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 the Claims
Claims 1-20 are presented for examination. Applicant filed a request for continued examination (RCE) on 06/16/2026 amending claims 1-2, 7-8, 10, 14-15, and 19-20. In light of Applicant's amendments, Examiner has withdrawn the previous § 112(a) and § 101 rejections of claims 1-20 in the instant Office action. Examiner has, however, established new § 101 rejection for claims 1-20 in the instant Office action.
Examiner's Remarks
Patent Eligibility under § 101:
Applicant argues in pages 14-15 of Applicant’s Remarks:
The Office Action states that claims 1-20 were previously deemed patent eligible based on a now-deleted limitation reciting regeneration using a retrained machine-learning model at predetermined intervals, in combination with other limitations. Applicant has amended the claims to restore the substance of that eligibility-significant limitation in language that is supported by the specification. Specifically, instead of reciting that the rules generation engine "is a machine learning model" or is "trained with historical tax question-and-answer pairs," the amended claims now recite that the rules generation engine processes user tax data and external tax data, applies computer-assisted machine-learning algorithms or techniques, and regenerates ordering rules at predetermined intervals by re-analyzing a larger data set. This amended language is consistent with the specification and preserves the same technical feature that the Examiner previously identified as significant to eligibility.
Examiner respectfully disagrees because claims were previously considered patent eligible based on the recited machine learning model that was trained with specific data to produce a specific outcome and then that same machine learning model was continuously re-trained or retrained in certain intervals. This subject matter is now canceled from the independent claims 1, 8, and 14. Therefore, claims 1-20 are no longer patent eligible but are directed to an abstract idea without “significantly more” under 35 USC § 101. Applicant is invited to set up an interview with Examiner to further discuss this issue.
Prior Art Rejection: Closest prior art reference, Mascaro (US 10,176,534 B1) teaches generally assisting a user on tax return preparation. Mascaro, however, fails to disclose – alone in in combination with other references – the following claim limitations found in independent claims 1, 8, and 14, as an ordered combination of steps with other claim limitations:
generating, using a rules generation engine, an optimization algorithm for minimizing tax questions asked to the user based on a generated ordering of the tax questions, wherein the rules generation engine processes user tax data from the user tax data store and external tax data from an external tax data store, wherein the external tax data comprises prior conversations between customers and tax professionals, and wherein generating the optimization algorithm comprises applying one or more computer-assisted machine-learning algorithms to the user tax data and the external tax data, wherein the optimization algorithm comprises a first set of ordering rules;
generating, by the ordering model, an ordering of a plurality of tax questions based on the first set of ordering rules of the optimization algorithm, the ordering including an order in which the plurality of tax questions are presented to the user such that a number of tax questions presented to the user is minimized;
refining the ordering of the plurality of tax questions based on the one or more tax data variables and one or more ordering rules from the first set of ordering rules of the optimization algorithm, comprising: applying the one or more ordering rules from the first set of ordering rules to the plurality of tax questions, wherein the one or more ordering rules applied to the plurality of tax questions are based on the one or more tax data variables, and regenerating the question tree based on the updated one or more tax data variables by eliminating a node and other nodes dependent on the eliminated node from the question tree, wherein questions corresponding to the eliminated node and the other nodes are not subsequently presented to the user;
applying the optimization algorithm to compute one or more value distributions corresponding to one or more unknown tax data variables, wherein each value distribution from the one or more value distributions represents a likelihood of one or more potential outcomes occurring; and
regenerating the optimization algorithm for minimizing the tax questions asked to the user based on the generated ordering of the tax questions, comprising: re-analyzing, using the rules generation engine and at predetermined intervals, a larger data set comprising additional tax returns added to at least one of the user tax data store or the external tax data store after generation of the first set of ordering rules, wherein the re-analyzing comprises applying the one or more computer-assisted machine-learning algorithms or techniques to the larger data set, wherein a second set of ordering rules is obtained from regenerating the optimization algorithm.
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 USC § 101 because they are directed to non-statutory subject matter. The rationale for this finding is explained below.
The Supreme Court in Mayo laid out a framework for determining whether an applicant is seeking to patent a judicial exception itself or a patent-eligible application of the judicial exception. See Alice Corp., 134 S. Ct. at 2355,110 USPQ2d at 1981 (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961). This framework, which is referred to as the Mayo test or the Alice/Mayo test (“the test”), is described in detail in Manual of Patent Examining Procedure (”MPEP”) (see MPEP § 2106(III) for further guidance). The step 1 of the test: It need to be determined whether the claims are directed to a patent eligible (i.e., statutory) subject matter under 35 USC § 101. Step 2A of the test: If the claims are found to be directed to a statutory subject matter, the next step is to determine whether the claims are directed to a judicial exception i.e., law of nature, natural phenomenon, and abstract idea (Prong 1). If the claims are found to be directed to an abstract idea, it needs to be determined whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong 2). Step 2B of the test: If the claims are directed to a judicial exception, the next and final step is to determine whether the claims recite additional elements that amount to significantly more than the judicial exception.
Step 1 of the Test:
When considering subject matter eligibility under 35 USC § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Here, the claimed invention of claims 1-7 is a system, and, thus, one of the statutory categories of invention. Further, the claimed invention of claims 8-13 is a series of steps, which is method (i.e., a process), which is also one of the statutory categories of invention. Still further, the claimed invention of claims 14-20 is one or more non-transitory computer-readable media, which is also one of the statutory categories of invention.
Conclusion of Step 1 Analysis: Therefore, claims 1-20 are statutory under 35 USC § 101 in view of step 1 of the test.
Step 2A of the Test:
Prong 1: Claims 1-20, however, recite an abstract idea of assisting a user on tax return preparation. The creation of assisting a user on tax return preparation, as recited in the independent claims 1, 8, and 14, belongs to certain methods of organizing human activity (i.e., legal interaction – legal obligations) that are found by the courts to be abstract ideas. The limitations in independent claims 1, 8, and 14, which set forth or describe the recited abstract idea, are found in the following steps:
“generating an optimization algorithm for minimizing tax questions asked to the user based on a generated ordering of the tax questions, wherein the rules generation engine processes user tax data from the user tax data store and external tax data from an external tax data store, wherein the external tax data comprises prior conversations between customers and tax professionals, and wherein generating the optimization algorithm comprises applying one or more computer-assisted machine-learning algorithms to the user tax data and the external tax data, wherein the optimization algorithm comprises a first set of ordering rules” (claims 1, 8, and 14);
“maintaining a question tree comprising interconnected nodes corresponding to tax questions, wherein a particular answer from the user dictates a next tax question” (claims 1, 8, and 14);
“generating an ordering of a plurality of tax questions based on the first set of ordering rules of the optimization algorithm, the ordering including an order in which the plurality of tax questions are presented to the user such that a number of tax questions presented to the user is minimized” (claims 1, 8, and 14);
“generating a first output to be presented to the user, wherein the first output is a first question of the plurality of questions based on the ordering” (claims 1, 8, and 14);
“updating the one or more tax data variables based on the one or more inputs from the user” (claims 1, 8, and 14);
“refining the ordering of the plurality of tax questions based on the one or more tax data variables and one or more ordering rules from the first set of ordering rules of the optimization algorithm, comprising: applying the one or more ordering rules from the first set of ordering rules to the plurality of tax questions, wherein the one or more ordering rules applied to the plurality of tax questions are based on the one or more tax data variables, and regenerating the question tree based on the updated one or more tax data variables by eliminating a node and other nodes dependent on the eliminated node from the question tree, wherein questions corresponding to the eliminated node and the other nodes are not subsequently presented to the user” (claims 1, 8, and 14);
“applying the optimization algorithm to compute one or more value distributions corresponding to one or more unknown tax data variables, wherein each value distribution from the one or more value distributions represents a likelihood of one or more potential outcomes occurring” (claims 1, 8, and 14);
“determining whether a likelihood of at least one potential outcome represented by the one or more value distributions satisfies a predetermined likelihood threshold” (claims 1, 8, and 14);
“responsive to the likelihood satisfying the predetermined likelihood threshold, populating at least one unknown tax data variable of the one or more unknown tax data variables” (claims 1, 8, and 14); and
“regenerating the optimization algorithm for minimizing the tax questions asked to the user based on the generated ordering of the tax questions, comprising: re-analyzing, using the rules generation engine and at predetermined intervals, a larger data set comprising additional tax returns added to at least one of the user tax data store or the external tax data store after generation of the first set of ordering rules, wherein the re-analyzing comprises applying the one or more computer-assisted machine-learning algorithms or techniques to the larger data set, wherein a second set of ordering rules is obtained from regenerating the optimization algorithm” (claims 1, 8, and 14).
Prong 2: In addition to abstract steps recited above in Prong 1, independent claims 1 and 14 recite additional elements:
“at least one processor” (claim 1);
“a user tax data store, wherein the user tax data store comprises one or more tax data variables corresponding to the user” (claim 1);
“a rules generation engine” (claim 1, 8, and 14);
“an ordering model” (claim 1, 8, and 14); and
“one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the at least one processor, perform a method of assisting the user on the tax return preparation” (claims 1 and 14).
These additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components. Also, the following limitation recites insignificant extra solution activity (for example, data gathering):
“receiving one or more inputs from the user in response to the first output” (claims 1, 8, and 14).
These additional elements/limitation do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception. The additional elements/limitation of independent claims 1, 8, and 14, here do not render improvements to the functioning of a computer or to any other technology or technical field (see MPEP § 2106.05(a)), nor do they integrate the abstract idea into a practical application under MPEP § 2106.05(b) (particular machine); MPEP § 2106.05(c) (particular transformations); or MPEP § 2106.05(e) (other meaningful limitations).
Conclusion of Step 2A Analysis: The limitations in independent claims 1, 8, and 14, which set forth or describe the recited abstract idea are not patent eligible either alone or in combination. The additional elements/limitation in independent claims 1, 8, and 14, are not patent eligible either alone or in combination. Further, the combination of these additional elements/limitation and the limitations which set forth or describe the recited abstract idea is no more than mere instructions to apply the exception using a generic device. Accordingly, even in combination, these additional elements/limitation and the limitations which set forth or describe the recited abstract idea do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, independent claims 1, 8, and 14, are non-statutory under 35 USC § 101 in view of step 2A of the test.
Step 2B of the Test: The additional elements of independent claims 1, 8, and 14, (see above under Step 2A – Prong 2) are well-understood, routine, and conventional elements that amount to no more than implementing the abstract idea with a computerized system. The Applicant’s Specification describes these additional elements in following terms:
Operational Environment for Embodiments of The Invention
[0001] Turning first to FIG. 1, an exemplary hardware platform for certain embodiments of the invention is depicted. Computer 102 can be a desktop computer, a laptop computer, a server computer, a mobile device such as a smartphone or tablet, or any other form factor of general- or special-purpose computing device. Depicted with computer 102 are several components for illustrative purposes. In some embodiments, certain components may be arranged differently or absent. Additional components may also be present. Included in computer 102 is system bus 104, via which other components of computer 102 can communicate with each other. In certain embodiments, there may be multiple busses or components that may communicate with each other directly. Connected to system bus 104 is central processing unit (CPU) 106. Also attached to system bus 104 are one or more random-access memory (RAM) modules 108. Also attached to system bus 104 is graphics card 110. In some embodiments, graphics card 110 may not be a physically separate card, but rather may be integrated into the motherboard or the CPU 106. In some embodiments, graphics card 110 has a separate graphics-processing unit (GPU) 112, which can be used for graphics processing or for general purpose computing (GPGPU). Also, on graphics card 110 is GPU memory 114. Connected (directly or indirectly) to graphics card 110 is display 116 for user interaction. In some embodiments no display is present, while in others, it is integrated into computer 102. Similarly, peripherals such as keyboard 118 and mouse 120 are connected to system bus 104. Like display 116, these peripherals may be integrated into computer 102 or absent. Also connected to system bus 104 is local storage 122, which may be any form of computer-readable media and may be internally installed in computer 102 or externally and removably attached.
[0002] Computer-readable media include both volatile and nonvolatile media, removable and nonremovable media, and contemplate media readable by a database. For example, computer-readable media include (but are not limited to) RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD), holographic media or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage, and other magnetic storage devices. These technologies can store data temporarily or permanently. However, unless explicitly specified otherwise, the term "computer-readable media" should not be construed to include physical, but transitory, forms of signal transmission such as radio broadcasts, electrical signals through a wire, or light pulses through a fiber-optic cable, but only non-transitory data storage. Examples of stored information include computer-useable instructions, data structures, program modules, and other data representations. In particular, computer- readable media may store computer-executable instructions that, when executed by at least one processor such as CPU 106, perform methods in accordance with embodiments of the present disclosure.
[0003] Finally, network interface card (NIC) 124 is also attached to system bus 104 and allows computer 102 to communicate over a network such as local network 126.NIC 124 can be any form of network interface known in the art, such as Ethernet, ATM, fiber, Bluetooth, or Wi-Fi (i.e., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards). NIC 124 connects computer 102 to local network 126, which may also include one or more other computers, such as computer 128, and network storage, such as data store 130. Generally, a data store such as data store 130 may be any repository from which information can be stored and retrieved as needed. Examples of data stores include relational or object-oriented databases, spreadsheets, file systems, flat files, directory services such as LDAP and Active Directory, or email storage systems. A data store may be accessible via a complex API (such as, for example, Structured Query Language), a simple API providing only read, write, and seek operations, or any level of complexity in between. Some data stores may additionally provide management functions for data sets stored therein such as backup or versioning. Data stores can be local to a single computer such as computer 128, accessible on a local network such as local network 126, or remotely accessible over public Internet 132.Local network 126 is, in turn, connected to public Internet 132, which connects many networks such as local network 126, remote network 134 or directly attached computers such as computer 136. In some embodiments, computer 102 can itself be directly connected to public Internet 132.
[0033] In some embodiments, user tax data store 210 and external tax data store 212 then provide user tax data and external tax data to rules generation engine 214. Rules generation engine 214 processes user tax data from user tax data store 210 as well as information from external tax data store 212 to generate ordering rules for later use by ordering model 208. A person of skill in the art will appreciate that such a calculation, particularly on a large data set, is only possible with the aid of computer-assisted machine-learning algorithms and techniques such as multivariate analysis and/or cluster analysis. In some embodiments, big-data techniques, including generalized linear modeling and k-means clustering can be used to generate rules. In other embodiments, tree-based algorithms such as gradient-boosting machines can be used.
This is a description of general-purpose computer. Further, the additional limitation of “receiving” data amount to no more than mere instruction to apply the exception using generic computer component. For the same reason this element is not sufficient to provide an inventive concept. The additional limitation of “receiving” data was considered insignificant extra-solution activity in Step 2A – Prong 2. Re-evaluating here in Step 2B, it is also determined to be well-understood, routine, and conventional activity in the field. Similarly to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), the additional limitation of independent claims 1, 8, and 14, “receives” data over a network in a merely generic manner. The courts have recognized receiving data from a user device function as well-understood, routine and conventional when claimed in a merely generic manner. Therefore, the additional elements/limitation of independent claims 1, 8, and 14, are well-understood, routine, and conventional. Further, taken as combination, the additional elements/limitation add nothing more than what is present when the elements are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
Conclusion of Step 2B Analysis: Therefore, independent claims 1, 8, and 14, are non-statutory under 35 USC § 101 in view of step 2B of the test.
Dependent Claims: Dependent claims 2-7 depend on independent claim 1; dependent claims 9-13 depend on independent claim 8; and dependent claims 15-20 depend on independent claim 14. The elements in dependent claims 2-7, 9-13, and 15-20, which set forth or describe the abstract idea, are:
“transmitting, to a third party, a completed tax return corresponding to the user” (claim 2: insignificant extra solution activity);
“providing the first output as an audio signal to the user; and receiving the one or more inputs as audio from the user” (claim 3: both “providing” step and “receiving” step are insignificant extra solution activity);
“generating the first output using natural language generation; and providing the first output as an audio signal” (claim 4: “generating” step is further narrowing the recited abstract idea; and “providing” step is insignificant extra solution activity);
“receiving the one or more inputs as an audio signal; and translating the one or more inputs using natural language processing” (claim 5: “receiving” step is insignificant extra solution activity; and “translating” step is further narrowing the recited abstract idea);
“refining the ordering of the plurality of questions comprises: receiving the one or more inputs; and applying the one or more ordering rules; and applying the one or more ordering rules determines the ordering of the plurality questions” (claim 6: wherein “receiving” step is insignificant extra solution activity; and “applying" step is further narrowing the recited abstract idea);
“generating the optimization algorithm comprising the first set of ordering rules is based on at least a portion of the one or more tax data variables in the user tax data store and external tax data in an external tax data store, wherein the one or more computer-assisted machine-learning algorithms or techniques comprise at least one of multivariate analysis, cluster analysis, generalized linear modeling, k-means clustering, a tree-based algorithm, or a gradient-boosting machine” (claim 7: further narrowing the recited abstract idea);
“refining the ordering of the plurality of questions comprises: receiving the one or more tax data variables; and storing the one or more tax data variables in a user tax data store, wherein the one or more ordering rules applied to the plurality of tax questions are based on at least the one or more tax data variables and the one or more inputs from the user” (claim 9: insignificant extra solution activity);
“generating the first set of ordering rules is based on at least a first portion of the one or more tax data variables in the user tax data store and at least a second portion of external tax data in an external tax data store, wherein the one or more computer-assisted machine-learning algorithms or techniques comprise at least one of multivariate analysis, cluster analysis, generalized linear modeling, k-means clustering, a tree-based algorithm, or a gradient-boosting machine” (claim 10: further narrowing the recited abstract idea);
“transmitting, to a third party, a finalized tax return corresponding to the user” (claim 11: insignificant extra solution activity);
“recomputing one or more values associated with the one or more tax data variables utilizing the one or more inputs from the user” (claim 12: further narrowing the recited abstract idea);
“refining the ordering of the plurality of questions occurs responsive to a plurality of user inputs being received, wherein the plurality of user inputs corresponds to one or more fields associated with a tax form” (claim 13: further narrowing the recited abstract idea);
“the one or more computer-assisted machine-learning algorithms or techniques comprise at least one of multivariate analysis, cluster analysis, generalized linear modeling, k-means clustering, a tree-based algorithm, or a gradient-boosting machine” (claim 15: further narrowing the recited abstract idea);
“generating the first output using natural language generation; and providing the first output as an audio signal” (claim 16: “generating” step is further narrowing the recited abstract idea; and “providing” step is insignificant extra solution activity);
“receiving the one or more inputs from the user as an audio signal; and translating the one or more inputs using natural language processing” (claim 17: “receiving” step is insignificant extra solution activity; and “translating” step is further narrowing the recited abstract idea);
“applying the optimization algorithm to compute one or more uncertainty levels corresponding to one or more unknown tax data variables” (claim 18: further narrowing the recited abstract idea);
“responsive to a value distribution from the one or more value distributions exceeding the predetermined threshold the likelihood of the at least one potential outcome satisfying the predetermined likelihood threshold, populating a tax return field associated with the value distribution the at least one unknown tax data variable” (claim 19: further narrowing the recited abstract idea); and
“regenerating the optimization algorithm occurs determining that the tax return corresponding to the user is complete” (claim 20: further narrowing the recited abstract idea).
Conclusion of Dependent Claims Analysis: Dependent claims 2-7, 9-13, and 15-20, do not correct the deficiencies of independent claims 1, 8, and 14, and they are, thus, rejected on the same basis.
Conclusion of the 35 USC § 101 Analysis: Therefore, claims 1-20 are rejected as directed to an abstract idea without “significantly more” under 35 USC § 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Podgorny (US 11,269,665 B1) discloses: “A method and system provides personalized assistance to users of a data management system. The method and system trains an analysis model with both a supervised machine learning process and an unsupervised machine learning process to identify relevant assistance topics based on a user query and the attributes of the user that provided the query. The method and system outputs personalized assistance to the user based on the analysis of the analysis model.”
Furbish (US 2024/0257267 A1) discloses: “Systems and methods or determining tax recommendations for a taxpayer by using a tax calculation graph to identify tax variables that a taxpayer can control and modify, including a recommendation engine configured to analyze a tax calculation graph which is calculated using tax data of the taxpayer. An identified tax variable can be analyzed by determining nodes of the graph affecting a value of the identified tax variable, providing a user interface enabling at least one modification to the nodes, and determining an effect on the identified tax variable due to the at least one modification.”
Angele (WO 2005055134 A2) discloses: “The inference machine has at least one information generating unit for storing or generating data forming a data set, at least one arithmetic unit for generating an object model consisting of a class structure and a declarative rule system, an input/output unit for entering a query and outputting responses, an inference unit in which the rules are evaluated to generate a response to a query and an associated evaluation unit into which inference protocols relating to rule instantiations can be read and statements generated about the evaluation of rules depending on the inference protocols.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIRPI H. KANERVO whose telephone number is 571-272-9818. The examiner can normally be reached on Monday – Friday, 10 am – 6 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Abhishek Vyas can be reached on 571-270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VIRPI H KANERVO/Primary Examiner, Art Unit 3691
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