DETAILED ACTION
Response received on January 30, 2026 has been acknowledged. Claims 1, 5, 12, 14, 16, 18, 19, and 20 have been amended. Therefore, Claims 1-20 are pending.
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 .
Status of Claims
This Final Office action is in response to the application filed on 05/21/2024 and in response to Applicant’s Arguments/Remarks filed on 01/30/2026. Claims 1-20 are pending.
Priority
Application 18/670,155 was filed on 05/21/2024.
Applicant’s Reply
Applicant's response of January 30, 2026 has been entered. The examiner will address applicant’s remarks at the end of this office action. The examiner acknowledges the amendments made to Claim 1, 5, 12, 14, 16, and 18-20.
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 judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1‐13 are directed to a system (machine), Claims 14-18 are directed to a method (process), and Claims 19-20 are directed to a non-transitory computer readable storage medium storing (machine/apparatus). Thus, these claims fall within one of the four statutory categories of invention. (Step 1: YES).
For step 2A, the Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent claim 14 and 19. Claim 1, as exemplary is recited below, isolating the abstract idea from the additional elements, wherein the abstract idea is set in bold:
An electronic online system for compliance analysis of an organization, the online system comprising: a processor subsystem; and memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to: receive, from a user of the electronic online system, an indication of a law for analysis; parse the law to produce law chunks; provide the law chunks to a large language model LLM with a prompt to determine whether each law chunk is a business-relevant law chunk that is relevant to the organization, a business line of the organization, a product line of the organization, a business area of the organization, or a market area, and discard law chunks determined to be not relevant; receive, from the user, an indication of a business policy for analysis; parse the business policy to produce policy chunks; compare the business-relevant law chunks with the policy chunks to determine similarity scores for respective pairs of law chunks and policy chunks; and present law chunks that have similarity scores less than a threshold similarity score to the user.
The above bolded limitations recite the abstract idea of providing corporate compliance analysis configured to find the gaps in a company’s coverage of laws and regulations that are applicable to it. These limitations under its broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations as well as fundamental economic principles) but for the recitation of generic computer components. That is, other than reciting a system implemented by a data processor (computer) the claimed invention amounts to the abstract idea stated above. For example, for the related computer components, this claim encompasses legal actions that could conventionally be performed by compliance officers, lawyers, or business auditors manually as part of company compliance or auditing process. This progress can be done manually through paper and pencil. Additionally, providing corporate compliance analysis is considered a commercial or legal interactions because it involves compliance review between laws and business policies, which are basic methods of organizing human activity related to commercial and regulatory decision-making. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions between parties, but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. The mere nominal recitation of a “an electronic online system”, “a processor subsystem”, “a memory”, and “a large language model LLM”, do not take the claim out of the methods of organizing human interactions grouping. Thus, claims 1, 14, and 19 recites an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A). In particular, Claim 1 recites additional elements of “an electronic online system”, “a processor subsystem”, “a memory”, and “a large language model LLM”. Claim 14 recites the additional elements of “an electronic online system”, and “a large language model LLM”. Claim 19 recites additional elements of “non-transitory machine-readable medium”, “an electronic online system”, and “a large language model LLM”. These additional elements are all considered nothing more than generic computing devices to perform generic communicating functions such as storing data and instructions, transmitting and receiving data between computers. The computing devices are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of communicating data between users) such that they amount no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements (combination of computer and the use ledgers) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are recited at a high level of generality when considered both individually and as a whole. Thus, Claims 1, 14, and 19 are directed to an abstract idea without an integration into a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
For step 2B, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not amount to more than simply instructing one to practice the abstract idea by using generic computer components to carry out the steps that define the abstract idea, as discussed above. This does not render the claims as being eligible. See MPEP 2106.05(f). The additional elements of using an “electronic online system”, “a processor subsystem”, “a memory”, and “a large language model LLM”, when considered both individually and as an ordered combination did not add significantly more to the abstract idea because they were simply applying the abstract idea using generic computer components. In addition, the claims recite the additional elements which are considered nothing more than a general link to technology because there is no recitation of specifics of how this additional element is being used. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (See MPEP 2106.05(f)). Accordingly, these additional elements, do not change the outcome of the analysis, and claims 1, 14, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Claims 2-4, 7-9, 15, and 17 recite limitations that further define the same abstract idea of independent claims to include wherein the indication of the law includes a filename of a document that includes the law, wherein the indication of the law includes a universal resource locator (URL) of a document that includes the law, wherein the indication of the law includes a database identifier of a record that includes the law, and wherein the indication of the business policy includes a filename of a document that includes the business policy, a universal resource locator (URL) of a document that includes the business policy, or a database identifier of a record that includes the business policy. The dependent claims do not include any new additional elements and therefore are considered patent ineligible for the reasons given above.
Claim 5 and 10 recite limitations that further define the same abstract idea of independent claims to include wherein parse the law to produce law chunks, apply a chunking algorithm to the law and wherein to parse the business policy to produce policy chunks, apply a chunking algorithm to the business policy. In addition, claim 5 ad 10 recite the additional element “the processor subsystem” which is considered nothing more than a general link to a technological environment because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore claim 5 and 10 are patent ineligible.
Claims 6, 11, 16, and 18, recite limitations that further define the same abstract idea of independent claims to include wherein the chunking algorithm includes at least one of: naïve splitting, recursive chunking, or semantic chunking. In addition, claim 6,11,16, and 18 recite the additional element “a sentence-level tokenizer process” and “a sentence-level tokenizer process with context preservation” which is considered nothing more than a general link to a technological environment because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore claim 6, 11, 16, and 18 are patent ineligible.
Claims 12-13, and 20 recite limitations that further define the same abstract idea of independent claims to include wherein calculate a vector representation of a law chunk; calculate a vector representation of a policy chunk; use a vector comparison operation to compare the vector representation of the law chunk to the vector representation of the policy chunk, the vector comparison producing a similarity score, and wherein the vector comparison is one of: a dot product operation, a cosine similarity operation, or a soft cosine similarity operation. The dependent claims do not include any new additional elements and therefore are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 103
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hoang et al. (US20230237399) in view of Chen et al. (US 20250182138), in view of Massie et al. (US 20250272581), further in view of PONDICHERRY MURUGAPPAN et al. (US20200111023).
With regards to Claim 1, Hoang et al. teaches an electronic online system for compliance analysis of an organization, the online system comprising (See Abstract & FIG 1):
a processor subsystem; (See [0003]-Various embodiments for identifying and correlating regulatory data with executable rules in a computing environment by a processor are provided.)
a memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to (See [0053]- system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.):
parse the law to produce law chunks; (See [0021]- legislation, laws, policies, regulations, or a combination thereof may be extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity. Semantic data extracted from a law, a policy, a regulation, or a combination thereof may be associated with text data, from the one or more data sources, describing at least a portion of the law, the policy, the regulation, or a combination thereof.)
parse the business policy to produce policy chunks; See [0019]- aligning and correlating existing policy rules (code) to specific sections/paragraphs in policy and legislative text. Also [0021]- legislation, laws, policies, regulations, or a combination thereof may be extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity. Said differently, the obligation targets/content extraction component may perform a perceptron algorithm to extract entity classes. Also See [0022]- a one or more entities may be extracted using one or more natural language processing (NLP) and/or named-entity recognition (NER) operations.)
compare the business-relevant law chunks with the policy chunks to determine similarity scores for respective pairs of law chunks and policy chunks; See [0093]- the regulatory data correlation system 430 may perform one or more various types of calculations or computations. The calculation or computation operations may be performed using various mathematical operations or functions that may involve one or more mathematical operations (e.g., similar thresholds for combined variables, etc.). Also See [0099]- each of the logical structure may be extracted from the texts from the policy document 510 and the rule document 520 and uses the logical structure to compare to the rule logical structure from the policy document 510 to obtain the candidate rules that have similar structures from the rule document 520. Also See [0100]-In order to determine the matching scores (e.g., paragraph-rule matching scores) to correlate semantic data (e.g., a paragraph of text of a policy) to rule data, the determination may be performed as follows. In one implementations, for a given a set of rules (e.g., legislation rules, policies, regulations, etc.), R=r.sub.1, r.sub.2, r.sub.n and a set of texts T=T.sub.1, T.sub.2, . . . , T.sub.n (e.g., text paragraph in legislation policy texts) identify and locate the most optimal, best, or closest alignment of rules to texts, i.e., for each rule R finds the best matching texts T, as depicted in the matching score table 530.)
provide the law chunks to a large language model LLM with a prompt to determine whether each law chunk is a business-relevant law chunk that is relevant to the organization, a business line of the organization, a product line of the organization, a business area of the organization, or a market area, (See [0016]- Moreover, entities (e.g., businesses, governments, organizations, academic institutions, etc.) may be subject to certain processes, policies, guidelines, rules, laws, and/or regulations relevant to the entities. Also See [0025]- Examples of concepts or topics may include, but are not limited to, regulatory compliance information, policy information, legal information, governmental information, business information, educational information, or any other information group. Also See [0125]- the operations of 900 may use natural language processing (NLP) to determine the semantic data and text data with obligation content. The semantic data and text data may be sentences and the obligation content requires an obligation to conform to the obligation, the law, the policy, the regulation, or a combination thereof, initialize a machine learning mechanism to learn, determine, or identify the obligation from the one or more segments of the text data, and/or generate a compliance corpus from training one or more machine learning models for managing regulatory compliance. The operations of 900 may define an obligation as a required action for compliance with a law, policy, regulation, or a combination thereof, a prohibition of conduct, behavior, or activity of the entity, a legal right, a constraint of the entity, or a combination thereof. Also See [0129]-The operations of 900 may initialize a machine learning mechanism to associate the semantic data of the law, the policy, the regulation, or a combination thereof with text data using a machine learning operation.)
Hoang et al. teaches a law and business policy but does not teach receive, from a user of the electronic online system, an indication of a law for analysis or receive, from the user, an indication of a business policy for analysis. However, Chen et al. teaches:
receive, from a user of the electronic online system, an indication of a law for analysis; (See [0011]- such that a user can request any of a number of different types or classifications of the enterprise data. Also See [0030]- the updated query from operation 260 may include a requested action to be performed by the data warehouse 120 (or by the policy system 110 within the data warehouse 120) for the stored enterprise data. Also See [0039]- The external source (or sources) may be the data sources 130, and the policy system 110 may receive enterprise data from the data sources 130 automatically (e.g., on a “push” system) or in response to a request from the policy system 110 (e.g., on a “pull” system). The policy system 110 may process the received enterprise data in order to align a format of the received data with previously-stored data in the centralized data warehouse (e.g., data warehouse 120), to normalize data for analysis with other dissimilar data (e.g., normalizing to a value between 0 and 1), and to label or notate the received enterprise data to facilitate future requests or queries.)
receive, from the user, an indication of a business policy for analysis; (See [0011]- such that a user can request any of a number of different types or classifications of the enterprise data. Also See [0024]- The policy system 110 may determine compliance, and may weight the levels of compliance with each policy (or risk) based on the provided weight. For example, if the policy system 110 determines that the returned enterprise data has a high level of compliance with a policy associated with a high weight and a low level of compliance with a policy associated with a low weight, the policy system 110 may generate a relatively high weighted value.)
Hoang et al. and Chen et al. are both considered to be analogous to the claimed invention because they are in the same field of identifying and correlating regulatory data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Hoang et al. reference to further include receive, from a user of the electronic online system, an indication of a law for analysis or receive, from the user, an indication of a business policy for analysis as taught by Chen et al. This is desirable such that by creating this shared and centralized data warehouse, the system may improve the refresh rate of enterprise data, as the system may automatically and periodically refresh each of the data sources to access updated enterprise data rather than a user having to manually refresh each source. (See Chen, [0010).
The Hoang-Chen combination does not teach discard [law] chunks determined to be not relevant.
However, Massie et al. teaches:
discard [law] chunks determined to be not relevant; (See [0094]- In such instances, a prompt may be provided to the user to remove irrelevant or duplicative sections and/or to upload additional documents providing information for weak areas of the knowledge database.)
Hoang et al., Chen et al., and Massie et al. are all considered to be analogous to the claimed invention because they are in the same field of identifying and correlating regulatory data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Hoang-Chen combination to further include discard [law] chunks determined to be not relevant as taught by Massie et al. This is desirable such that may be used to select between predetermined responses received from managers and other administrative users and information contained within previously-submitted documents when generating responses. (See Massie, [0005]).
The Hoang reference teaches a law chunks but the Hoang-Chen-Massie combination does not teach present [law chunks] that have similarity scores less than a threshold similarity score to the user.
PONDICHERRY MURUGAPPAN et al. teaches:
present [law chunks] that have similarity scores less than a threshold similarity score to the user (See [0051]- corresponding sections such as the requirements sections or the definitions sections of one of the prior domain-specific documents identified as relevant and the received domain-specific document 150 can be analyzed for similarity. If any dissimilarities are identified or if the corresponding sections are found to have less similarity than a predetermined similarity threshold, then such sections can be pointed out to the user on one of the GUIs 180. Therefore, the data processing system 100 is enabled to identify similarities and disparities between complex textual documents such as regulations.).
Hoang et al., Chen et al., Massie et al., and Pondicherry Muragappan et al. are all considered to be analogous to the claimed invention because they are in the same field of identifying and correlating regulatory data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Hoang-Chen- Massie combination to further include present [law chunks] that have similarity scores less than a threshold similarity score to the user as taught by Pondicherry Muragappan et al. This is desirable such that the regulatory data processing system disclosed herein further enables building a regulatory compliance toolkit wherein regulatory knowledge can be offered as a service on a computing platform. (See Pondicherry Muragappan, [0027]).
In regards to Claim 14 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention similar to Claim 1 with the addition of:
Hoang et al. further teaches:
A method for compliance analysis, the method performed on an electronic online system, the method comprising: (See Abstract & FIG 1):
In regards to Claim 14 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention similar to Claim 1 with the addition of:
Hoang et al. further teaches:
A non-transitory machine-readable medium comprising instructions for compliance analysis, which when executed by a machine in an electronic online system cause the machine to: (See Abstract & FIG 1, Also See [0122]- The functionality 900 may be implemented as a method executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine-readable storage medium.)
In regards to Claim 2 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the law includes a filename of a document that includes the law (See FIG 5, Also See [0023]- the term regulation may be a document written in natural language containing a set of legislation, laws, policies, regulations, regulatory targets, entities, and requirements specifying obligations, obligation targets, constraints and preferences pertaining to the desired structure and behavior of an enterprise. Also See [0095]- a policy document 510 (e.g., policy documents from one or more data sources) and a rule document 520 (e.g., a set of rules, laws, or regulations from one or more data sources) may be analyzed and text data (“T” such as, for example, T.sub.1, T.sub.2, and T.sub.n) may be ingested. One or more segments of text data may be extracted from the policy document 510 and the legislation document 520. Each entity within the regulatory text (e.g., the legislation document 402) may be identified and extracted.).
In regards to Claim 3, the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the law includes (See FIG 5, Also See [0095]- a policy document 510 (e.g., policy documents from one or more data sources) and a rule document 520 (e.g., a set of rules, laws, or regulations from one or more data sources) may be analyzed and text data (“T” such as, for example, T.sub.1, T.sub.2, and T.sub.n) may be ingested. One or more segments of text data may be extracted from the policy document 510 and the legislation document 520. Each entity within the regulatory text (e.g., the legislation document 402) may be identified and extracted.).
Hoang et al. teaches the indication of the law, but does not teach a universal resource locator (URL) of a document that includes the law. However, Massie et al. teaches:
a universal resource locator (URL) of a document that includes the law (See [0059]- The server 102 may receive the question from the user device 134, and an indication of the entity or the MDU, and the server 102 may retrieve one or more responses from the databases 132 that correspond to the question and the indication… the user may enter the question “Are pets allowed at Sunnyside Apartments?” In this example, the server 102 may access the databases 132 based on the question and the indication of Sunnyside Apartments to retrieve two responses linked with the question, which are provided by the server 102 to the user device 134 for outputting to the user. To further illustrate, the GUI at the user device 134 may display the following text: “The pet policy for Sunnyside Apartments may be found here: <link>”, where “<link>” is a link (e.g., a uniform resource locator (URL)) to a copy of an electronic document for the pet policy.).
Hoang et al., Chen et al., Massie et al., and Pondicherry Muragappan et al. are all considered to be analogous to the claimed invention because they are in the same field of identifying and correlating regulatory data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Hoang-Chen- Massie-Pondicherry Muragappan combination to further include a universal resource locator (URL) of a document that includes the law as taught by Massie et al. This is desirable such that may be used to select between predetermined responses received from managers and other administrative users and information contained within previously-submitted documents when generating responses. (See Massie, [0005]).
In regards to Claim 4 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the law includes a database identifier of a record that includes the law (See [0071]-The learned content of the data sources consumed by the NLP system may be merged into a database 420 (and/or knowledge store) or other data storage method of the consumed content with learned concepts, methods, and/or features of the data sources 401-403 providing association between the content referenced to the original data sources 401-403. Also See [0075]-The regulatory data correlation system 430 may also include an identification component 432. The identification component 432 may use data retrieved directly from one or more data sources or stored in the database 420. The identification component 432 may identify segments, sentences, phrases, paragraphs, and topics that pertain to the one or more decisions, identify each decision element pertaining to the one or more decisions and the criteria of each of the one or more decisions, and/or identify and extract the criteria and one or more alternative suggestions relating to the one or more decisions.).
In regards to Claim 5 and 16 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein to parse the law to produce law chunks, the processor subsystem is to apply a chunking algorithm to the law (See [0021]- legislation, laws, policies, regulations, or a combination thereof may be extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity. Also See [0084]- generate a cluster of entities identified from the one or more segments of the text data. Also See [0095]- One or more segments of text data may be extracted from the policy document 510 and the legislation document 520. Each entity within the regulatory text (e.g., the legislation document 402) may be identified and extracted. Also See [0089]- Some non-limiting examples of unsupervised learning which may be used with the present technology include artificial neural network, data clustering, expectation-maximization, self-organizing map, radial basis function network, vector quantization, generative topographic map, information bottleneck method, IBSEAD (distributed autonomous entity systems based interaction), association rule learning, apriori algorithm, eclat algorithm, FP-growth algorithm, hierarchical clustering, single-linkage clustering, conceptual clustering, partitional clustering, k-means algorithm, fuzzy clustering, and reinforcement learning.)
In regards to Claim 6 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the chunking algorithm includes at least one of: semantic chunking (See [0089], Also See [0018]- Semantic data extracted from a law, a policy, a regulation, or a combination thereof may be associated with text data, from the one or more data sources, describing at least a portion of the law, the policy, the regulation, or a combination thereof. Also See [0022]- A set of sentences with an obligation-like content may be determined (e.g., computed) using the extraction operation and one or more filtering operations applied to the content of semantic roles from the sentences. Also See [0083]- extract one or more logical structures from the text data; and identify a portion of the semantic data as candidate semantic data by comparing the one or more logical structures with the semantic data.).
In regards to Claim 7 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the business policy includes a filename of a document that includes the business policy (See FIG 5, Also See [0022]- a one or more entities may be extracted using one or more natural language processing (NLP) and/or named-entity recognition (NER) operations. A NER operation may be a subtask of information extraction that may locate and classify named entities in text into pre-defined categories such as, for example, persons, entities, organizations, and locations. Also See [0023]- the term regulation may be a document written in natural language containing a set of legislation, laws, policies, regulations, regulatory targets, entities, and requirements specifying obligations, obligation targets, constraints and preferences pertaining to the desired structure and behavior of an enterprise. Also See [0095]- a policy document 510 (e.g., policy documents from one or more data sources) and a rule document 520 (e.g., a set of rules, laws, or regulations from one or more data sources) may be analyzed and text data (“T” such as, for example, T.sub.1, T.sub.2, and T.sub.n) may be ingested. One or more segments of text data may be extracted from the policy document 510 and the legislation document 520. Each entity within the regulatory text (e.g., the legislation document 402) may be identified and extracted.).
In regards to Claim 8 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the business policy includes (See [0019]- aligning and correlating existing policy rules (code) to specific sections/paragraphs in policy and legislative text. Also [0021]- legislation, laws, policies, regulations, or a combination thereof may be extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity. Said differently, the obligation targets/content extraction component may perform a perceptron algorithm to extract entity classes. Also See [0022]- a one or more entities may be extracted using one or more natural language processing (NLP) and/or named-entity recognition (NER) operations.)
Hoang et al. teaches the indication of the business policy, but does not teach a universal resource locator (URL) of a document that includes the business policy. However, Massie et al. teaches:
a universal resource locator (URL) of a document that includes the business policy (See [0059]- The server 102 may receive the question from the user device 134, and an indication of the entity or the MDU, and the server 102 may retrieve one or more responses from the databases 132 that correspond to the question and the indication… the user may enter the question “Are pets allowed at Sunnyside Apartments?” In this example, the server 102 may access the databases 132 based on the question and the indication of Sunnyside Apartments to retrieve two responses linked with the question, which are provided by the server 102 to the user device 134 for outputting to the user. To further illustrate, the GUI at the user device 134 may display the following text: “The pet policy for Sunnyside Apartments may be found here: <link>”, where “<link>” is a link (e.g., a uniform resource locator (URL)) to a copy of an electronic document for the pet policy.).
Hoang et al., Chen et al., Massie et al., and Pondicherry Muragappan et al. are all considered to be analogous to the claimed invention because they are in the same field of identifying and correlating regulatory data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Hoang-Chen- Massie-Pondicherry Muragappan combination to further include a universal resource locator (URL) of a document that includes the business policy as taught by Massie et al. This is desirable such that may be used to select between predetermined responses received from managers and other administrative users and information contained within previously-submitted documents when generating responses. (See Massie, [0005
In regards to Claim 9 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the business policy includes a database identifier of a record that includes the business policy (See [0071]-The learned content of the data sources consumed by the NLP system may be merged into a database 420 (and/or knowledge store) or other data storage method of the consumed content with learned concepts, methods, and/or features of the data sources 401-403 providing association between the content referenced to the original data sources 401-403. Also See [0075]-The regulatory data correlation system 430 may also include an identification component 432. The identification component 432 may use data retrieved directly from one or more data sources or stored in the database 420. The identification component 432 may identify segments, sentences, phrases, paragraphs, and topics that pertain to the one or more decisions, identify each decision element pertaining to the one or more decisions and the criteria of each of the one or more decisions, and/or identify and extract the criteria and one or more alternative suggestions relating to the one or more decisions.).
In regards to Claim 10 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein to parse the business policy to produce policy chunks, the processor subsystem is to apply a chunking algorithm to the business policy (See [0021]- legislation, laws, policies, regulations, or a combination thereof may be extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity. Also See [0084]- generate a cluster of entities identified from the one or more segments of the text data. Also See [0095]- One or more segments of text data may be extracted from the policy document 510 and the legislation document 520. Each entity within the regulatory text (e.g., the legislation document 402) may be identified and extracted. Also See [0089]- Some non-limiting examples of unsupervised learning which may be used with the present technology include artificial neural network, data clustering, expectation-maximization, self-organizing map, radial basis function network, vector quantization, generative topographic map, information bottleneck method, IBSEAD (distributed autonomous entity systems based interaction), association rule learning, apriori algorithm, eclat algorithm, FP-growth algorithm, hierarchical clustering, single-linkage clustering, conceptual clustering, partitional clustering, k-means algorithm, fuzzy clustering, and reinforcement learning.).
In regards to Claim 11 and 18 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the chunking algorithm includes at least one of: semantic chunking (See [0089], Also See [0018]- Semantic data extracted from a law, a policy, a regulation, or a combination thereof may be associated with text data, from the one or more data sources, describing at least a portion of the law, the policy, the regulation, or a combination thereof. Also See [0022]- A set of sentences with an obligation-like content may be determined (e.g., computed) using the extraction operation and one or more filtering operations applied to the content of semantic roles from the sentences. Also See [0083]- extract one or more logical structures from the text data; and identify a portion of the semantic data as candidate semantic data by comparing the one or more logical structures with the semantic data.).
In regards to Claim 12 and 20 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein to compare the law chunks with the policy chunks to determine similarities, the processor subsystem is to: calculate a vector representation of a law chunk; calculate a vector representation of a policy chunk; (See [0111]- a set of named entities may be mapped to one or more paragraphs of texts. For example, a set of entities S may be the input set of named entities (derived from a rule R). Given a paragraph of text, a set of paragraph of text P of named entities from the paragraph may be extracted. Also See [0113] The variable S may be converted into a vector VS using a pre-trained embedding model. The variable of the paragraph of text P maybe into a vector VP in the same embedding space. A similarity metric may be determined between VS and VP (e.g., such as, for example, a cosine similarity). A pre-trained embedding model may be used, for example, to convert variable S into the vector VS. There may need be an intermediate step, in which the variable S is converted into a sentence (text) and then use the embedding model to convert this sentence into the vector VS. This may be performed using the named entities in set of entities S.)
use a vector comparison operation to compare the vector representation of the law chunk to the vector representation of the policy chunk, the vector comparison operation producing a similarity score (See [0113] The variable S may be converted into a vector VS using a pre-trained embedding model. The variable of the paragraph of text P maybe into a vector VP in the same embedding space. A similarity metric may be determined between VS and VP (e.g., such as, for example, a cosine similarity). A pre-trained embedding model may be used, for example, to convert variable S into the vector VS. There may need be an intermediate step, in which the variable S is converted into a sentence (text) and then use the embedding model to convert this sentence into the vector VS. This may be performed using the named entities in set of entities S. Also See [0127] The operations of 900 may generate a cluster of entities identified from the one or more segments of the text data. The operations of 900 may assign a matching score between the semantic data and the text data, wherein the score indicates a degree of corresponding relevance between the semantic data and the text data.)
In regards to Claim 13 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the vector comparison is one of: a cosine similarity operation See [0113] The variable S may be converted into a vector VS using a pre-trained embedding model. The variable of the paragraph of text P maybe into a vector VP in the same embedding space. A similarity metric may be determined between VS and VP (e.g., such as, for example, a cosine similarity). A pre-trained embedding model may be used, for example, to convert variable S into the vector VS. There may need be an intermediate step, in which the variable S is converted into a sentence (text) and then use the embedding model to convert this sentence into the vector VS. This may be performed using the named entities in set of entities S)
In regards to Claim 15 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the law includes a filename of a document that includes the law, a universal resource locator (URL) of a document that includes the law, or a database identifier of a record that includes the law (See [0071]-The learned content of the data sources consumed by the NLP system may be merged into a database 420 (and/or knowledge store) or other data storage method of the consumed content with learned concepts, methods, and/or features of the data sources 401-403 providing association between the content referenced to the original data sources 401-403. Also See [0075]-The regulatory data correlation system 430 may also include an identification component 432. The identification component 432 may use data retrieved directly from one or more data sources or stored in the database 420. The identification component 432 may identify segments, sentences, phrases, paragraphs, and topics that pertain to the one or more decisions, identify each decision element pertaining to the one or more decisions and the criteria of each of the one or more decisions, and/or identify and extract the criteria and one or more alternative suggestions relating to the one or more decisions.).
In regards to Claim 17 the Hoang-Chen-Massie-Pondicherry Murugappan combination teaches the claimed invention as recited in the independent claim above.
Hoang et al. further teaches:
wherein the indication of the business policy includes a filename of a document that includes the business policy, a universal resource locator (URL) of a document that includes the business policy, or a database identifier of a record that includes the business policy (See [0071]-The learned content of the data sources consumed by the NLP system may be merged into a database 420 (and/or knowledge store) or other data storage method of the consumed content with learned concepts, methods, and/or features of the data sources 401-403 providing association between the content referenced to the original data sources 401-403. Also See [0075]-The regulatory data correlation system 430 may also include an identification component 432. The identification component 432 may use data retrieved directly from one or more data sources or stored in the database 420. The identification component 432 may identify segments, sentences, phrases, paragraphs, and topics that pertain to the one or more decisions, identify each decision element pertaining to the one or more decisions and the criteria of each of the one or more decisions, and/or identify and extract the criteria and one or more alternative suggestions relating to the one or more decisions.).
Response to arguments
Applicant's arguments filed on January 30, 2026 have been fully considered but they are not persuasive.
The comments regarding the 35 USC 101 rejection are noted. On page 7 of Applicant’s response, applicant asserts that submits that the claims are rooted in a technical solution implemented in a processor-implemented system. The Examiner respectfully disagrees because the claimed “processor subsystem” merely performs generic data processing steps (receiving, parsing, comparing, scoring, and presenting information) which constitute an abstract idea of evaluating compliance between laws and business policies, which is a certain method of organizing human activity. The recitation of a processor and LLM does not integrate the abstract idea into a practical application, as these elements are recited at a high level of generality and perform generic functions without improving computer functionality or any other technology. Applicant further argues that here, even if portions of the claims could broadly read on aspects of a method of organizing human activity or a mental process, the amended claims integrate any alleged abstract idea into a practical application. The use of a large language model (LLM) (e.g., artificial intelligence) and related technology to analyze text and determine semantic relevancy of their content is a technological solution. The Examiner respectfully disagrees. Examiner notes that the claimed use of a LLM to analyze text and determine semantic relevancy merely automates the abstract idea of evaluating and comparing information, which can be performed manually through known business practices, and thus does not constitute a technological improvement. The LLM and related components are recited at a high level of generality and perform their ordinary functions without any specific implementation that improves computer functionality or transformation of data into a different technological state, and therefore do not integrate the abstract idea into a practical application. Therefore, the amendment does not meaningfully transform the abstract idea into a patent-eligible application. For the reasons mentioned above, the argument to the contrary is not persuasive. Thus, the rejections of Claims 1-20 under 35 USC 101 are maintained.
The comments regarding the 35 USC 103 rejection are noted. On page 8 of Applicant’s response, applicant asserts that the applicant cannot find in the cited portions of Hoang, Chen, Pondicherrv Murugappan, or in the office action’s reasoning, any disclosure of the amended claim limitations as claim 1 presently recites. The examiner refers the applicant to the 103 rejections above, in which the claim elements are mapped to the newly incorporated cited prior art reference of Massie et al. with respect to the claim amendments. Therefore, when combined with the teachings of the original reference, the art reasonably suggests the new claimed limitations. Furthermore, the examiner notes that the applicant has not explained how the amended claim limitations is distinguishable from the cited prior art of record and has instead merely asserted, without analysis, that the references do not teach the limitations. Therefore, without a specific explanation of why the prior art fails to discloses or suggest the amended features, the examiner must maintain the prior rejections. Examiner has reconsidered the claims in light of this prior art, and upon further review, has maintained the 35 USC 103 rejection. Thus, the rejections of Claims 1-20 under 35 USC 103 are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension
of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the
mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this
final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory
period, then the shortened statutory period will expire on the date the advisory action is mailed, and any
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 date of this final
action.
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/A.W.H./ Examiner, Art Unit 3626
/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626