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
This action is in response to the amendment filed on 5/26/2026. Claims 1, 3-5, 7-14, 16-19 are pending. Claim 1 is amended. Claim 21 was withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 5/26/2026. Claim 21 has also been cancelled.
Response to Arguments
Applicants’ arguments filed 5/26/2026 have been fully considered but they are not persuasive. The applicant has argued “Applicant respectfully disagrees that the claims recite any mental processes. Furthermore, even if the claims did receive an abstract idea, which Applicant does not concede that they do, according to the MPEP.” The examiner respectfully disagrees. The claims recite receiving historical numerical data, constructing a hierarchical mapping of sub-populations, identify entity sub-types and sub populations, generating normal distributions, performing Bayesian inference, projecting inferred statistical values, and filtering entities based on thresholds. These limitations describe mathematical concepts and data analysis techniques, including statistical modeling and probabilistic inference. Mathematical modeling, including the use of probability distributions and Bayesian inference, falls squarely within the category of mathematical concepts. Additionally, organizing and evaluating information to classify or filter entities based on derived values constitutes a mental process because such evaluation and decision-making can be performed in the human mind or with pen and paper, even if the claims recite performance on a computer. The fact that the claims involve large datasets or complex calculations does not remove them from the mental process category. The courts have consistently held that merely performing mathematical calculations on a computer does not render them non-abstract.
The applicant has argued “Here, Applicant's specification identifies a particular problem in the technical field of data modelling to achieve reduced computational resources in data analytics and data mining. In particular, the Specification states at paragraphs [55]-[56] and [62]…”The examiner respectfully disagrees. It appears the applicant is characterizing data modelling, data analytics, and data mining, however the applicant is merely using analytical and statistical activity to perform the claim. The particular problem the claim appears to be solving is a statistical problem not a computing problem (¶ 56, Thus, forming a distribution of the activity-related quantity indices within the sub-population may result in inconsistent or unreliable metrics.). This is a problem inherent to an inference (abstract idea) not a problem related to the computer, or database, or network. The claim does not have any change to a processor architecture, memory management, storage indexing, or network operation only a substitution of one statical method for another. The specifications identification of a particular problem in data modeling is in the world of statistics and is not a technical problem or a technical solution.
The applicant has argued “Thus, Applicant's amended claim 1 ties the model inferred performance distribution to a concrete database operation. The inferred performance distribution is not merely an intermediate mathematical result; it is stored in a specific sub-population index distribution storage in an external database (i.e., the entity database) and then used to filter the population of entity records within that external database. Thus, the claim uses the generated distribution to change how the database is organized, accessed, and reduced to a filtered population of entities based on both the stored inferred distribution and a threshold future performance metric value. This is a practical application of the modeling technique in computer-based database management, not a mere instruction to "apply" a mathematical concept on a generic computer.” The examiner respectfully disagrees. The stored value remains a mathematical result, storing the value is the output of an earlier calculation, which does not change the analysis. Filtering by a threshold is a mental process, not a database management operation. The claim does not recite any change to how the database is organized or accessed. The entity database in the filtering step is the same database. The claim does not recite any specific technical storage structure, does not recite any change to how the database organizing, indexes or access data, and sures the stored value only as an input to a comparison. The applicant is merely using a generic database as a tool to perform the steps of the invention. The claims recite a generic processor to perform the steps of the invention. Although the applicant is arguing specific storage structure, this is asserted, but not claimed. Filtering by a threshold comparison is a mental process not a database management operation. The claim does not recite any change to how the database is organized or accessed; applicant’s arguments assert an effect that the claim language does not support. The claim does not recite any specific technical storage structure, does not recite any change to how the database organizes, indexes, or accesses data, and uses the stored value as an input to a comparison, the storing and filtering are generic computer functions.
The applicant has argued “Thus, the Application as filed identifies a particular technical problem in data analytics and data record processing whereby data scarcity results in either ineffective modelling and/or high computational costs to model the scarce data. Further, the Application as filed identifies a particular solution of a hierarchical segmentation of data records for Bayesian mixture modelling on select sub-populations according to the hierarchical segmentation to compensate for data scarcity. For example, as described in the specification (see, e.g., [55]-[56]), the claimed index distribution model engine improves scoring and ranking of entity records by compensating for scarcity in activity-related data, particularly where sub-populations at lower hierarchy levels may have few or inconsistent numbers of entities, resulting in unreliable metric. This claimed index distribution model engine can reduce or eliminate such simulations and thereby make database operations more efficient, reducing processing times from weeks to hours. See Applicant's Specification, [62]. In addition, as described in the specification (see e.g., [98]-[100], the claimed approach stores the inferred index distribution in a sub-population index distribution storage, uses the stored index distribution to generate inferred statistical values indicative of expected or predicted activity-related quantity indices, and filters records within the entity database based on whether an inferred statistical value exceeds a threshold statistical value.” The examiner respectfully disagrees. Although the applicant is claiming an index distribution storage, the claim does not include an “index distribution model engine.” As was argued above, the particular technical problem identified is a statistical problem, not a technical one. Small sample statistical unreliability is a problem inherent to an inference. Whether or not a computer is involved. Hierarchical segmentation… for Bayesian mixture modelling on select sub-populations is an abstract data organization (categorizing) combined with a mathematical technique, not a technical issue. The applicant is citing paragraph 62 of the specification which appears to be disclosing a change in the algorithm not a change to how the database stores, indexes, retrieves, or organizes records. Applicant’s arguments appear to be directed to things that are not claimed. The applicant also appears to be mischaracterizing a statistical limitation. The claims do not integrate the judicial exception into a practical application or supply an inventive concept.
The applicant has argued “Moreover, as described in the specification (see, e.g., [68], [98]), the claimed approach leverages the combination of the mixture model, and a variational inference mean field algorithm to achieve a substantial reduction in computational runtime. This improvement is not merely a mathematical result, but a concrete enhancement to the operation of the computer itself, enabling the system to generate accurate statistical inferences in hours rather than weeks. This is precisely the type of improvement to computer technology that the USPTO and courts have recognized as patent eligible. See, e.g., DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016).” The examiner respectfully disagrees. The stored value is a mathematical result, specifically writing Data scarcity in modeling is a problem arising in the context of statistical analysis and data analytics, not in the functioning of a computer or other technological system. Limitations relating to insufficient data for statistical modeling concern the quality or reliability of analytical results, which is a mathematical or informational problem rather than a technological one. The courts have consistently held that improving the accuracy or efficiency of an abstract idea i.e. statistical modeling does not, by itself, constitute an improvement to computer technology. Hierarchical segmentation of data records and application of Bayesian mixture modeling to selected sub-populations constitutes the application of mathematical techniques (segmentation, probabilistic modeling, and Bayesian inference) to improve statistical outcomes. These techniques are themselves mathematical concepts. Limiting the modeling to “select sub-populations” according to a hierarchy merely refines how the mathematical analysis is performed; it does not alter how the computer operates, nor does it provide a technological improvement to memory architecture, processor operation, data storage structures, or network functionality. The claims do not recite a new data structure, a specific improvement to database architecture, a novel memory management technique, or any modification to computer processing itself. Rather, they use generic computing components to execute statistical modeling steps more selectively. Reducing computational cost by choosing to model smaller subsets of data is an efficiency improvement in the abstract analytical process, not an improvement to computer technology under MPEP §2106.05(a). Characterizing the hierarchical segmentation as a “particular solution” does not render the claims non-abstract. The hierarchy is recited at a functional level without specifying a concrete technical implementation that changes how data is stored or processed at the hardware or system level. As such, the segmentation framework amounts to organizing information into categories, which is itself an abstract idea.
Under Step 2A, Prong Two, the Examiner maintains that the claims merely apply mathematical modeling techniques to data in a generic computing environment and therefore do not integrate the judicial exception into a practical application.
The applicant has argued “Note that a claim is not "directed to" a judicial exception if it integrates any alleged judicial exceptions into a practical application. Here, the claim does not merely recite a mathematical concept or mental process, but instead requires a specific sequence of steps that result in a technological improvement-namely, a reduction in computational runtime for statistical inference. The use of a hierarchical map object, a variational inference mean field algorithm in combination with a mixture model is not conventional or generic, and the claim as a whole is directed to a specific improvement in computer-based modeling technology - an indicator that the claims integrate any alleged judicial exceptions into a practical application.” The examiner respectfully disagrees. The mathematical techniques are for approximating probability distributions and performing statistical inference. The recitation of a hierarchical map object constitutes organizing data into structured groupings, which is likewise an abstract process of organizing and analyzing information. While the Applicant characterizes the claims as requiring a “specific sequence of steps,” the steps themselves remain mathematical calculations and data organization operations performed on a generic computer. Merely arranging abstract operations in a particular order does not transform them into a technological improvement. Applicant’s reliance on reduced computational runtime is not persuasive. An improvement in the efficiency of performing a mathematical calculation is an improvement to the abstract idea itself. The claims do not recite a specific improvement to processor operation, memory management, database architecture, network communication, or any other aspect of computer functionality. Instead, they invoke generic computing components to execute statistical modeling techniques more efficiently. The asserted “hierarchical map object” is claimed at a functional level and does not define a specific data structure that improves how a computer stores or retrieves data at a technical level. Similarly, variational inference and mixture modeling are known approximation techniques in statistical computing. The claims do not recite a particular unconventional implementation of these techniques beyond their intended mathematical purpose. Unlike cases where eligibility was found based on a specific improvement to computer technology, the present claims improve only the quality and speed of statistical inference. As explained in Federal Circuit precedent, improvements to the accuracy or efficiency of an abstract idea—without improving the computer itself—do not constitute integration into a practical application.
The applicant has argued “Accordingly, even if the claim were considered to recite mathematical concepts, which Applicant does not concede, amended claim 1 integrates any such concepts into a practical application under Step 2A, Prong 2. The claim is directed to a specific computer-implemented process for improving database analytics and database management in data-sparse environments: the system generates an inferred performance distribution without processor-intensive simulations, stores that distribution in a database-associated storage structure, and uses the stored distribution to filter entity records in the entity database. This ordered combination is not a mental process and cannot practically be performed in the human mind or with pen and paper, because it requires processor-based modeling of database records, storage of an inferred distribution in database storage, and filtering of a population of entity records within the entity database based on that stored distribution and threshold future metric values.” The examiner respectfully disagrees. The applicant is reciting a generic computer performing conventional operations which does not remove the claim from the mental process grouping. The MPEP’s practically performed in the mind inquiry is not a question of whether a human could realistically process a large database as fast as a computer; it asks whether the claimed steps themselves are the kind of thing a human, given a paper record and enough time, could carry out using observation, evaluation and judgement. The fact that a database performs the recording and retrieval function faster and at a greater volume than a filing cabinet does not change the nature of the underlying activity. Because each of the storing, modeling, and filtering limitations is recited using only generic computer/database language, and because each could be done using human judgement the ordered combination does not escape the mental process grouping because it merely operates on entity records within a database.
The “Applicant further submits that the improvement recited in the claim storing the inferred performance distribution in a sub-population index distribution storage in an entity database cannot be performed as a mental process or with pen and paper. It is inherently tied to the operation of a computer and addresses a problem unique to computer-based data modeling. The claim does not preempt all uses of mixture models or variational inference, but is limited to a specific, practical application that improves the efficiency of computer-based modeling. For at least these reasons above, Applicant submits that amended independent claim 1 is not directed to an abstract idea, but rather to a specific, practical application that improves the functioning of a computer and the field of data modeling, and is thus, patentable under Step 2A, Pron 2.” The examiner respectfully disagrees. As claimed the applicant is using a mixture model (a probabilistic model for representing the presence of sub-populations within an overall population) combined with a variational inference mean field algorithm (an approximation method) “to reduce a computational runtime.” In order to show that the invention is directed to a practical application the applicant needs to claim an improvement to the functioning of a computer or other technology. To successfully use computational runtime arguments the applicant must show that the invention offers a specific, technical improvement, and that the claims reflect this improvement. This requires more than a generic statement that the invention is faster. As claimed the invention uses a model “to reduce a computational runtime” but this is a general claimed limitation claimed without detailing the process. Specifically, the Federal Circuit has consistently rejected claims that describe achieving a result without detailing the inventive method or process for accomplishing it. Even using generic or conventional machine learning models and training techniques to simply speed up a human task is not sufficient. The alleged runtime improvement does not represent a patent-eligible "inventive concept" but is merely the expected result of applying an abstract idea using conventional computing technology. Applicant’s assertion that the problem is “unique to computer-based data modeling” is not persuasive. The alleged problem arises from the nature of statistical inference, not from a technological limitation in computer architecture. The claims do not identify a deficiency in processor design, memory systems, database structures, or network communication protocols. Instead, they apply known mathematical approximation methods to improve convergence speed and computational efficiency of a statistical model. Such improvements relate to the abstract modeling technique itself.
The applicant has argued “The prior response explained that the claimed "hierarchical map object," "mixture model," and "variational inference mean field algorithm " provide a specific technical solution to a data- sparsity problem in computer-based data analytics, reducing computational runtime and avoiding large-scale simulations. Applicant maintains that position. In view of the present amendments, the claim even more clearly recites the practical database implementation of that solution: the inferred distribution is persisted in sub-population index distribution storage associated with the entity database and is then used to filter entity records within the entity database. This is not merely the use of a computer as a tool, but a particular improvement to computer-based data modeling and database filtering.” The examiner respectfully disagrees. With regards to the map object, characterizing the limitation as a “system-generated data structure” does not, by itself, establish a technological improvement. The claims appear to recite generating a hierarchical mapping of data records and using that mapping to identify sub-populations for modeling. Organizing data into hierarchical categories for purposes of selective analysis constitutes an abstract process of organizing and analyzing information. The claims do not define a specific memory layout or storage architecture that improves how the computer stores or retrieves data at a technical level. The map object is recited functionally as a structure used to group and select data for subsequent mathematical analysis. The courts have consistently held that describing an abstract information organization concept as a data structure does not render it non-abstract where the claimed improvement lies in the information content or analytical outcome rather than in a technological change to computer memory or processing mechanisms. Regarding the combination of the mixture model and the variational inference mean field algorithm, these remain mathematical techniques for approximating probability distributions. Reducing computational runtime by applying an approximation method to a statistical model reflects an improvement to the mathematical modeling process itself. The claims do not recite a specific technological mechanism by which processor cycles are reduced, memory bandwidth is optimized, or computational resources are allocated differently at a systems level. Instead, the runtime reduction is achieved by selecting and applying a known class of approximation algorithms. Even when considered in combination, the hierarchical mapping and probabilistic modeling steps operate at the level of data analysis and mathematical abstraction. The computer is invoked as a tool to execute these calculations. The claims do not recite a specific improvement to the functioning of the computer itself, but rather an improvement in the efficiency of performing an abstract statistical inference task.
The applicant has argued “Therefore, the method of amended claim 1 further improves the computer-based technology of data analytics and data mining by using the "hierarchical map object," "mixture model," and "variational inference meant field algorithm" to compensate for data scarcity and reduce computational resources.” The examiner respectfully disagrees. While the Applicant characterizes the claimed subject matter as an improvement to “computer-based technology,” the claim limitations are directed to mathematical modeling and data analysis techniques applied to data records. The hierarchical map object organizes data into sub-populations, and the mixture model with variational inference performs probabilistic approximation. These operations improve the statistical modeling process itself, not the underlying computer technology. An improvement to the field of data analytics constitutes an improvement to an abstract idea rather than to computer functionality. The claims do not recite a specific technological improvement to memory architecture, processor design, database indexing, parallelization mechanisms, or resource scheduling at the systems level. Instead, the computer performs its ordinary functions of storing data, executing instructions, and processing calculations. Reducing computational resource usage as a consequence of selecting a more efficient mathematical approximation technique does not transform the claim into one that improves the functioning of the computer itself. The Federal Circuit has repeatedly distinguished between improvements to computer technology and improvements to abstract analytical methods implemented on a computer.
Applicants’ arguments related to the substantial reduction in computational runtime is merely a generic statement. Even using generic or conventional machine learning models and training techniques to simply speed up a human task is not sufficient. The alleged runtime improvement does not represent a patent-eligible "inventive concept" but is merely the expected result of applying an abstract idea using conventional computing technology.
The applicant has argued “Here, the claims recite a particular ordered combination of data structures and operations that improves computer-based modeling and database management for sparse entity data. The claims therefore are not directed to an abstract idea under Step 2A. At minimum, the same ordered combination supplies significantly more than any alleged abstract idea under Step 2B because it applies the inferred distribution in a specific database storage and filtering architecture that improves the operation of the computer-implemented data analytics system. In addition, the claims utilize hierarchical map object," "mixture model," and "variational inference meant field algorithm" as system-generated data structures to compensate for data scarcity and reduce computational resources in data analytics. As in Ex Parte Desjardins, the present claims are more than mere "generic computer components with an unpatentable "algorithm," but rather, provide a particular solution of hierarchical segmentation of data records for Bayesian mixture modelling on select sub-populations according to the hierarchical segmentation to compensate for data scarcity.” The examiner respectfully disagrees. While Ex Parte Desjardins addressed claims in which the combination of elements improved computer functionality rather than merely performing an abstract mathematical calculation, the present claims remain directed to statistical modeling and data analysis. The hierarchical map object organizes data into sub-populations, and the mixture model with variational inference is used to approximate inferred performance distributions. These steps are mathematical techniques and analytical methods applied to data, not improvements to the operation of the computer itself. The claimed “system-generated data structures” are functional abstractions for grouping and selecting data records; they do not recite a particular implementation that changes how a computer stores, retrieves, or processes information at a technical level. The computer components are generic, performing their conventional functions of data storage and calculation. Any reduction in computational resources results from the choice of mathematical approximation and data organization, not from an unconventional computer implementation. Thus, unlike the claims in Ex Parte Desjardins, which involved a specific improvement to the way a computer performed a technical task, the current claims improve the abstract process of statistical modeling rather than the technological operation of the computer.
Applicants’ invention appears at most to be an improvement in the abstract idea of receiving, manipulating, and outputting data specifically related to simulating market performance based on modeled distributions. The applicant is receiving performance data history, generating a hierarchical map object, utilizing the hierarchical map to determine an entity type, identify an entity type, identify sub-populations, generate a plurality of normal distributions, model a performance distribution, eliminate an inferred performance distribution, estimate parameters, use a model, project an inferred performance metric, and filter the population of entities. Receiving, manipulating, and filtering this information by way of intended use is not an improvement to the technology, merely an improvement to the information/data. No additional element or combination of elements recited in applicant’s claims that contain any “inventive concept” or add anything “significantly more” to transform the abstract concept into a patent-eligible application.
The applicant has amended claim 1 to overcome the 112(b), second rejections of claims 1, 3-5, 7-11. Therefore, the previous 112(b), second rejections of claims 1, 3-5, 7-11 are withdrawn.
Applicants’ arguments with regards to amending claim 12 to overcome the 112(a), first paragraph new matter rejections of claims 12-14, 16-19 have been fully considered but they are not persuasive. Although the applicant amended claim 12, the applicant did not amend or even argue the claims to overcome all the rejected new matter limitations, therefore the 112(a), first rejection of claims 12-14, 16-19 remains.
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 therefore, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-14, 16-19 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 3-5, 7-11 are directed to a method, claims 12-14, 16-19 are directed to a system. Therefore, claims 1, 3-5, 7-14, 16-19 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1 and 12 recite an abstract idea based on “Mathematical Calculations” A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation. In this instance the applicant is both using a Bayesian model, modeling a performance distribution, aggregating data, using an algorithm, generating a normal distribution. Bayesian modeling is a mathematical tools a particular approach to applying probability to statistical problems. Claim 1 recites abstract limitations including “receiving performance data history, generating a hierarchical map object, utilizing the hierarchical map to determine an entity type, identify an entity type, identify sub-populations, generate a plurality of normal distributions, model a performance distribution, eliminate simulations, estimate parameters, use a model, project an inferred performance metric, and filter the population of entities. Claim 12 recites “receiving performance data history, generating a hierarchical map object, utilizing the hierarchical map to determine an entity type, identify an entity type, identify sub-populations, generate a plurality of normal distributions, model a performance distribution, eliminate simulations, estimate parameters, use a model, project an inferred performance metric, and filter the population of entities.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by at least one processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by at least one processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by at least one processor” language, the claim steps in the context of the claim encompass an abstract idea directed to a mental process and “Mathematical Calculations.”
Dependent claims 3-11, 13-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Independent claims 1, 12, do not integrate the judicial exception into a practical application. Claim 1 is a method comprising “receiving by at least one process form an entity database… models… a performance distribution… by the at least one processor… the inferred performance distribution in a sub-population index distribution storage in an entity database… within the entity database.” Claim 12 is a system comprising: “at least one processor configured to execute software instructions causing the at least one processor to perform steps to: receive from an entity database… models… store the inferred performance distribution in a sub-population index distribution storage in an entity database… entity database.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely using the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, model, generate, and filter data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., Mathematical Calculations) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 3-5, 7-11, 13-14, 16-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application. Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 12, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a method comprising “receiving by at least one process form an entity database… models… a performance distribution… by the at least one processor… the inferred performance distribution in a sub-population index distribution storage in an entity database… within the entity database.” Claim 12 is a system comprising: “at least one processor configured to execute software instructions causing the at least one processor to perform steps to: receive from an entity database… models… store the inferred performance distribution in a sub-population index distribution storage in an entity database… entity database.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely using the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, model, generate, and filter data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., Mathematical Calculations) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 3-5, 7-11, 13-14, 16-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Therefore, based on the above analysis as conducted based on MPEP 2106 from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, does not provide significantly more, and does not provide an inventive concept, therefore the claims are ineligible. Accordingly, claims 1, 3-5, 7-14, 16-19 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12-14, 16-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The applicant had previously made significant amendments to the independent claims for which there is no support in the originally filed disclosure, specifically:
wherein each entity sub-type of the plurality of entity sub-types comprises a first aggregation of the historical performance metrics for each entity associated with each entity sub-type. Although the applicant has support in the originally filed disclosure for a sub-types and for historical performance metrics the applicant does not have support for “each entity sub-type” comprising a historical performance metric for each entity.
wherein the at least one entity type comprises a second aggregation of the first aggregation for each entity sub-type associated with the at least one entity type. Although the applicant has support in the originally filed disclosure for aggregation the applicant does not have support in the originally filed disclosure for a first and second aggregation in view of the entity sub-types.
wherein a variational inference mean field algorithm is used to iteratively estimate parameters of the mixture model to approximate a statistical distribution of the historical performance metrics for the particular sub-population, the variational inference mean field algorithm being configured to iteratively estimate parameters of a probability density function that best fit the historical performance metrics for the particular sub-population Although the applicant has support for the language of a variation inference mean field algorithm the applicant does not have support for the claimed usage and functionality.
The applicant is advised to check all amendments for 112(a), first support.
The written description requirement prevents an applicant from claiming subject matter that was not adequately described in the specification as filed. New or amended claims which introduce elements or limitations that are not supported by the as-filed disclosure violate the written description requirement. See, e.g., In re Lukach, 442 F.2d 967, 169 USPQ 795 (CCPA 1971) (subgenus range was not supported by generic disclosure and specific example within the subgenus range); In re Smith, 458 F.2d 1389, 1395, 173 USPQ 679, 683 (CCPA 1972) (an adequate description of a genus may not support claims to a subgenus or species within the genus). The applicant has added amendments that introduce limitations that are not supported by the original disclosure. Appropriate action is required.
The dependent claims inherit the rejections of the claims from which they depend.
Pertinent prior art considered includes Fine et al. (US 8396777 B1) which discloses a prediction market, a relational database used for predicting future events based on crowd forecasts. Hunt et al. (WO2008092149A2) which discloses analyzing data associated with the sales and marketing efforts of enterprises. Louvau et al. (US 20210406289 A1) which discloses constructing a deferred object model based on a hierarchical object definition, the hierarchical object definition still would conventionally be fully parsed to obtain the object definitions, and then a constructor would construct at least the deferred form of the object into the deferred object model. Cronin et al. (US 20140280065 A1) which discloses predictive query implementation and usage in a multi-tenant database system. Walters et al. (US 20200012902 A1) which discloses generating synthetic time-series data. Although there is prior art that discloses hierarchical schemes, filtering database data, utilizing the filtered data, identifying a sub-population, generating a normal distribution the prior art does not in combination disclose the generating a normal sub-distribution the prior art does not in combination disclose the wherein at least one normal distribution of the plurality of normal distributions is a respective sub-distribution of the performance distribution centered around a respective mean quantity value of a respective sub-population; eliminating simulations by modelling; with a Bayesian model, an inferred performance distribution for the particular sub-population within the population based on the combination of the plurality of normal distributions; wherein the inferred performance distribution for the particular sub- population is modeled using a mixture model; wherein a variational inference mean field algorithm is used to iteratively estimate parameters of the mixture model to approximate a statistical distribution of the historical performance metrics for the particular sub-population, the variational inference mean field algorithm being configured to iteratively estimate parameters of a probability density function that best fit the historical performance metrics for the particular sub-population. And for specifically claim 1, wherein a variational inference mean field algorithm is used to train parameters of the mixture model to approximate a statistical distribution of the historical performance metrics, the variational inference mean field algorithm being configured to reduce runtime in formulating the approximate of the statistical distribution. The rejection would be based on improper hindsight.
Conclusion
Applicants’ 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 JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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, Brian Epstein can be reached at (571) 270 5389. 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.
JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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