interpretation, Vanggaard’s extraction, tokenization, classification, and comparison of compliance requirements constitute filtering of materials prior to reviewing.) (“the compliance requirement extractor 202 can perform tokenization to split each compliance regulation document 110 into a plurality of concepts and/or terms, which can be stored in compliance vocabulary database 127 of FIG. 1. The classifier 204 is configured to classify extracted compliance requirements into respective categories. The classification adjuster 206 is configured to adjust categories determined by the classifier 204 if validation by SME 120 fails (SME 120 disagrees with categories determined by the classifier 204)”) (0056).
As per claims 3 and 13, Vanggaard discloses, wherein the filtering comprises selecting at least one context of the at least one material (Examiner interprets “selecting at least one context” as identifying or utilizing relevant contextual information associated with the material, including but not limited to regulatory categories, compliance domains, document classifications, or semantic groupings. Context, under the broadest reasonable interpretation, encompasses any metadata, classification, or domain-specific grouping that informs how the material is evaluated. Vanggaard discloses selecting and utilizing compliance requirements, precedent data, regulatory documents, and categorized information to generate recommendations, which inherently requires identifying and applying contextual information associated with the material being reviewed. Thus, the use of categorized compliance requirements, regulatory sources, and precedent data constitutes selecting at least one context for the material.) (“The compliance recommendation engine 130 can provide a list of compliance decision recommendations 136 to the user 104 in response to a compliance question or query 102 of the user 104. The list of compliance decision recommendations 136 can be provided based on precedent 132 from the decision precedent database 128, compliance requirements 123 from the compliance requirement database 124, and/or insights 134 from the insight database 126. The list of compliance decision recommendations 136 can include a combination of any of precedent 132, compliance requirements 123, and/or insights 134. The user 104 selects a compliance decision 106 from the list of compliance decision recommendations 136” and “the computing device recommends a compliance decision (an answer to the question or query) satisfying the one or more healthcare compliance requirements to the user. The compliance decision is provided as an output from the computing device. A trained machine learning model (e.g., compliance recommendation engine 130 of FIG. 1) can recommend a compliance decision in response to the question or query. The machine learning model can be trained by a large number of prior decisions (e.g., from the decision precedent database 128), concepts and terms (e.g., from compliance vocabulary database 127), compliance regulation documents (e.g., from the compliance regulation document database 119), and compliance requirements (e.g., from the compliance requirement database 124) and insights (e.g., from the insight database 126).”) (0048-0052 and 0061).
As per claims 4 and 14, Vanggaard specifically doesn’t disclose, wherein the proprietary portion of the set of materials comprises user-defined content related to an industry, and the public portion comprises publicly-available content related to the industry, however Thakkar discloses, wherein the proprietary portion of the set of materials comprises user-defined content related to an industry, and the public portion comprises publicly-available content related to the industry (Examiner interprets “user-defined content related to an industry” as data that is generated, selected, or associated with a particular user, client, or domain-specific application, and “industry” as any field of use, which does not limit the claimed invention structurally. Accordingly, private data associated with specific devices or users in Thakkar reasonably corresponds to user-defined content, while public data corresponds to publicly-available industry-related content.) (“computer-implemented method 300 for leveraging public data 160 in training a neural network with private mirror descent. During an initial or pre-training of a machine learning model 150, the method performs operations 302 and 304. At operation 302, the method 300 includes obtaining a set of public gradients 117 each generated based on processing corresponding public data 160. At operation 304, the method 300 includes applying mirror descent to the set of public gradients 117 to learn a geometry 215 of the public gradients 117 that may be applied to or for a set of DP gradients 143. For example, by using the public gradients 117 derived as a mirror map to learn the geometry 215 for the set of DP gradients 143”) (0040-0041, 0032).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for reviewing at least one material by the ML algorithm trained with the selected set of materials; and outputting at least one result of the reviewing by the ML algorithm, as taught by Vanggaard, wherein the proprietary portion of the set of materials comprises user-defined content related to an industry, and the public portion comprises publicly-available content related to the industry, as taught by Thakkar for the purpose to enable domain-specific adaptation of ML models while maintaining generalization across broader datasets, thereby improving prediction accuracy and reducing bias introduced by limited proprietary datasets.
As per claims 5 and 15, Vanggaard discloses, wherein the industry is a life sciences industry (“harvesting new or updated healthcare compliance requirements from a health authority website. In some implementations, robots harvest healthcare compliance regulation documents from the health authority website. A compliance regulation document classifier classifies compliance regulation documents into the first categories. The compliance requirement classifier identifies healthcare compliance requirements from the harvested compliance regulation documents and classifies healthcare compliance requirements into second categories. If the healthcare compliance requirements are new or updated, then the new compliance requirements or updated compliance requirements are stored in a database. In some implementations, a subject-matter expert (SME) can validate the first categories of healthcare compliance regulation documents and second categories of healthcare compliance requirements, and adjust the first categories or second categories if the validation fails (e.g., if the SME disagrees with first categories classified by the compliance regulation document classifier or second categories classified by the compliance requirement classifier). In some implementations, healthcare compliance concepts and/or terms can be extracted from healthcare compliance regulation documents and added to a healthcare compliance vocabulary database.”) (0004).
As per claims 6 and 16, Vanggaard discloses, wherein the publicly-available content related to the industry comprises at least one of laws, regulations, or regulatory body guidance materials (“the computing device recommends a compliance decision (an answer to the question or query) satisfying the one or more healthcare compliance requirements to the user. The compliance decision is provided as an output from the computing device. A trained machine learning model (e.g., compliance recommendation engine 130 of FIG. 1) can recommend a compliance decision in response to the question or query. The machine learning model can be trained by a large number of prior decisions (e.g., from the decision precedent database 128), concepts and terms (e.g., from compliance vocabulary database 127), compliance regulation documents (e.g., from the compliance regulation document database 119), and compliance requirements (e.g., from the compliance requirement database 124) and insights (e.g., from the insight database 126). The training process involves initializing some random values for each of the training matrixes and attempting to predict the output of the input data using the initial random values. In the beginning, the error will be large, but by comparing the model's prediction with the correct output (e.g., labeled by SME 118, 120), the machine learning model is able to adjust the weights and biases values until having a good predicting model.”) (0061, 0004, 0023).
As per claims 9 and 19, Vanggaard discloses, wherein the at least one supplemental material includes a package insert (Examiner interprets “supplemental material” broadly as any additional information provided to support or enhance evaluation of a primary material. Examiner further interprets a “package insert” as a type of regulatory or informational document associated with a regulated product, particularly in the life sciences domain, that provides detailed information regarding usage, safety, and compliance. Under the broadest reasonable interpretation, such documents fall within the category of regulatory documents and compliance-related materials. Vanggaard discloses the use of healthcare compliance regulation documents, compliance requirements, and domain-specific vocabulary, which constitute regulatory and informational materials used to support compliance analysis. These materials are analogous to, and would have been understood by a POSITA to include, documents such as package inserts, which are commonly used in healthcare compliance evaluation. Therefore, Vanggaard’s regulatory documents reasonably correspond to the claimed supplemental material including a package insert.) (“the method can further include receiving, from a subject-matter expert (SME), a first validation for classification of the healthcare compliance regulation document into the first category; in response to the first validation, classifying the healthcare compliance regulation document into a third category; receiving, from the SME, a second validation for each healthcare compliance requirement into the second category; and in response to the second validation, classifying each healthcare compliance requirement into a fourth category … providing the at least one healthcare compliance term to a subject-matter expert (SME); and adding the at least one healthcare compliance term to a healthcare compliance vocabulary engine upon approval of the SME”) (0016-0017, 0012).
As per claims 10 and 20, Vanggaard discloses, further comprising providing a chat interface with the ML algorithm (“the question or search query 102 and the recommended compliance decision 106 in reply can be shown in an artificial intelligence (AI) chatbot integrated with the example compliance intelligence system 100. The example compliance intelligence system 100 can provide a full audit trail and traceability from answers (recommended compliance decision 106) to source documents (compliance regulation documents 110) or questions 102”) (0034).
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20230317261 (“Vanggaard”) in view of U.S. Pub. 20230103911 (“Thakkar”) in view of U.S. Pub. 20200387937 (“Sheth”).
As per claim(s) 7 and 17, Vanggaard specifically doesn’t disclose, wherein the proprietary portion of the set of materials comprises user-defined content related to an industry, and the public portion comprises publicly-available content related to the industry, however Sheth discloses, wherein the at least one material reviewed by the ML algorithm comprises at least one of an advertisement for a regulated product, a promotional communication for a regulated product, or a non-promotional communication for a regulated product (Examiner interprets the recited materials (advertisement, promotional communication, or non-promotional communication) broadly as different types of content subject to analysis, including marketing materials and regulated communications. Sheth discloses classification and evaluation of content based on brand directives and audience targeting, which encompasses promotional and non-promotional communications and therefore satisfies the claimed limitation.) (“detail how compliance system 100 compares content 124 with brand directives 108. A first set of deep learning algorithms 170 may classify content 124 on a message level and compare any identified messages in content 124 with brand directives 108. A second set of sequence labeling algorithms 172 may classify content 124 on a phrase level and compare any identified phrases in content 124 with brand directives 108. As mentioned above, compliance system 100 may use any other natural language learning algorithms 174 known to those to those skilled in the art to detect any other selected brand directives 108 in content 124” and “Brand directive 108B may include different selectable audiences 188 that include: generation Z, millennials, generation X, boomers, silent generation, economic buyer, champion, technical buyer, and practitioner. Generation Z may be between ages 15-20, millennials may be between ages 21-34, generation X may be between ages 35-49, boomers may be between ages 50-64, and silent generation may be over age 65. The economic buyer may evaluate a return on investment (ROI), a champion may be looking for implementation of a solution, a technical buyer may evaluate feasibility, and the practitioner may evaluate a user experience. Of course, all of these are just examples of any audience 188 where a brand may want to direct content”) (0033 and 0039, 0012).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for reviewing at least one material by the ML algorithm trained with the selected set of materials; and outputting at least one result of the reviewing by the ML algorithm, as taught by Vanggaard, wherein the at least one material reviewed by the ML algorithm comprises at least one of an advertisement for a regulated product, a promotional communication for a regulated product, or a non-promotional communication for a regulated product, as taught by Sheth for the purpose to improve the accuracy and reliability of content evaluation by providing relevant domain-specific information and reference criteria and thus improving compliance analysis systems, consistent with known machine learning practices.
As per claims 8 and 18, Vanggaard specifically doesn’t disclose, providing at least one supplemental material to the ML algorithm, the at least one supplemental material related to the at least one material, however Sheth discloses, further comprising providing at least one supplemental material to the ML algorithm, the at least one supplemental material related to the at least one material (Examiner interprets “providing at least one supplemental material” as supplying additional reference information, criteria, or contextual data to the machine learning system to assist in evaluating a primary material. Such supplemental material may include guidelines, rules, reference datasets, or comparison criteria. Sheth discloses providing brand directives and compliance criteria that are used alongside content to evaluate compliance and generate scores. These brand directives and criteria function as supplemental materials because they provide additional context and reference information used by the ML system to analyze the primary content, i.e. suggests providing supplemental material related to the material being reviewed) (“content compliance system then compares the selected brand criteria with content generated by the creative agency. The content compliance system uses AI algorithms to generate a compliance score that provides a real-time objective indication of the compliance of the creative content with the selected brand criteria. The creative agency can then modify the creative content and receive a real-time updated compliance score”) (0012).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for reviewing at least one material by the ML algorithm trained with the selected set of materials; and outputting at least one result of the reviewing by the ML algorithm, as taught by Vanggaard, providing at least one supplemental material to the ML algorithm, the at least one supplemental material related to the at least one material, as taught by Sheth for the purpose to improve the accuracy and reliability of content evaluation by providing relevant domain-specific information and reference criteria and thus improving compliance analysis systems, consistent with known machine learning practices.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pub. 20250238723 (“Cleveland”).
Cleveland outlines a method for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.
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/GAUTAM UBALE/
Primary Examiner, Art Unit 3689
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