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 1/27/2026, Applicant responded on 3/6/2026. Amended claims 1, 11, 12, and 19.
Claims 1-7, 9-22, and 25 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 11, 12, and 19 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
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, “…Proposed amended independent Claim 1 recites additional elements that integrate the alleged judicial exception into a practical application in the field of training neural networks…The human mind is not reasonably capable of training a neural network, applying a neural network (in particular, one or more hidden layers of a neural network), or updating a neural network using revised training data. See, Office Action, p. 11, classifying "neural network" as an additional element. See also, the August 4, 2025 memorandum by Charles Kim, Deputy Commissioner for Patents, titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101." Therefore, even assuming (solely for the sake of argument) that Claim 1 recites a judicial exception, the claim nonetheless recites additional elements that integrate the alleged judicial exception into a practical application in the field of training machine neural networks…” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, the claims do not recite a technical improvement to a technical problem since improve the quality of human career progression pathways is a problem directed to i.e. mental process (i.e. human gathering data regarding human employees, human evaluating human employee data, human modelling human employee career paths), organizing human activities (i.e. human recommending career paths to human employees), mathematical concepts (i.e. human modelling human employee career paths using vectors and clustering model), 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. The limitations and elements are directed to abstract idea with respect to the first prong of Step 2A, the additional elements apply the identified abstract ideas and generally link to a technical environment, i.e. computer, neural network, graphical user interface, and performing extra-solution activities, i.e. data gathering and data output, as analyzed under Step 2A Prong 2, do not provide a practical application or a technical improvement.
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.
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).
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 1-7, 9-22, 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (similarly 12, 19) recites, “…, cause performance of operations comprising:
obtaining training data comprising a plurality of employee profiles, the plurality of employee profiles comprising one or more of an employment history, a set of employee skills, a list of employee credentials, or professional activities performed by employees corresponding to the plurality of employee profiles;
generating a plurality of vector representations corresponding to the plurality of employee profiles;
training a … model, based on the plurality of vector representations corresponding to the plurality of employee profiles, to generate career progression pathways for accomplishing target employment goals, each of the career progression pathways comprising a corresponding set of one or more interim objectives, wherein the … model comprises …;
receiving, for a particular employee, employee information comprising:
a target employment goal for the particular employee;
an employee profile corresponding to the particular employee;
a set of one or more new employment conditions acceptable to the particular employee;
generating a vector representation of the employee profile;
applying the trained … model to the vector representation of the employee profile corresponding to the particular employee and the target employment goal to generate a first …-based career progression pathway to accomplish the target employment goal, the first …-based career progression pathway comprising a first set of one or more interim objectives that the particular employee must meet to reach the target employment goal;
wherein applying the trained … model comprises applying … vector representation of the employee profile, to determine that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee;
responsive to determining that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee:
presenting, in a …,
(a) a recommendation of the first …-based career progression pathway for the particular employee to reach the target employment goal, and
(b) a plurality of … elements associated respectively with a plurality of employee preferences;
receiving, via the …, user input that adjusts a value of a particular … element associated with a particular employee preference in the plurality of employee preferences,
wherein before receiving the user input, the particular … element indicates an initial importance of the particular employee preference, and
wherein after receiving the user input, the particular … element indicates an adjusted importance of the particular employee preference;
based on the user input, revising the training data at least by assigning an adjusted value, corresponding to the adjusted importance of the particular employee preference according to the value of the particular user interface element, to a weight associated with the particular … element; and
updating the … model using the revised training data.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …obtaining training data comprising a plurality of employee profiles, the plurality of employee profiles comprising one or more of an employment history, a set of employee skills, a list of employee credentials, or professional activities performed by employees corresponding to the plurality of employee profiles; generating a plurality of vector representations corresponding to the plurality of employee profiles; training a … model, based on the plurality of vector representations corresponding to the plurality of employee profiles, to generate career progression pathways for accomplishing target employment goals, each of the career progression pathways comprising a corresponding set of one or more interim objectives, wherein the … model comprises …; receiving, for a particular employee, employee information comprising: a target employment goal for the particular employee; an employee profile corresponding to the particular employee; a set of one or more new employment conditions acceptable to the particular employee; generating a vector representation of the employee profile; applying the trained … model to the vector representation of the employee profile corresponding to the particular employee and the target employment goal to generate a first …-based career progression pathway to accomplish the target employment goal, the first …-based career progression pathway comprising a first set of one or more interim objectives that the particular employee must meet to reach the target employment goal; wherein applying the trained … model comprises applying … vector representation of the employee profile, to determine that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee; responsive to determining that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee: presenting, in a …, (a) a recommendation of the first …-based career progression pathway for the particular employee to reach the target employment goal, and (b) a plurality of … elements associated respectively with a plurality of employee preferences; receiving, via the …, user input that adjusts a value of a particular … element associated with a particular employee preference in the plurality of employee preferences, wherein before receiving the user input, the particular … element indicates an initial importance of the particular employee preference, and wherein after receiving the user input, the particular … element indicates an adjusted importance of the particular employee preference; based on the user input, revising the training data at least by assigning an adjusted value, corresponding to the adjusted importance of the particular employee preference according to the value of the particular user interface element, to a weight associated with the particular … element; and updating the … model using the revised training data.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations above; therefore, the claims are directed to a mental process.
Further, the limitations regarding, … obtaining training data comprising a plurality of employee profiles, the plurality of employee profiles comprising one or more of an employment history, a set of employee skills, a list of employee credentials, or professional activities performed by employees corresponding to the plurality of employee profiles; generating a plurality of vector representations corresponding to the plurality of employee profiles; training a … model, based on the plurality of vector representations corresponding to the plurality of employee profiles, to generate career progression pathways for accomplishing target employment goals, each of the career progression pathways comprising a corresponding set of one or more interim objectives, wherein the … model comprises …; receiving, for a particular employee, employee information comprising: a target employment goal for the particular employee; an employee profile corresponding to the particular employee; a set of one or more new employment conditions acceptable to the particular employee; generating a vector representation of the employee profile; applying the trained … model to the vector representation of the employee profile corresponding to the particular employee and the target employment goal to generate a first …-based career progression pathway to accomplish the target employment goal, the first …-based career progression pathway comprising a first set of one or more interim objectives that the particular employee must meet to reach the target employment goal; wherein applying the trained … model comprises applying … vector representation of the employee profile, to determine that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee; responsive to determining that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee: presenting, in a …, (a) a recommendation of the first …-based career progression pathway for the particular employee to reach the target employment goal, and (b) a plurality of … elements associated respectively with a plurality of employee preferences; receiving, via the …, user input that adjusts a value of a particular … element associated with a particular employee preference in the plurality of employee preferences, wherein before receiving the user input, the particular … element indicates an initial importance of the particular employee preference, and wherein after receiving the user input, the particular … element indicates an adjusted importance of the particular employee preference; based on the user input, revising the training data at least by assigning an adjusted value, corresponding to the adjusted importance of the particular employee preference according to the value of the particular user interface element, to a weight associated with the particular … element; and updating the … model using the revised training data.…, under the broadest reasonable interpretation, is managing human employee career paths, therefore, the claims are directed to organizing human activities.
Further, the limitations regarding, …obtaining training data comprising a plurality of employee profiles, the plurality of employee profiles comprising one or more of an employment history, a set of employee skills, a list of employee credentials, or professional activities performed by employees corresponding to the plurality of employee profiles; generating a plurality of vector representations corresponding to the plurality of employee profiles; training a … model, based on the plurality of vector representations corresponding to the plurality of employee profiles, to generate career progression pathways for accomplishing target employment goals, each of the career progression pathways comprising a corresponding set of one or more interim objectives, wherein the … model comprises …; receiving, for a particular employee, employee information comprising: a target employment goal for the particular employee; an employee profile corresponding to the particular employee; a set of one or more new employment conditions acceptable to the particular employee; generating a vector representation of the employee profile; applying the trained … model to the vector representation of the employee profile corresponding to the particular employee and the target employment goal to generate a first …-based career progression pathway to accomplish the target employment goal, the first …-based career progression pathway comprising a first set of one or more interim objectives that the particular employee must meet to reach the target employment goal; wherein applying the trained … model comprises applying … vector representation of the employee profile, to determine that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee; responsive to determining that the first set of one or more interim objectives is compatible with the set of one or more new employment conditions acceptable to the particular employee: presenting, in a …, (a) a recommendation of the first …-based career progression pathway for the particular employee to reach the target employment goal, and (b) a plurality of … elements associated respectively with a plurality of employee preferences; receiving, via the …, user input that adjusts a value of a particular … element associated with a particular employee preference in the plurality of employee preferences, wherein before receiving the user input, the particular … element indicates an initial importance of the particular employee preference, and wherein after receiving the user input, the particular … element indicates an adjusted importance of the particular employee preference; based on the user input, revising the training data at least by assigning an adjusted value, corresponding to the adjusted importance of the particular employee preference according to the value of the particular user interface element, to a weight associated with the particular … element; and updating the … model using the revised training data.…, are directed to mathematical concepts.
Accordingly, the claims are directed to a mental process, organizing human activities, mathematical concepts, 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, 12, 19: One or more non-transitory computer-readable media storing instructions, which when executed by one or more hardware processors, machine learning, ML-based, A system comprising: at least one device including a hardware processor; the system being configured to, neural network, graphical user interface, a plurality of user interface elements, particular user interface element, trained machine learning model comprises applying one or more hidden layers of the neural network
Claim 25: particular user interface element comprises one or more of a slider, dial, or other user-adjustable user interface element
, 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, “obtaining…”, “receiving …”, “recommending…”, ” updating…”, “presenting…”, 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 – “obtaining…”, “receiving …”, “obtaining…”, “updating…”, “presenting…”, data output – “recommending…”, “presenting…”.
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:
[0061] The system may apply the trained ML model to generate one or more ML-based career progression pathways, which includes a set of interim objectives (operation 216). A career progression pathway may be based on (a) the target employment goal and the (b) employee profile. As indicated above, the trained ML model may execute the operation 216 using any number of different ML models, whether clustering, similarity analysis, neural network analysis, among others.
[0094] According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or network processing units (NPUs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, FPGAs, or NPUs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
[0095] For example, Figure 3 is a block diagram that illustrates a computer system 300 upon which an embodiment of the invention may be implemented. Computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information. Hardware processor 304 may be, for example, a general purpose microprocessor.
[0096] Computer system 300 also includes a main memory 306, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304. Main memory 306 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. Such instructions, when stored in non-transitory storage media accessible to processor 304, render computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.
[0097] Computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk or optical disk, is provided and coupled to bus 302 for storing information and instructions.
[0098] Computer system 300 may be coupled via bus 302 to a display 312, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is cursor control 316, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
[0099] Computer system 300 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 300 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions may be read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
[00100] The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 310. Volatile media includes dynamic memory, such as main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, content-addressable memory (CAM), and ternary content- addressable memory (TCAM).
[00101] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. [00102] Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 300 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 302. Bus 302 carries the data to main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by main memory 306 may optionally be stored on storage device 310 either before or after execution by processor 304.
[00103] Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, communication interface 318 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 318 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[00104] Network link 320 typically provides data communication through one or more networks to other data devices. For example, network link 320 may provide a connection through local network 322 to a host computer 324 or to data equipment operated by an Internet Service Provider (ISP) 326. ISP 326 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the "Internet" 328. Local network 322 and Internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 320 and through communication interface 318, which carry the digital data to and from computer system 300, are example forms of transmission media.
[00105] Computer system 300 can send messages and receive data, including program code, through the network(s), network link 320 and communication interface 318. In the Internet example, a server 330 might transmit a requested code for an application program through Internet 328, ISP 326, local network 322 and communication interface 318. [00106] The received code may be executed by processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution.
[00107] In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.
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 1-7, 9-22, 25 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN MAX LEE whose telephone number is (571)272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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