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 AIA .
Status of Claims
This communication is a Non-Final office action in response to RCE filed on 12/16/2025. Claims 1, 8, and 15 have been amended. Claims 2, 5, 9, 12, 16 and 19 have been canceled. Claim 21 has been newly added. Therefore, claims 1, 3-4, 6-8, 10-11, 13-15, 17-18 and 20-21 are currently pending and have been addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/16/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6-8, 10-11, 13-15, 17-18 and 20-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1, 3-4, 6-7 and 21 are directed to a non-transitory computer readable medium (i.e. an article of manufacture). Claims 8, 10-11, 13--14 are directed to a system (i.e. a machine). Claims 15, 17-18 and 20 are directed to a process (i.e. a method). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1, 3-4, 6-8, 10-11, 13-15, 17-18 and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claims 8 and 15 recite: identify, by a contextual training service, user context event data for a user account, the user context event data comprising identifications of: a user group, a user role, and at least one of: an activity history, a training history, a location history, and an email history, wherein identifying the user context event data includes receiving, from a client device associated with the user account, event content for a user context event identified, wherein the analyzes the user context event and transmits to the contextual training service a reduced representation of the user context event, the reduced representation comprising at least one keyword, identifier, or ticket number, and wherein the reduced representation excludes transmission of full email text, ticket bodies, or document content associated with the user context event
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under at least the abstract groupings of:
Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As independent claims discuss a contextual training service for a training recommendation that recommends a training program that is mapped to a user, including administrative feedback and feedback of a target user associated with the user, which is one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (claim 1 recites for example, “identify, by a contextual training service, user context event data for a user account”; “analyzes the user context event and transmits to the contextual training service”; “generate, by the contextual training service, a training recommendation that recommends a training program that is mapped to the user context event data”; “generating the training recommendation comprises at least one of: applying an event model comprising rules that define contextual trigger conditions for the training recommendation”; “generate the training recommendation for the training program”; “receive feedback data based on the training program”; “the feedback data including administrative feedback and feedback of a target user associated with the user account”). Concepts performed in the human mind as mental processes because the steps of identifying, analyzing, generating, receiving, rules that define conditions and feedback data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)). Dependent claims 3-4, 6-7, 10-11, 13-14, 17-18 and 20-21 add additional limitations, for example: (claims 3, 10 and 17) wherein the activity history comprises a ticket history associated with a ticket service; (claims 4, 11 and 18) wherein the activity history comprises a workflow history associated with a workflow service; (claims 6, 13 and 20) wherein the client is associated with an administrator or a manager for the user account; (claims 7 and 14) wherein the client is a personal of a user described by the user account, (claim 21) prior to surfacing the training program to the client, surface the training recommendation to an administrative client associated with an administrator or manager for the user account; receive administrative feedback comprising at least one of an approval, a rejection, a modification designating the training program as mandatory, an expansion of the training recommendation to additional user accounts, or a contraction of the training recommendation to fewer user accounts; modify the training recommendation based on the administrative feedback before execution of the training program by the target user; and provide the administrative feedback, including a type of modification applied, as an explicit training, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of certain methods of organizing human activity and mental processes but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) non-transitory computer-readable medium, processor(s), computing device, timestamped, device layer, audiovisual, trained machine learning model (claim 8) a system, computing device, processor(s), a memory, machine-readable instructions. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 3-4, 6-7, 10-11, 13-14, 17-18 and 20-21 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 3-4, 6-8, 10-11, 13-15, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over MERCURY et al. (US 2019/0087781 A1), hereinafter “Mercury”, over Ash et al. (US 2022/0292525 A1), hereinafter “Ash”.
Regarding Claim 1, Mercury teaches a non-transitory computer-readable medium comprising machine-readable instructions, wherein the instructions, when executed by at least one processor, cause at least one computing device to at least: (See at least Mercury, Fig. 2, para 0070, teaches the computing environment including a non-transitory storage medium; para 0090-0091, teaches processor(s)); identify, by a contextual training service, timestamped user context event data for a user account, the timestamped user context event data comprising identifications of: a user group, a user role, and at least one of: an activity history, a training history, a location history, and an email history, ... (Mercury, Abstract, An employee records database is accessed to identify a plurality of employees. Within the plurality of employees, a first set of employees is identified (Examiner notes a first set is a group); para 0163, teaches time stamp data of a user; para 0215, teaches a small to medium size work group; para 0237, a particular role at the company (e.g., performing a particular job, at a particular seniority level, at a particular location/region/office, etc.); para 0196, user's other documents (e.g., emails, web history, etc.); para 0059, professional training and educational contexts, the user data server may analyze each user's training materials viewed, presentations attended, courses completed, interactions, evaluation results, and the like.); generate, by the contextual training service, a training recommendation that recommends a training program that is mapped to the timestamped user context event data, wherein the training program includes one or more audiovisual executable files, and wherein generating the training recommendation comprises at least one of: (Mercury, para 0163, teaches time stamp data of a user; para 0210, teaches using artificial intelligence and/or analytics processes performed by the badge platform server … with output related data such as expected changes in salary, career arc, etc., if the badge earner obtains the badge; para 0238, tools may recommend badges to the particular badge earner … may include course/training costs; See at least Mercury, para 0092-0094 and para 0120, teaches audio/video systems);
applying an event model comprising rules that define contextual trigger conditions for the training recommendation based on the timestamped user context event data, and (Mercury, para 0149, teaches data during a live simulation, or during a virtual reality or augment reality simulation, audio and keystroke data from the user during the testing processing, the user's reaction time and/or decision-making data during a split-second simulated scenario (Examiner notes event)); para 0163, data associated with particular time stamps; para 0244, identify both the unique fields/characteristics of the new occupation/job, as well as the fields/characteristics shared with other occupation or job listings. The badge platform server may determine which of those fields/characteristics are important (Examiner notes rules), and based on other occupations/fields determined to be similar, the platform server may recommend which badges, skills, and/or other traits/qualifications are applicable. Examiner notes recommended based on conditions of importance (e.g. skills or qualifications));
surface, to a client device identified by the contextual training service, the training recommendation or the training program (Examiner note: Examiner is interpreting “surface” as transmit. Mercury, para 0238, tools may recommend badges to the particular badge earner … different badge issuers may charge various amounts for their different badges. Costs may include course/training costs).
receive feedback data based on execution of the one or more audiovisual executable files of the training program in response to surfacing of the training recommendation or the training program, the feedback data including administrative feedback and feedback of a target user associated with the user account; and (Examiner note: Examiner is interpreting “surfacing” as transmitting. Mercury, Figure 42, para 0085, teaches different types of users (e.g., end users, supervisors, administrators, etc.); Mercury, para 0092-0094 and para 0120, teaches audio/video systems; Further, Mercury, para 0149, teaches data may include, for example, audio and video of the user during a live simulation, or during a virtual reality or augment reality simulation, audio and keystroke data from the user during the testing processing, the user's reaction time and/or decision-making data during a split-second simulated scenario; Even further, Mercury, para 0137, feedback data; para 0159 and 0183, user feedback; para 0146-0147, user feedback regarding the testing/credentialing systems; data may include, for example, explicit user feedback);
train the at least one training recommendation machine learning model based on the received feedback data (Mercury, para 0137, feedback data; para 0159 and 0183, user feedback; para 0146-0147, user feedback regarding the testing/credentialing systems; the successful/unsuccessful output rates used in the analytics and/or artificial intelligence may be based on subsequent user feedback data).
Yet, Mercury does not appear to explicitly teach and in the same field of endeavor Ash teaches wherein identifying the timestamped user context event data includes receiving, from a client device associated with the user account, event content for a user context event identified at a device layer of the client device, wherein the client device analyzes the user context event and transmits to the contextual training service a reduced representation of the user context event, the reduced representation comprising at least one keyword, identifier, or ticket number, and wherein the reduced representation excludes transmission of full email text, ticket bodies, or document content associated with the user context event (See at least Ash, Abstract, teaches various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods; Events are taught throughout Ash, see at least para 0414-0415, teaches event records and context data; Identifiers are taught throughout Ash, see at least para 0148, teaching an event record that corresponds to a specific event, which may define an event identifier (e.g., a unique value that identifies the event), an event type (e.g., a merger, a new hire), and event data (e.g., a date on which the event occurred, a place that the event occurred, information sources from which the event was identified, and the like; para 0177, teaches transmit a packet indicating an identifier of the personalized message and a timestamp; para 0386, teaches reducing the computing resources; para 0393, teaches a reduction module including reduced entity-specific vector representative of an entity; tickets are taught throughout Ash, see at least para 0210, teaches a client device, may customize its service solution (Examiner notes device layer), a client may provide one or more customization parameters corresponding to one or more of the service features. Customization parameters can include customized tickets) executing a machine learning model trained to take inputs of the timestamped user context event data, previous timestamped user context event data and previous feedback data and generate the training recommendation for the training program: (Ash, para 0150, training machine learned models to perform specific tasks and reinforcing the trained models based on feedback that is received in connection with the output of a model; Further, Ash, para 0109, teaches a suggestion machine learning system ... the suggestion machine learning system iteratively applies a set of weights to input data, wherein the weights are adjusted based on a parameter of success... the suggestion machine learning system is provided with a training data set that is created based on human creation of a set of suggested topics (Examiner notes feedback); Further, see Ash, para 0396, teaches training set of data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Mercury with wherein identifying the timestamped user context event data includes receiving, from a client device associated with the user account, event content for a user context event identified at a device layer of the client device, wherein the client device analyzes the user context event and transmits to the contextual training service a reduced representation of the user context event, the reduced representation comprising at least one keyword, identifier, or ticket number, and wherein the reduced representation excludes transmission of full email text, ticket bodies, or document content associated with the user context event ...executing a machine learning model trained to take inputs of the timestamped user context event data, previous timestamped user context event data and previous feedback data and generate the training recommendation for the training program as taught by Ash with the motivation for ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods (Ash, Abstract). The Mercury invention now incorporating the Ash invention, has all the limitations of claim 1.
Regarding Claim 3, Mercury, now incorporating Ash, teaches The non-transitory computer-readable medium of claim 1, wherein the activity history comprises a ticket history associated with a ticket service (Mercury, para 0138, monitor and track the activities of the user, including, for example, the workplace tasks performed by the user based on analyses of the various monitoring systems/sensor data installed at the user's workstation and/or workplace environment. … analyses of written and electronic documents associated with the user or workplace. For instance, documents such as maintenance requests, work orders, customer tickets).
Regarding Claim 4, Mercury, now incorporating Ash, teaches The non-transitory computer-readable medium of claim 1, wherein the activity history comprises a workflow history associated with a workflow service (Mercury, para 0138, monitor and track the activities of the user, including, for example, the workplace tasks performed by the user based on analyses of the various monitoring systems/sensor data installed at the user's workstation and/or workplace environment … In some cases, determining what work-related tasks a user has performed, and what other activities they have been engaged in, may be done by analyses of written and electronic documents associated with the user or workplace. For instance, documents such as maintenance requests, work orders, customer tickets, purchase receipts, and the like may be analyzed to determine what skills or tasks the user has completed and when).
Regarding Claim 6, Mercury, now incorporating Ash, teaches The non-transitory computer-readable medium of claim 1, wherein the client device is associated with an administrator or a manager for the user account (Mercury, para 0236, teaches administrator client devices; para 0055, administrator devices).
Regarding Claim 7, Mercury, now incorporating Ash, teaches The non-transitory computer-readable medium of claim 1, wherein the client device is a personal device of a user described by the user account (Mercury, para 0054, user devices may include mobile devices such as smartphones, tablet computers, personal digital assistants, wearable computing devices and personal computers.)
Regarding claims 8 and 15, the claims are an obvious variant to claim 1 above, and are therefore rejected on the same premise. Mercury further teaches a system, comprising: at least one computing device comprising at least one processor; and a memory comprising machine-readable instructions. See at least Mercury, Figures 1-2, para 0052, teaches computing environment including memory; para 0090-0091, teaches processor(s)); para 0088; Fig. 5, teaches a computer system and computing device(s).
Regarding Claims 10 and 17, the claims recite analogous limitations to claim 3 above, and are therefore rejected on the same premise.
Regarding Claims 11 and 18, the claims recite analogous limitations to claim 4 above, and are therefore rejected on the same premise.
Regarding Claims 13 and 20, the claims recite analogous limitations to claim 6 above, and are therefore rejected on the same premise.
Regarding Claim 14, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Claims 21 is are rejected under 35 U.S.C. 103 as being unpatentable over Mercury and Ash, and over Nardotti, JR. et al. (US 2007/0088563 A1), hereinafter “Nardotti”.
Regarding Claim 21, Mercury, now incorporating Ash, teaches The non-transitory computer-readable medium of claim 1, wherein the instructions further cause the at least one computing device to: prior to surfacing the training program to the client device, surface the training recommendation to an administrative client device associated with an administrator or manager for the user account; (Examiner note: Examiner is interpreting “surface” as transmit; Mercury, para 0236, teaches administrator client devices; para 0055, administrator devices; Mercury, para 0078, supervisory access over one or more end users, allowing the supervisor to access all or portions of the end user's content, activities, evaluations, etc. Mercury, para 0146-0147, user feedback regarding the testing/credentialing systems; data may include, for example, explicit user feedback);
modify the training recommendation based on the administrative feedback before execution of the training program by the target user; and (Mercury, para 0083, Based on the retrieved information from data sources, the content customization system may modify content resources for individual users);
provide the administrative feedback, including a type of modification applied, as an explicit training signal to the machine learning model (See at least Ash, para 0150, training machine learned models to perform specific tasks and reinforcing the trained models based on feedback that is received in connection with the output of a model).
Yet, Mercury and Ash do not appear to explicitly teach and in the same field of endeavor Nardotti teaches receive administrative feedback comprising at least one of an approval, a rejection, a modification designating the training program as mandatory, an expansion of the training recommendation to additional user accounts, or a contraction of the training recommendation to fewer user accounts (See at least Nardotti, Abstract, teaches a system and method for managing a continuing education program. A request submission to attend a CLE course may be sent to a supervisor to be approved or disapproved. Nardotti, para 0001, teaches mandatory CLE requirements. Examiner notes supervisor approval for training(s) is obvious to one of ordinary skill in the art).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Mercury and Ash with receive administrative feedback comprising at least one of an approval, a rejection, a modification designating the training program as mandatory, an expansion of the training recommendation to additional user accounts, or a contraction of the training recommendation to fewer user accounts as taught by Nardotti with the motivation for managing a continuing education program (Nardotti, Abstract). The Mercury and Ash invention now incorporating the Nardotti invention, has all the limitations of claim 21.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Delfing (US 2004/0241627 A1) - The present invention relates to the field of interactive electronic methods and systems for providing orientation, training and certification of employees, and for controlling employee access to a jobsite. More particularly, the present invention relates to methods and systems for interactive computer-aided orientation, training and certification that provides instruction using multimedia content and obtains feedback from a plurality of trainees via a computer network.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Response to Arguments
Applicants arguments filed on 12/16/2025 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejection in light of the most recent claim amendments and maintains the 101 rejection. Applicant’s arguments have been fully considered but are found unpersuasive. With respect to the abstract idea, the claimed invention falls within at least the abstract groupings of certain methods of organizing human activity and mental processes as explained in the above 101 analysis. Further, Examiner respectfully notes, Applicant appears to be confusing the additional elements (e.g. client-device layer, machine learning model, etc.) under Step 2A: Prong One: Abstract Ideas. The additional elements are analyzed in Step 2A: Prong Two and Step 2B of the 101 analysis.
With respect to Applicant’s remarks (page 9) “With respect to patent eligibility under 35 U.S.C. §101, even if recommending training programs were viewed at a high level as relating to organizing human activity, amended Claim 1 is not directed to that concept. Rather, the claim as a whole is directed to a specific improvement in how computer systems identify, transmit, store, and process contextual event data across client and server components.”, and Applicant’s remarks (page 10) “These features are not recited as generic computer implementation steps but as specific technical mechanisms that together improve the operation of the computing system itself.” Examiner respectfully does not find these assertions persuasive because Applicant does not explain how or why the limitations of the claims provide an improvement, Applicant only makes the assertion. As such, Examiner finds Applicant's arguments amount to a general allegation that the claims define a patent eligible invention without specifically pointing out how the language of the claims reflect a practical application (e.g., how the claims reflect an improvement).
With respect to integration of the abstract idea into a practical application, the computing elements ((claim 1) non-transitory computer-readable medium, processor(s), computing device, timestamped, device layer, audiovisual, trained machine learning model (claim 8) a system, computing device, processor(s), a memory, machine-readable instructions) are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. These components are described in the specification at a high level of generality. With respect to the machine learning, machine learning is recited at such a high level that it amounts to generally linking the abstract idea to the field of machine learning and merely using machine learning as a tool to apply the abstract idea (See MPEP 2106.04(d)(I)). Examiner fails to see how the generic recitations of these most basic computer components and/or of a system so integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. Thus, Examiner finds that the claims recite the judicial exception of certain methods of organizing human activity and mental processes and is not integrated into a practical application.
With respect to Applicant’s remarks (page 9) “reduced representation, such as keywords, identifiers, or ticket numbers” and Applicant remarks (page 10) “These limitations describe a concrete technological solution that reduces network bandwidth consumption, reduces server- side processing load, improves privacy and security by suppressing full content transmission, and enables efficient event-triggered processing.” Examiner respectfully does not find these remarks persuasive. As an initial matter, “bandwidth” is not claimed nor in Applicant’s specification. Further, reducing data is only mentioned once in Applicant’s specification, para 0069. Further, Examiner notes, using identifiers or keywords to reduce transmitted information (rather than transmitting full content of a document or email) is common and known, along with the benefits of increased efficiency and speed, improved privacy, etc. Therefore, the general purpose device(s) are being used for the very purpose that such devices are known to be used for. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
In summary, the computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. The claimed sequence of steps comprises only “conventional steps, specified at a high level of generality,” which is insufficient to supply an “inventive concept.” Id. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1297, 1300). Also the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence the claims do not recite significantly more than an abstract idea. Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. With respect to the prior art rejections, Applicants arguments have been considered but are moot in light of the most recent claim amendments as the Examiner has updated the rejections with the newly applied Ash and Nardotti references.
With respect to Applicant’s remarks (page 11): “Mercury does not disclose or suggest identifying user context events at a client-device layer, analyzing those events on the client device to generate a reduced representation, excluding transmission of full email text, ticket bodies, or document content, or processing timestamped reduced event data as model inputs.” Examiner notes these are newly added limitations which are taught by the applied Ash reference. See above updated 103 rejection.
Conclusion
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/R.R.N./Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626