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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 3/9/2026, Applicant responded on 7/9/2026. Amended claims 1, 19, 20.
IDS filed on 3/6/2026, 7/9/2026 are acknowledged and considered by the Examiner.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 19, 20 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 19, 20 are sufficient to overcome the prior art rejections set forth in the previous action. The prior art rejections are hereby withdrawn.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Under Step 2A, Prong One, the claims do not recite a mental process or any other abstract idea. The amended independent claims recite specific computer-implemented operations that cannot practically be performed in the human mind. For example, each of independent claims 1, 19, and 20 recites "accessing a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties, the one or more goal properties including one or more metadata attributes relating to the plurality of goal entities, the plurality of goal entities including one or more employee objectives and one or more organizational strategies, the one or more goal relationships including an alignment between the one or more employee objectives and the one or more organizational strategies, the alignment automatically predicted by an application of a machine-learning model implemented by an artificial intelligence system to the one or more goal properties, the machine-learning model trained on prepared goals data, the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing, wherein network parameters of the machine-learning model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set, wherein the machine-learning model is configured to generate a predicted alignment of a new employee goal with an existing company objective." This claim language does not recite a mental process. It expressly recites "a machine- learning model implemented by an artificial intelligence system," "prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing," and "network parameters of the machine-learning model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set." These limitations are not observations, evaluations, judgments, or opinions that can practically be performed in the human mind...The amended claims also recite "obtaining a set of mapping rules defining mappings between one or more goals based on the one or more goal properties and the one or more goal relationships in the flexible goal ontology, wherein the set of mapping rules comprises hierarchy rules defined using a declarative domain-specific language that maps ontology elements based on property constraints and relationship patterns." The claims further recite "evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user." The claims also recite "incrementally updating the customized goal representation based on a re-evaluation of the set of mapping rules affected by changes to the plurality of goal entities, the one or more goal relationships, and the one or more goal properties, wherein the query plans are incrementally re-evaluated on data changes." These limitations likewise are not mental processes. They require "hierarchy rules defined using a declarative domain-specific language," "executing query plans generated from the hierarchy rules," and "query plans" that "are incrementally re-evaluated on data changes." Nor do the amended claims merely recite a "certain method of organizing human activity." The claims are not directed merely to managing personal behavior, business relations, or human goal-setting activity. Instead, the claims recite a particular computer-implemented architecture for processing goal data and generating a customized graphical representation, including a flexible goal ontology, an artificial-intelligence-implemented machine-learning model trained on prepared goals data, natural language processing of unstructured historical goal text, network-parameter updates based on a loss function, declarative-domain-specific-language hierarchy rules, generated query plans, and incremental re-evaluation of the query plans on data changes. The 2024 Al SME Update explains that "not all methods of organizing human activity are abstract ideas" and that this grouping should not be expanded beyond the enumerated sub-groupings except in rare circumstances. The amended claims therefore do not recite a human organizational practice and merely automate it; they recite specific computer-implemented data-modeling, machine-learning, rule-evaluation, and graphical-rendering operations…The specification explains that "it can be a difficult technical problem to determine how to connect data items related to work and/or goals of employees to data items pertaining to the broader business strategy of an organization, such that an organization can, for example, optimally celebrate and reward employees for their efforts." [0306]. The specification further explains that "the system may be configured to be integrated with external systems, such as Jira and Salesforce, such that goals and OKRs are synchronized across the organization's systems and the organization's progress measurement and reporting stays up to date in real time without the need for manual updating." [0308]. The specification also states that "the system may also implement artificial intelligence to recommend goal alignments using machine learning trained on historical goals data."… The present claims are directly tied to those technological problems and solutions. Each of independent claims 1, 19, and 20 recites "the alignment automatically predicted by an application of a machine-learning model implemented by an artificial intelligence system to the one or more goal properties," "the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing," and "wherein the machine-learning model is configured to generate a predicted alignment of a new employee goal with an existing company objective." These limitations correspond to the specification's description that "A machine learning model may be trained on historical goals data to generate predictions for aligning new employee goals." [1189]. They also correspond to the specification's description that "The raw goals data may include unstructured text descriptions of past employee goals" and "Natural language processing techniques may be used to extract structured information from the goals text." [1190]. The specification further describes that "when an employee submits a new goal, the text description is passed to the trained model" and that "The model generates a predicted alignment for the new goal with existing company objectives."[1199]…The claim language is also tied to the specification's technological solution for dynamically assembling and updating goal representations. Each of independent claims 1, 19, and 20 recites "wherein the set of mapping rules comprises hierarchy rules defined using a declarative domain- specific language that maps ontology elements based on property constraints and relationship patterns," "evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user," and "wherein the query plans are incrementally re-evaluated on data changes." The specification describes that "Hierarchical goal alignments are defined using declarative mapping rules as opposed to rigid manual goal tree constructions." [1240]. The specification further states that this "allows dynamically materializing hierarchical goal views on demand based on the current state of the flexible goal ontology" and that "Goal hierarchies can be efficiently rebuilt in response to changes."[1240]. The specification also states that "[h]ierarchies may be defined using a declarative domain-specific language (DSL) that maps ontology elements based on property constraints and patterns," that "[a] DSL compiler may convert mappings into executable query plans," and that "[p]lans are incrementally re-evaluated on data changes." [1337]-[1338]. The 2024 AI SME Update confirms that, under Step 2A, Prong Two, the analysis considers whether the additional elements integrate any judicial exception into a practical application. The 2024 AI SME Update further states that the analysis considers whether "the claim reflects an improvement in the functioning of a computer, or an improvement to another technology or technical field."…. The independent claims reflect such an improvement. The claims do not merely use a computer as a tool to display goal information. Claim 1 recites "a machine-learning model implemented by an artificial intelligence system," "natural language processing," "network parameters," "a loss function," "hierarchy rules defined using a declarative domain-specific language," "executing query plans generated from the hierarchy rules," "query plans" that "are incrementally re-evaluated on data changes," and "causing graphical rendering of the customized goal representation at a client device associated with the user based on a context of the user." These limitations integrate any alleged abstract concept into a practical application by tying the claimed operations to the technological problem of connecting employee-goal data with organizational- strategy data and dynamically generating and updating customized goal representations. More specifically, the claimed improvement is not merely the business result of better- aligned employee goals. The claimed improvement is a computer-implemented architecture that dynamically assembles and incrementally updates customized goal representations from an extensible ontology using declarative hierarchy rules and generated query plans. The specification explains that rigid manual goal tree constructions are replaced with declarative mapping rules, which allows hierarchical goal views to be dynamically materialized on demand and efficiently rebuilt in response to changes. The claims reflect that improvement by reciting hierarchy rules defined using a declarative domain-specific language, executing query plans generated from those hierarchy rules, and incrementally re-evaluating the query plans on data changes. Thus, the claims recite a particular way to generate and maintain adaptive goal-representation interfaces, rather than merely claiming the desired result of displaying goal information….The Federal Circuit's reasoning in McRO is also instructive. In McRO, the claims were not held ineligible merely because they used rules. Rather, the court focused on whether the claims recited a specific asserted improvement in computer animation and whether the claimed rules limited the process to a particular implementation. Here, the amended claims likewise do not merely claim the result of aligning goals or displaying goal information. They recite a particular implementation using a flexible goal ontology, machine-learning-predicted alignment, NLP processing of unstructured historical goal text, hierarchy rules defined using a declarative domain- specific language, query plans generated from the hierarchy rules, and incremental re-evaluation of those query plans on data changes. As in McRO, the ordered combination of recited operations limits the claims to a particular technological implementation for dynamically generating and updating customized graphical goal representations...The December 2025 Memo, entitled "Advance notice of change to the MPEP in light of USPTO's precedential Appeals Review Panel Decision in Ex Parte Desjardins," likewise confirms the relevance of software-implemented technological improvements. The December 2025 Memo states that Ex Parte Desjardins analyzed eligibility in terms of whether claims were directed to "an improvement in the functioning of a computer, or an improvement to other technology or technical field." The memo further states that Enfish recognized that "' [m]uch of the advancement made in computer technology consists of improvements to software"' and that such improvements may be defined by "'logical structures and processes."' The December 2025 Memo quotes Enfish as recognizing that much advancement in computer technology consists of software improvements that may be defined by logical structures and processes. The claims here recite specific logical structures and processes, including "a flexible goal ontology," "a machine-learning model implemented by an artificial intelligence system," "hierarchy rules defined using a declarative domain-specific language," "query plans generated from the hierarchy rules," and "query plans" that "are incrementally re-evaluated on data changes."…The AI examples issued with the 2024 AI SME Update, Examples 47-49, further support eligibility. Example 47 analyzes AI-related claims involving anomaly detection, Example 48 analyzes AI-based speech-signal processing, and Example 49 analyzes an AI model used to personalize medical treatment. Those examples illustrate that claims applying AI in a particular technological environment may be eligible when they recite a specific application and practical implementation rather than merely claiming an abstract result. The amended claims likewise recite a specific AI implementation: NLP extraction from unstructured historical goal text, model training using true alignment labels and a loss function, prediction of a new employee-goal alignment with an existing company objective, and use of DSL-generated query plans for dynamic graphical rendering. The claims therefore fit the 2024 AI SME Update's distinction between merely applying an abstract idea and reciting a specific technological implementation. Accordingly, even if the Examiner determines that the claims recite a judicial exception, the claims integrate any such exception into a practical application under Step 2A, Prong Two….The Office Action has not established that the ordered combination of limitations in the amended claims is well-understood, routine, and conventional. Claim 1 recites, as an ordered combination, "a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties," "the alignment automatically predicted by an application of a machine- learning model implemented by an artificial intelligence system to the one or more goal properties," "the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing," "network parameters of the machine-learning model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set," "the machine- learning model is configured to generate a predicted alignment of a new employee goal with an existing company objective," "the set of mapping rules comprises hierarchy rules defined using a declarative domain-specific language that maps ontology elements based on property constraints and relationship patterns," "evaluating the set of mapping rules by executing query plans generated from the hierarchy rules," "wherein the query plans are incrementally re-evaluated on data changes," and "causing graphical rendering of the customized goal representation at a client device associated with the user based on a context of the user." The Office Action does not provide factual support showing that this ordered combination was well-understood, routine, and conventional. Instead, the Office Action states that "these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional." That statement does not address the present claim language, including "a machine-learning model implemented by an artificial intelligence system," "unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing," "network parameters," "a loss function comparing predicted goal alignments against true labels," "hierarchy rules defined using a declarative domain-specific language," "query plans generated from the hierarchy rules," and "query plans" that "are incrementally re-evaluated on data changes." The 2024 AI SME Update confirms that factual support is required for a conclusion that additional elements are well-understood, routine, and conventional. The 2024 AI SME Update states that, when an additional element is evaluated for whether it is well-understood, routine, conventional activity in the field, "the rejection should contain factual support for this conclusion." The Office Action does not provide such factual support for the ordered combination recited in amended independent claims 1, 19, and 20. For at least the foregoing reasons, claims 1, 19, and 20 are patent eligible under 35 U.S.C. § 101. Claims 2-18 depend from claim 1 and are patent eligible for at least the same reasons. Applicant respectfully requests withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101….” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, unlike McRO, Enfish, Desjardins, Examples 47-49 and 2024/2025 Memos, by Applicant’s own admission, the claims and the argued elements, indeed recite and directed to, …connect data items related to work and/or goals of employees to data items pertaining to the broader business strategy of an organization, such that an organization can, for example, optimally celebrate and reward employees for their efforts…organization's progress measurement and reporting stays up to date …problem of connecting employee-goal data with organizational- strategy data and dynamically generating and updating customized goal representations…automatic prediction of alignments between organizational strategies and employee objectives…traditional goal management systems require manual processes for creating goal representations, identifying relationships between goals, and updating representations when goals change…prediction of a new employee-goal alignment with an existing company objective…, which is a problem directed to organizing human activity (i.e. human managing human employees and human employees’ goals and human business organization objectives) and a mental process (i.e. human requesting to view human goals, human employees aligning personal goals to organizational goals, humans presenting requested goals for viewing on paper), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination as a whole, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, as an ordered combination as a whole, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, machine learning, DSL, and NLP, performing extra solution activities. Therefore, as an ordered combination as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and wurc).
Applicant admits in Applicant’s own remarks and specification, for example, “…the alignment automatically predicted by an application of a machine-learning model implemented by an artificial intelligence system to the one or more goal properties…”, “…Here are some examples of how machine learning could potentially be applied to enhance the compensation and goals features:…”, that the argued additional elements are indeed “apply it” in Step 2A Prong 2. Accordingly, Applicant admits, the claims and additional elements are “apply it” and do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and wurc).
At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.
The additional elements beyond the abstract ideas, as admitted by Applicant in Applicant’s remarks and Applicant’s specification, are “apply it”, in Step 2A Prong 2 and Step 2B.
However, in addition of being “apply it”, the additional elements beyond the abstract ideas are also well-understood, routine, conventional activities, since the factual support is evinced by Applicant’s own specification, as required by the Berkheimer memo, in at least:
[1193] The machine learning model may comprise a neural network implementing an embedding layer, convolutional layers, pooling layers, and/or dense layers. A convolutional neural network architecture may be selected based on its proven effectiveness in text classification tasks.
[1195] One skilled in the art will appreciate that various standard neural network architectures could be adapted for the goals alignment task.
[1204] A regression neural network may be used to predict appropriate compensation changes. The network has an input layer accepting the engineered features, hidden layers for processing, and/or an output layer predicting the compensation change amount.
[1205] Those skilled in the art will appreciate the wide range of applicable regression models.
[1225] Here are some examples of insights that the machine learning described herein may be configured to reveal regarding compensation and goals:
[1226] Predictive Modeling for Budgeting: A regression model could forecast next year's payroll budget needs by analyzing historical trends in compensation changes, hiring forecasts, attrition predictions, and economic indicators. The predictions can help guide budget planning cycles.
[1273] In example embodiments, one or more user interfaces may be caused to be presented that include advanced visualizations, embedded analytics, mobile optimization, drag-and-drop manipulation, visual cues, animations, recommendations, or simulations, as described herein. For example, one or more user interfaces my include one or more of the following features:
[1274] An interactive sidebar providing contextual actions on goals including creating, aligning, updating, editing, ending, deleting, and reactivating goals;
[1337] Hierarchies may be defined using a declarative domain-specific language (DSL) that maps ontology elements based on property constraints and patterns. For example: o Map Goals where priority > 0.8 under KeyGoals o Map Objectives alignedTo CompanyObjectives under CompanyTreeo Map Salaries where amount > 100000 as HighEarnerBand
[1338] A DSL compiler may convert mappings into executable query plans. Plans are incrementally re-evaluated on data changes.
[1339] The declarative nature enables adapting hierarchies by modifying mappings instead of hand-coding new queries. This provides more maintainable, customizable hierarchies compared to predefined views.
[1376] Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.
[1383] Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.
[1384] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
[1385] In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).
[1386] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures merit consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments.
[1387] FIG. 300 is a block diagram of an example computer system 30000 on which methodologies and operations described herein may be executed, in accordance with an example embodiment.
[1388] In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
[1389] The example computer system 30000 includes a processor 30002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 30004 and a static memory 30006, which communicate with each other via a bus 30008. The computer system 30000 may further include a graphics display unit 30010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 30000 also includes an alphanumeric input device 30012 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 30014 (e.g., a mouse), a storage unit 30016, a signal generation device 30018 (e.g., a speaker) and a network interface device 30020.
[1390] The storage unit 30016 includes a machine-readable medium 30022 on which is stored one or more sets of instructions and data structures (e.g., software) 30024 embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 30024 may also reside, completely or at least partially, within the main memory 30004 and/or within the processor 30002 during execution thereof by the computer system 30000, the main memory 30004 and the processor 30002 also constituting machine-readable media.
[1391] While the machine-readable medium 30022 is shown in an example embodiment to be a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions 30024 or data structures. The term "machine-readable medium" shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions (e.g., instructions 30024) for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
[1393] Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
[1394] Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.
Since Applicant admits in Applicant’s remarks, the additional elements are indeed “apply it”, and Applicant’s specification also discloses the additional elements are “apply it” and “WURC”. The additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and wurc).
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Further, “the transformation is extra-solution activity or a field-of-use (i.e., the extent to which (or how) the transformation imposes meaningful limits on the execution of the claimed method steps). A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application).” MPEP 2106.05(c). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and certain methods of organizing human activities implemented using or applying generic computer components.
Further, contrary to Applicant’s assertions from Applicant’s previous remarks, “These are technological problems that arise specifically in the realm of computer-implemented goal management systems. Before computers, there were no flexible goal ontologies, no mapping rules that evaluate ontological properties and relationships, no machine learning models that predict alignments, and no incremental updating based on reevaluating affected mapping rules. These technological problems and the claimed technological solutions are inherently rooted in computer technology”.
Ontology and business decision mapping existed and solved mental problems and business problems, long before the advent of computers and do not root in computing technologies or solve technical problems, and are manual and mental processes, which are abstract ideas.
“ In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.
Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge. Each uses ontological assumptions to frame explicit theories, research and applications. New ontologies may improve problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.[1]
For instance, the definition and ontology of economics is a primary concern in Marxist economics,[2] but also in other subfields of economics.[3] An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management).
What ontologies in both information science and philosophy have in common is the attempt to represent entities, ideas and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in computer science),[4] and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence).
Applied ontology is considered a spiritual successor to prior work in philosophy, however many current efforts are more concerned with establishing controlled vocabularies of narrow domains than first principles, the existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI.
Ontologies arise out of the branch of philosophy known as metaphysics, which deals with questions like "what exists?" and "what is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Metaphysics has been an ongoing topic of discussion since recorded history.
Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems[citation needed]. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding,[8][9][10] publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings.[11] Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy.[12]
In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber[13] used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization:
An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy.[14]
Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance, Gruber stated (1993):
Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions — that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world.[15] To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms.[16]
As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity."[17] ”
See https://web.archive.org/web/20220918220226/https://en.wikipedia.org/wiki/Ontology_(information_science)
“ Business decision mapping (BDM) is a technique for making decisions, particularly for the kind of decisions that often need to be made in business. It involves using diagrams to help articulate and work through the decision problem, from initial recognition of the need through to communication of the decision and the thinking behind it.
BDM is designed for use in making deliberative decisions—those made based on canvassing and weighing up the arguments. It is also qualitative—although numbers may be involved, the main considerations are qualitatively specified and there is no calculation-based route to the right decision. In these two key elements, BDM is similar to the natural or typical way of making decisions.
However, it differs from typical, informal decision making by providing a structured, semi-formal framework, and using visual language, taking advantage of our ability to grasp and make sense of information faster and more easily when it is graphically presented.
BDM is centered on the creation of a decision map—a single diagram that brings together in one organized structure all the fundamental elements of a decision, and that functions as a focus of collaboration.
BDM aims to support the decision process, making it easier, more reliable and more accountable. It addresses some major problems that can afflict business decision making the way it is generally done, including stress, anxiety, time pressure, lost thinking and inefficiency. By mapping the decision problem, the options, the arguments and all relevant evidence visually using BDM, the decision maker can avoid holding a large amount of information in his or her head, is able to make a more complete and transparent analysis and can generate a record of the thinking behind the final decision.”
See https://web.archive.org/web/20220629025100/https://en.wikipedia.org/wiki/Business_decision_mapping
“ A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts,[1] mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.
Semantic networks are used in natural language processing applications such as semantic parsing[2] and word-sense disambiguation.[3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.[4]
Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.
In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages.[5] Although the importance of this work and the CLRU was only belatedly realized.
Semantic networks were also independently implemented by Robert F. Simmons[6] and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association for Computational Linguistics, Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text."[7] Other researchers, most notably M. Ross Quillian[8] and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian;[9][10] Collins and Loftus[11] Quillian[12][13][14][15]). Still later in 2006, Hermann Helbig fully described MultiNet.[16]
In the late 1980s, two Netherlands universities, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph.[17] In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.[18][19] In 2012, Google gave their knowledge graph the name Knowledge Graph.
The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004.[20] This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998[21] and the Active Document Framework ADF.[22] Since 2003, research has developed toward social semantic networking.[23] This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network).[24] The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network.[25][26] Recently it has been developed to support Cyber-Physical-Social Intelligence.[27] It was used for creating a general summarization method.[28] The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links[29][30] It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications.[31][32] Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence[33]
More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations.[34] ”
See https://web.archive.org/web/20220706011040/https://en.wikipedia.org/wiki/Semantic_network
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process and organizing human activities, generally linked to a technical environment, i.e. computer, machine learning, DSL, and NLP, applying the abstract ideas in Step 2A Prong2 and Step 2B. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception.
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
vii. Selecting one type of content (e.g., FM radio content) from within a range of existing broadcast content types, or selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of well-known, routine, conventional functions performed by the hardware, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016). See 2106.05(a).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). 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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
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);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s amendments are sufficient in overcoming the cited prior art references.
The closest prior art are US10896390B1 to Duggan et al., (hereinafter referred to as “Duggan”) in view of US Patent Publication to US20240028953A1 to Willardson et al., (hereinafter referred to as “Willardson”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 19, 20,
receiving a request from a user to view a goal representation;
accessing a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties, the one or more goal properties including one or more metadata attributes relating to the plurality of goal entities, the plurality of goal entities including one or more employee objectives and one or more organizational strategies, the one or more goal relationships including an alignment between the one or more employee objectives and the one or more organizational strategies, the alignment automatically predicted by an application of a machine-learning model implemented by an artificial intelligence system to the one or more goal properties, the machine-learning model trained on prepared goals data, the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using natural language processing, wherein network parameters of the machine-learning model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set, wherein the machine-learning model is configured to generate a predicted alignment of a new employee goal with an existing company objective;
obtaining a set of mapping rules defining mappings between one or more goals based on the one or more goal properties and the one or more goal relationships in the flexible goal ontology, wherein the set of mapping rules comprises hierarchy rules defined using a declarative domain-specific language that maps ontology elements based on property constraints and relationship patterns;
evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user;
incrementally updating the customized goal representation based on a re-evaluation of the set of mapping rules affected by changes to the plurality of goal entities, the one or more goal relationships, and the one or more goal properties, wherein the query plans are incrementally re-evaluated on data changes; and
causing graphical rendering of the customized goal representation at a client device associated with the user based on a context of the user.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 19, 20.
The prior art rejection is hereby withdrawn.
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 19, 20) recite, … to perform operations, the operations comprising:
receiving a request from a user to view a goal representation;
accessing a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties, the one or more goal properties including one or more metadata attributes relating to the plurality of goal entities, the plurality of goal entities including one or more employee objectives and one or more organizational strategies, the one or more goal relationships including an alignment between the one or more employee objectives and the one or more organizational strategies, the alignment automatically predicted by an … model implemented by… to the one or more goal properties, the … model trained on prepared goals data, the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using …, wherein network parameters of the … model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set, wherein the … model is configured to generate a predicted alignment of a new employee goal with an existing company objective;
obtaining a set of mapping rules defining mappings between one or more goals based on the one or more goal properties and the one or more goal relationships in the flexible goal ontology, wherein the set of mapping rules comprises hierarchy rules defined using a … that maps ontology elements based on property constraints and relationship patterns;
evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user;
incrementally updating the customized goal representation based on a re-evaluation of the set of mapping rules affected by changes to the plurality of goal entities, the one or more goal relationships, and the one or more goal properties, wherein the query plans are incrementally re-evaluated on data changes; and
causing graphical rendering of the customized goal representation a … associated with the user based on a context of the user.
Analyzing under Step 2A, Prong 1:
The limitations regarding, …receiving a request from a user to view a goal representation; accessing a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties, the one or more goal properties including one or more metadata attributes relating to the plurality of goal entities, the plurality of goal entities including one or more employee objectives and one or more organizational strategies, the one or more goal relationships including an alignment between the one or more employee objectives and the one or more organizational strategies, the alignment automatically predicted by an … model implemented by… to the one or more goal properties, the … model trained on prepared goals data, the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using …, wherein network parameters of the … model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set, wherein the … model is configured to generate a predicted alignment of a new employee goal with an existing company objective; obtaining a set of mapping rules defining mappings between one or more goals based on the one or more goal properties and the one or more goal relationships in the flexible goal ontology, wherein the set of mapping rules comprises hierarchy rules defined using a … that maps ontology elements based on property constraints and relationship patterns; evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user; incrementally updating the customized goal representation based on a re-evaluation of the set of mapping rules affected by changes to the plurality of goal entities, the one or more goal relationships, and the one or more goal properties, wherein the query plans are incrementally re-evaluated on data changes; and causing graphical rendering of the customized goal representation a … associated with the user based on a context of the user..., under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations. Therefore, the claims are directed to a mental process.
Further, …receiving a request from a user to view a goal representation; accessing a flexible goal ontology comprising a plurality of goal entities, one or more goal relationships between the plurality of goal entities, and one or more goal properties, the one or more goal properties including one or more metadata attributes relating to the plurality of goal entities, the plurality of goal entities including one or more employee objectives and one or more organizational strategies, the one or more goal relationships including an alignment between the one or more employee objectives and the one or more organizational strategies, the alignment automatically predicted by an … model implemented by… to the one or more goal properties, the … model trained on prepared goals data, the prepared goals data including historical goals data comprising unstructured text descriptions of past employee goals from which structured information is extracted using …, wherein network parameters of the … model are updated to minimize a loss function comparing predicted goal alignments against true labels in a training set, wherein the … model is configured to generate a predicted alignment of a new employee goal with an existing company objective; obtaining a set of mapping rules defining mappings between one or more goals based on the one or more goal properties and the one or more goal relationships in the flexible goal ontology, wherein the set of mapping rules comprises hierarchy rules defined using a … that maps ontology elements based on property constraints and relationship patterns; evaluating the set of mapping rules by executing query plans generated from the hierarchy rules to dynamically assemble a customized goal representation tailored to the user; incrementally updating the customized goal representation based on a re-evaluation of the set of mapping rules affected by changes to the plurality of goal entities, the one or more goal relationships, and the one or more goal properties, wherein the query plans are incrementally re-evaluated on data changes; and causing graphical rendering of the customized goal representation a … associated with the user based on a context of the user.…, under the broadest reasonable interpretation, are human managing human employees and human employees’ goals and human business organization objectives, therefore it is, managing personal behavior or relationships or interactions between people. Thus, the claims are directed to certain methods of organizing human activity.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 19, 20: A system comprising: one or more computer processors; one or more computer memories; a set of instructions incorporated into one or more memories, the set of instructions configuring the one or more computer processors, A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors, a client device associated with the user, application of a machine-learning model, an artificial intelligence system, natural language processing, declarative domain-specific language
Claim 13: graphical user interface, the graphical user interface comprising an interactive sidebar
Claim 14: graphical user interface that is configured for small screens through use of interactive sidebars providing context-specific navigation
Claim 16: external systems
Claim 17: neural network
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “receiving …”, “accessing…”, “obtaining…”, “…extracted…” “incrementally updating…”, “transmitting…”, “…user selection of the selected goal…”, ”…providing context-specific navigation…”, “…propagating updates…”, “…to generate…”, “…incrementally re-evaluated on data changes…”, “…executing query plans…”, “…causing graphical rendering…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “receiving …”, “accessing…”, “obtaining…”, “…extracted…” “…user selection of the selected goal…”, ”…providing context-specific navigation…”, “…incrementally re-evaluated on data changes…”, “…propagating updates…”, data output – “incrementally updating…”, “transmitting…”, “…user selection of the selected goal…”, ”…providing context-specific navigation…”, “…propagating updates…”, “…to generate…”, “…executing query plans…”, “…causing graphical rendering…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[1193] The machine learning model may comprise a neural network implementing an embedding layer, convolutional layers, pooling layers, and/or dense layers. A convolutional neural network architecture may be selected based on its proven effectiveness in text classification tasks.
[1195] One skilled in the art will appreciate that various standard neural network architectures could be adapted for the goals alignment task.
[1204] A regression neural network may be used to predict appropriate compensation changes. The network has an input layer accepting the engineered features, hidden layers for processing, and/or an output layer predicting the compensation change amount.
[1205] Those skilled in the art will appreciate the wide range of applicable regression models.
[1225] Here are some examples of insights that the machine learning described herein may be configured to reveal regarding compensation and goals:
[1226] Predictive Modeling for Budgeting: A regression model could forecast next year's payroll budget needs by analyzing historical trends in compensation changes, hiring forecasts, attrition predictions, and economic indicators. The predictions can help guide budget planning cycles.
[1273] In example embodiments, one or more user interfaces may be caused to be presented that include advanced visualizations, embedded analytics, mobile optimization, drag-and-drop manipulation, visual cues, animations, recommendations, or simulations, as described herein. For example, one or more user interfaces my include one or more of the following features:
[1274] An interactive sidebar providing contextual actions on goals including creating, aligning, updating, editing, ending, deleting, and reactivating goals;
[1337] Hierarchies may be defined using a declarative domain-specific language (DSL) that maps ontology elements based on property constraints and patterns. For example: o Map Goals where priority > 0.8 under KeyGoals o Map Objectives alignedTo CompanyObjectives under CompanyTreeo Map Salaries where amount > 100000 as HighEarnerBand
[1338] A DSL compiler may convert mappings into executable query plans. Plans are incrementally re-evaluated on data changes.
[1339] The declarative nature enables adapting hierarchies by modifying mappings instead of hand-coding new queries. This provides more maintainable, customizable hierarchies compared to predefined views.
[1376] Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.
[1383] Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.
[1384] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
[1385] In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).
[1386] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures merit consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments.
[1387] FIG. 300 is a block diagram of an example computer system 30000 on which methodologies and operations described herein may be executed, in accordance with an example embodiment.
[1388] In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
[1389] The example computer system 30000 includes a processor 30002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 30004 and a static memory 30006, which communicate with each other via a bus 30008. The computer system 30000 may further include a graphics display unit 30010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 30000 also includes an alphanumeric input device 30012 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 30014 (e.g., a mouse), a storage unit 30016, a signal generation device 30018 (e.g., a speaker) and a network interface device 30020.
[1390] The storage unit 30016 includes a machine-readable medium 30022 on which is stored one or more sets of instructions and data structures (e.g., software) 30024 embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 30024 may also reside, completely or at least partially, within the main memory 30004 and/or within the processor 30002 during execution thereof by the computer system 30000, the main memory 30004 and the processor 30002 also constituting machine-readable media.
[1391] While the machine-readable medium 30022 is shown in an example embodiment to be a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions 30024 or data structures. The term "machine-readable medium" shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions (e.g., instructions 30024) for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
[1393] Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
[1394] Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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