DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a final rejection in response to amendments/remarks filed on 07/20/2026. Claims 1, 8, and 15 are presently amended. Claims 4, 11, and 18 were previously cancelled. Claims 1-3, 5-10, 12-17, 19 and 20 remain pending and are examined herein.
Priority
The effective filing date of the disclosure is the filing date of the present application, 02/02/2024.
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 1-3, 5-10, 12-17, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3, 5-7: A system for client onboarding comprising: at least one processor; and memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:
Claim 8-10, 12-14: At least one non-transitory machine-readable medium comprising instructions for client onboarding that, when executed by at least one processor, cause the at least one processor to perform operations to:
Claim 15-17, 19, 20: A computer-implemented method for client onboarding comprising:
All of the claims fall under at least potentially eligible subject matter category, at least “process, machine, or manufacture,” therefore the claims are to be further analyzed under step 2.
Step 2a Prong 1: Is the claim reciting a Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, 8 and 15 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is in bold and the additional elements have been italicized as follows:
Claim 1 Preamble: A system for client onboarding comprising: at least one processor; and memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:
Claim 8 Preamble: At least one non-transitory machine-readable medium comprising instructions for client onboarding that, when executed by at least one processor, cause the at least one processor to perform operations to:
Claim 15 Preamble: A computer-implemented method for client onboarding comprising:
Claims 1, 8, and 15 Body:
receiv(ing), at an application programming interface (API) gateway, an onboarding request from a self-service onboarding portal;
query(ing) requirements data for onboarding requirements;
deduplicat(ing) the onboarding requirements based on a common taxonomy to create a standardized requirements set by:
tokenizing each onboarding requirement;
computing a similarity score between each tokenized requirement and entries in a predefined canonical taxonomy using a fuzzy logic algorithm, wherein the fuzzy logic algorithm comprises distance calculation with phonetic matching to identify semantically equivalent terms with variant spellings;
applying a predefined decision threshold to the similarity score to select a canonical taxonomy term for each requirement, wherein the predefined decision threshold is dynamically adjusted by a machine learning model based on historical accuracy metrics of previous taxonomy mappings, the machine learning model being retrained using feedback indicating whether previous taxonomy mappings were accepted or corrected;
creating a map of a plurality of requirement elements to a requirement element of the common taxonomy based on the similarity score and decision threshold;
replacing the plurality of requirement elements with the requirement element of the common taxonomy in the standardized requirements set; and
storing the selected canonical taxonomy terms in a hash-map data structure to create the standardized requirements set;
select(ing) user interface elements for presentation in a user interface based on the standardized requirements set;
present(ing) the user interface in the self-service onboarding portal;
execut(ing) an onboarding workflow across internal systems to fulfill requirements of the standardized requirements set using input received from the user interface;
and return(ing) a status of the onboarding request to the self-service onboarding portal.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, 8, and 15 are reciting an abstract idea category of “certain methods of organizing human activity.” More specifically, the present claims fall under the sub-grouping “commercial or legal interactions,” which include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The commercial or legal interaction at hand is “client onboarding,” which is both a necessary step in business relations and is also a legal interaction as it consists of steps to ensure compliance with “know your customer” standards. The present specification, particularly [0009], states, “There may be a heavy reliance on manual forms and data re-entry prolonging the onboarding process. [0010] The systems and techniques discussed herein address the shortcomings of current client onboarding processes by providing seamless digital client outreach, accurate client identification and data reuse, aggregation and deduplication of onboarding requirements, process orchestration and status transparency using artificial intelligence (AID) / machine learning (ML) for data extraction and auto-population.” According to the present specification, the claims improve upon onboarding process by removing reliance on manual forms and implementing the data processing features listed above. This would be considered an improvement to the abstract idea, inherent to the use of generic data processing on generic computing components. Since the functional steps, within the scope of the claim limitations in bold, recite steps corresponding to the intake of onboarding requirements(deemed a commercial or legal interaction), it is merely a recitation of an abstract idea.
Furthermore, even when considering the amended limitations that recite how the deduplication step is to be performed, the steps are still not recited with enough specificity to be more than instructions to an individually to perform data analysis steps. Even the amended steps are mere legal interactions because the steps recite comparing tokenized onboarding requirements to the users entries and determining the similarity of the requirement elements. Ensuring that requirements are fulfilled or storing a set of requirements would fall under agreements in the form of contracts or legal obligations. Furthermore, the data analysis steps encompass instructions to an individual to manage their personal behavior because they are recited at a level of generality such that they are no more than mathematical calculations that can be performed by a human performing the steps mentally with a physical aid. Therefore, the bolded limitations as a whole recite certain methods of organizing human activity, supported by the fact that the data analysis steps also fall within other sub-groupings (mental processes). MPEP 2106.05(a) states, “It should be noted that these groupings are not mutually exclusive, i.e., some claims recite limitations that fall within more than one grouping or sub-grouping. For example, a claim reciting performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within the mathematical concepts grouping and the mental process grouping. Accordingly, examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings and proceed with the analysis in Step 2A Prong Two.”
The following paragraphs cover data Analysis Steps that are recited at a level of generality such that they can be performed by a human with a pen and paper:
More specifically, the broadest reasonable interpretation of “tokenizing each onboarding requirement” in view of the specification can be performed in the human mind with the assistance of a pen and paper. MPEP 2106.04(a)(2)(III) states, “The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea... Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Since the claims cover any way of tokenizing an onboarding requirement, including the most fundamental forms of tokenization such as attributing a number to each letter, therefore, this step can be performed mentally.
Furthermore, the steps of computing a similarity score between each tokenized requirement and entries in a predefined canonical taxonomy using a fuzzy logic algorithm, wherein the fuzzy logic algorithm comprises distance calculation with phonetic matching to identify semantically equivalent terms with variant spellings; when given its broadest reasonable interpretation in view of the specification can still be performed in the human mind with the assistance of pen and paper. For example, a technique such as “Levenshtein distance,” which merely checks the minimum number of single-character edits, can be performed by a human using a pen and paper, resulting in a similarity score. Even when considering the potential complexity of performing this tasks for a large set of onboarding requirements, MPEP 2106.04(a)(2)(III) notes, “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.” Therefore, by performing “computations that can be made by pencil and paper calculations,” and manipulation of data using “pen and paper” methodologies, although claimed as computer-implemented, the steps above can still be performed “by a human, mentally or with and paper.”
Similarly, the amended step of applying a predefined decision threshold to the similarity score to select a canonical taxonomy term for each requirement, wherein the predefined decision threshold is dynamically adjusted by a machine learning model based on historical accuracy metrics of previous taxonomy mappings, the machine learning model being retrained using feedback indicating whether previous taxonomy mappings were accepted or corrected; when given its BRI in view of the specification cover any way of “dynamically adjusting” based on a model trained on human feedback based on whether previous taxonomy mappings were accepted or corrected. Given the generality of the claims, training a model using historical accuracy metrics and using feedback indicating whether taxonomy mappings were accepted or corrected, can be performed practically in the human mind with a pen and paper. For example, a model (even a machine learning model) can be a polynomial function that a user manually tunes, adjusts, and recalculates based on observing the outcomes of previous taxonomy mappings. Therefore, the claims are still recited at a level of generality such that the data analysis can be performed by a human using judgment, observations, or any mathematical calculations to determine the predefined decision threshold. Therefore, this is still a recitation of a mental process.
Creating a map...based on the similarity score and decision threshold...and replacing the plurality of requirement elements with the requirement element of the common taxonomy is also merely a data processing step recited at such a high level of generality that it could include mental processes of carrying out the method, and are thereby merely instructions to an individual to perform the “certain method of organizing human activity.”
Storing the selected taxonomy in a hash-map data structure is merely a data storage step, and since it can practically be performed by an individual with a pen and paper, it is still a recitation of an abstract idea.
Therefore, when considering that the claim as a whole recites “certain methods of organizing human activity,” with data collection, data analysis, and display steps, wherein the analysis steps are recited at such a high level of generality that they can be performed by a human with a pen and paper, then the claims are reciting an abstract idea. As will be further explained in the Prong 2 analysis, even interactions between a person and a computer can be “certain methods of organizing human activity,” and steps requiring a computer may still recite a mental process. MPEP 2106.05(a)(2)(II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” MPEP 2106.05(a)(2)(III)(C), “For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”
Therefore, the claims at least recite at least one abstract idea under “certain methods of organizing human activity.”
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 8, and 15 recite the following additional elements:
- A system for comprising: at least one processor; and memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to: in claim 1
- non-transitory machine-readable medium in claim 8
- computer-implemented method in claim 15
- application programming interface (API) gateway in claims 1, 8, 15
- self-service onboarding portal in claims 1, 8, 15
-user interface/ user interface elements in claims 1, 8, 15
- machine learning model being retrained using feedback indicating whether previous taxonomy mappings were accepted or corrected; in claims 1, 8, 15
The additional elements listed above, when considered individually and in combination with the claim as a whole, no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on generic computing components as outlined in MPEP 2106.05(f). In this case, the abstract idea of “client onboarding” is merely instructed to be performed on generic computing components such as processor, memory, non-transitory machine-readable medium, computer, and self-service onboarding portal. As seen in Fig. 5, the computing infrastructure is merely a generic computer, with no particular improvements to the computing devices themselves, which is one of the considerations as to whether an additional element integrates the abstract idea into a practical application.
Furthermore, the additional elements of “API gateway” and “user interface elements” are a general link to a particular technological environment or field of use as outlined in MPEP 2106.05(h). Limiting the step of receiving input to an API gateway does not meaningfully limit the claims to more than generally linking the abstract idea to the technological environment of an API gateway. Similarly, determining user interface elements and presenting the elements to a user interface of a user is recited so generally that is no more than a general link to the field of user interfaces. The claims fail to indicate particular structural elements of the user interface, therefore there is no possible improvement to the field of user interface technology.
Additionally, the use of a machine learning model trained on “feedback indicating whether previous taxonomy mappings were accepted or corrected” is also equivalent to “apply it” and is a general link to the technical field of machine learning. It is apply it because it merely recites the use of machine learning in a black box manner, such that it does not specify how the intended outcome is arrived at using machine learning. “Training the model using feedback,” does not meaningfully limit how the machine learning model arrives at the claimed solution. MPEP 2106.05(f) states, “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".” Furthermore, other than naming the model as a “machine learning” model, the additional element does not amount to more than generally linking the abstract idea to a particular field of use (machine learning). Finally, even when considering the machine learning model, along with the processors, memories, machine readable mediums, computer, and the API gateway, user interface elements, and portals, the claims recite each of the additional elements at such a high level of generality that even when considered in combination, they are no more than “apply it,” because it is merely performing the abstract idea on generic computing components or a technological environment.
Therefore, the additional elements, whether analyzed individually or in an ordered combination in the context of the claims, fail to integrate the abstract idea into a practical application. Therefore, the claims are directed to an abstract idea without integration into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 8, and 15 recite the following additional elements:
- A system for comprising: at least one processor; and memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to: in claim 1
- non-transitory machine-readable medium in claim 8
- computer-implemented method in claim 15
- application programming interface (API) gateway in claims 1, 8, 15
- self-service onboarding portal in claims 1, 8, 15
-user interface/ user interface elements in claims 1, 8, 15
- machine learning model being retrained using feedback indicating whether previous taxonomy mappings were accepted or corrected; in claims 1, 8, 15
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computing components such as processor, memory, non-transitory machine-readable medium, computer, and self-service onboarding portal to perform the abstract idea of “client onboarding” amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, such limiting the “moderator” to be automated. Furthermore, limiting the abstract idea to be performed on application programming interface (API) gateway, using user interface elements, or using a machine learning model trained on feedback indicating whether previous taxonomy mappings were accepted or corrected does not meaningfully limit the claim beyond generally linking the abstract idea to a particular technological environment or field of use. Accordingly, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 8, and 15 are not patent eligible because the claims are directed to an abstract without significantly more.
Dependent claims 2-3, 5-7, 9, 10, 12-14, 16-17, 19, and 20 are also given the full two part analysis both individually and in combination with the claims they depend on herein:
Claims 2, 9 and 16 further define the abstract idea by implementing a machine learning model to perform the determination of onboarding requirements. This is more of the same abstract idea since it still recite the steps of determining onboarding requirements, which is still a certain method of organizing human activity as it is determines compliance requirements, which are legal obligations. When analyzing “machine learning model” as an additional element, the claims have been found to be no more than a general link to a particular technological environment or field of use as outlined in MPEP 2106.05(h). In this case, the machine learning model is generally recited to perform prediction using historical data, which is inherent to generic machine learning technology. Therefore, there are no improvements to the field of machine learning, which is a consideration found in MPEP 2106.05(a). Therefore, the additional elements, whether analyzed individually or as an ordered combination with the additional elements found in the claims depended upon, fail to integrate the abstract idea into a practical application or provide significantly more. Therefore, the claims are still directed to an abstract idea without integration into a practical application and is not an inventive concept.
Claims 3, 10, and 17 merely further limit the abstract idea by performing mere data processing steps associated with generating a unique client identifier. This is merely an embellishment of the same abstract idea of “client onboarding” as it is a data collection, processing or output step, towards performing the legal interaction. Since there are no further additional elements to consider, the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claims 4, 11, and 18 further defines the abstract idea by reciting that a “fuzzy logic algorithm” is used to create a map of requirement elements and replacing the requirement elements of the common taxonomy from the standardized requirements step. These steps are merely mathematical steps and data processing to perform the abstract idea of “client onboarding.” Therefore, whether analyzed individually or as an ordered combination, the claim elements are more of the same abstract idea under the ”certain methods of organizing human activity” subcategory of “commercial or legal interactions.” Since there are no further additional elements to consider, the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 5, 12, and 19 further limits the abstract idea by limiting the user interface elements to include a document upload interface. This is more of the same abstract idea since a “document upload interface” does not recite specific structure that would meaningfully limit the abstract idea more than generally linking the abstract idea to a particular technological environment or field of use. Therefore, whether considering it part of the same abstract idea or as an additional element “document upload interface” whether considered individually or in combination, does not integrate the abstract idea into a practical application or provide significantly more.
Claims 6, 13, and 20 further limits the abstract idea by reciting generic data processing steps towards performing the same abstract idea of client onboarding. All of the steps recited in the dependent claim, even when combined with the claims depended upon are merely reciting the receiving, transmitting, obtaining, and compiling of data in a general manner. Since there are no further additional elements to consider, the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 7 and 14 further define the abstract idea by implementing the steps of “generate an onboarding request block in a distributed ledger, and updating the request block with the status of the onboarding request. The updating and data storage steps are merely data processing steps performing the abstract idea. When considering “block” and “distributed ledger” as additional elements, these additional elements are merely a general link to the technological environment of blockchain technology. Since the claims recite blockchain technology to perform generic functions inherent to the field (such as storing data on a block in a distributed ledger, and updating a status of the block), it does not meaningfully limit the claims more than generally linking the abstract idea to a particular technological environment or field of use as outlined in MPEP 2106.05(h). Furthermore, such storage of onboarding request data to the blockchain does not constitute an improvement to the technological environment of blockchain, which is a consideration under in MPEP 2106.05(a). Therefore, whether considered individually or combination with the claims depended upon, the additional elements do not integrate the abstract idea into a practical application or provide significantly more to consider the claims an inventive concept.
Subject Matter Distinguished Over Prior Art
Claims 1, 8, and 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The following is a statement of reasons for the indication of allowable subject matter:
Anapliotis, Gordon, and the prior art of record fail to teach:
- tokenizing each onboarding requirement;
- computing a similarity score between each tokenized requirement and entries in a predefined canonical taxonomy using a fuzzy logic algorithm, wherein the fuzzy logic algorithm comprises distance calculation with phonetic matching to identify semantically equivalent terms with variant spellings;
- applying a predefined decision threshold to the similarity score to select a canonical taxonomy term for each requirement, wherein the predefined decision threshold is dynamically adjusted by a machine learning model based on historical accuracy metrics of previous taxonomy mappings, the machine learning model being retrained using feedback indicating whether previous taxonomy mappings were accepted or corrected;
- creating a map of a plurality of requirement elements to a requirement element of the common taxonomy based on the similarity score and decision threshold;
- replacing the plurality of requirement elements with the requirement element of the common taxonomy in the standardized requirements set; and
- storing the selected canonical taxonomy terms in a hash-map data structure to create the standardized requirements set;
Riediger et al. (US 20150074507 A1) does teach a fuzzy logic algorithm to find matching characters between training and target data, however, it would not have been obvious to one of ordinary skill in the art to combine these teachings with the teachings of Anapliotis and Gordon to predictably arrive at the limitations above. Riediger’s fuzzy match algorithm specifically uses fuzzy matching to match characters, however, Riediger does not teach that this algorithm is applied “computing a similarity score between each tokenized requirement and entries in a predefined canonical taxonomy using a fuzzy logic algorithm;” nor does Riediger teach: wherein the fuzzy logic algorithm comprises distance calculation with phonetic matching to identify semantically equivalent terms with variant spellings; and wherein the predefined decision threshold is dynamically adjusted based on historical accuracy metrics of previous taxonomy mappings.
Response to Arguments
Applicant's arguments filed 07/20/2026 have been fully considered but they are not persuasive.
The applicant’s arguments over the rejections under 35 U.S.C. 101 have been fully considered, but are not persuasive for the following reasons:
The applicant alleges that the claimed advance is the particular pipeline, “not the business goal of onboarding a client,” and the examiner clarifies that the mere goal of onboarding a client is not what makes the claims ineligible under 35 U.S.C. 101, but it is the claims as a whole that fail to integrate the abstract idea into a practical application or provide significantly more.
The applicant points to McRo, Inc… alleging that the claims provide a “specific way” of achieving a result rather than merely claiming the idea of a solution or outcome. The examiner clarifies that the claims have not been oversimplified and are analyzed in the full breadth of the claim language, determining that within the breadth of the actual claim language, the claims can be satisfied by a human user performing the limitations manually as mental steps with the assistance of a pen and paper. Therefore, the applicant’s argument that McRo rejects the inference that a human can perform “comparable steps,” is not persuasive because it is a mischaracterization of the examiner’s arguments. The examiner argues not that the claims can perform “comparable steps,” or whether the result can be reached by “other means,” but that the claims itself are broad enough such that given the BRI of the claims, a human user can perform the exact steps mentally (other than merely “applying it” to a general purpose computer). Furthermore, in response to the argument that the claims are not merely a “result state functionality,” the examiner notes that the consideration of whether the claims are “results-oriented,” is merely a consideration under MPEP 2106.05(f), for whether the claims are integrated into a practical application or if they are merely “apply it.” Even assuming arguendo that the claims recite the full mechanism, if the full mechanism is part of the abstract idea, it is still a recitation of the abstract idea without significantly more. Therefore, the “mapping a Business License to the standardized Registration Certificate” in the KYC domain to “Registration Certificate” and removing the redundancy when two domains each request the same document, still falls squarely within at least one abstract idea category and at least “recites” an abstract idea.
The applicant’s assertions that “a machine learning model retrained on accumulated accept-or-correct feedback,” cannot be performed mentally within a pen and paper is not persuasive because other than the word “machine,” nothing limits the model to be trained by a human, with pen and paper alone, as machine learning was originally performed. While the applicant states that the specification teaches, “may be trained on hundreds of thousands of historical onboarding instances,” the scale of data analysis alone does not impact whether the claim itself can be performed by a human, especially when a machine is merely used to inherently increase the rate of analysis. MPEP 2106.05(f) states, “Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept… iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);”
Furthermore, In response to applicant's argument that a human cannot practically perform hundreds of thousands of onboarding instances, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the applicant’s argument citing the 2024 guidance update that “claim limitations that encompass artificial intelligence in a way that cannot practically be performed in the human mind,” is not persuasive because nothing meaningfully limits the machine learning model in the claims such that it cannot be performed practically in the human mind.
Regarding arguments over Step 2A, Prong Two, the applicant alleges that the claims improve “First, the claims improve the operation of the classification mechanism itself: the decision threshold is not merely "adjusted," but is retrained on a specific feedback signal, namely whether previous taxonomy mappings were accepted or corrected, so that the system, as the specification puts it, exhibits "learning over time" and adapts "dynamically rather than through rigid rules.” However, the examiner disagrees because the claims do not reflect a specific way to ensure that the system learns over time and adapts dynamically, other than claiming that “machine learning” would arrive at that claimed outcome. There is no guarantee that any machine learning model within the scope of the claims would provide the claimed outcome, and there is no mechanism for the machine learning to arrive at the claimed outcome based solely on the feedback that it is trained on. Secondly, the alleged improvement of “reconciling variant terminology to canonical terms and storing those terms in a hash-map data structure that drives downstream user interface generation and cross-system orchestration” does not reflect a technological improvement to a particular technical field or computer functionality. This type of “improvement” to how computer stores and accesses data is not the same type of improvement as Enfish as stated in MPEP 2106.05(a). MPEP 2106.05(a) states, “It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology.” Therefore, the applicant’s argument that the claims do not recite a generic “deduplicate step,” but specific operations… is not persuasive, because as argued previously, each of the specific operations can be performed by a human with a pen and paper, and computers are merely applied to increase the speed or output the data of the analysis.
Regarding the arguments over step 2B, the applicant points to AmDocs, in which “enhancing data in a distributed fashion,” solves the technological problem of “massive record flows which previously required massive databases.” However, it is noted that Amdocs is directed to a distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records. Reducing network congestion is an improvement to computer functionality because it meaningfully limited to the field of network communications.” On the other hand, the present claims address a problem within how human activity is organized, “users endure repetitive manual requests,” instead of a network-based solution. Even when considering the full scope of the techniques used to arrive at the solution, since such techniques can be performed mentally with a pen and paper, it is not a technical improvement, the same way that Amdocs is solely a technical improvement. The question of “well-understood, routine, or conventional,” is not relevant to the present claims because the claims can be performed as a mental process or are merely “apply it” or a general link to technology or a particular technological field. There is no burden on the examiner to provide evidence for well-understood, routine, conventional, when there is no claim of such being made. Therefore, the applicant’s argument that “these improvements include, but are not limited to, improvements to computer functionality through computational semantic analysis, adaptive learning capabilities, and optimized data structures” are not persuasive because none of the alleged improvements are meaningfully limited to computer implementation. Therefore, the claims remain rejected under 35 U.S.C. 101.
In regard to arguments over rejections under prior art, the applicant’s amendments to claims 1, 8, and 15 include further limitations to subject matter that has already been deemed free of prior art in the previous office action. Therefore, the amended claims 1, 8, and 15 remain free of prior art. Dependent claims 2-3, 5-7, 9, 10, 12-14, 16-17, 19, and 20 are also free of prior art by virtue of their dependency on amended claims 1, 8, and 15 respectively. The examiner further notes that distinguishing over the prior art does not inherently imply that the claims are directed to a technical improvement, especially when the improvement lies in the abstract idea. Therefore, the applicant’s argument that the combination fails to teach or suggest all of the amendments is persuasive and the rejection of claims under 103 has been withdrawn.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
Prosecution Insights