DETAILED ACTION
Remarks
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The Amendment filed 8/28/26 has been entered. Claim(s) 2-7, 9-14, 16-19 and 21-24 are pending in the application and are under examination.
Claim Rejections - 35 USC § 101
Claims 2-7, 9-14, 16-19 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
STEP 1 = YES: The claimed invention is to a machine, manufacture, or process, and thus fall under one of the four statutory categories (Step 1: YES).
STEP 2A, Prong 1 = YES: The claim(s) recite(s) a series of steps which can be practically performed by one or more humans through mental process (i.e., observation, evaluation, judgement, and/or opinion)(see MPEP § 2106.04(a)(2), subsection III) and/or certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). Moreover, the claims recite steps akin to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, which the court in Electric Power Group held to recite a mental process. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). This includes a system or method comprising:
obtain…exercise information for a plurality of user workout sessions, wherein the exercise information comprises a duration of a workout, a level of workout, or a number of repetitions performed (mental process, observation/collect information; organizing human activity, interaction between individuals);
identify first exercise information from a first user workout session of the plurality of user workout sessions (mental process, evaluation of observed/collected information);
identify first user information corresponding to the first user workout session (mental process, evaluation of observed/collected information);
determine, based on the first user information or the first exercise information, a first category corresponding to a first set of capabilities (mental process, evaluation of observed/collected information);
compute expected progress data comprising a sequence of categories defining a category progression from the first category corresponding to the first set of capabilities to a second category corresponding to a second set of capabilities (mental process, evaluation of observed/collected information);
determine a customized exercise recommendation from a plurality of exercise recommendations based on the first category and the sequence of categories defines by the expected progress data (mental process, evaluation of observed/collected information);
provide the customized exercise recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching); and
adjust the expected progress data based on tracking, using the exercise information for the plurality of user workout sessions, progress of a plurality of users through a plurality of category progressions, wherein the tracking indicates whether a respective category progression of the plurality of category progressions was achieved by another user (mental process, evaluation of observed/collected information);
track subsequent first user exercise information (mental process, observe and evaluate; organizing human activity, interaction between individuals);
present a subsequent first user exercise recommendation, the subsequent first user exercise recommendation being identified from the plurality of exercise recommendations based on the first user information and a comparison between the subsequent first user exercise information and the expected progress data (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
generate a classification model based on the collected exercise information (mental process, evaluation, e.g., training set of historical data for which category membership is known);
determine a first user exercise category based on the first user information and the classification model (mental process, evaluation, e.g., classify input data based on a training set of historical data for which category membership is known; Spec, par. 0026: classifications, in some cases, may be assigned manually),
wherein identifying the plurality of exercise recommendations from which the customized exercise recommendation is determined is based on the first user exercise category (further defines previously identified exception above);
receive subsequent first user exercise information (mental process, observation/collect information; organizing human activity, interaction between individuals);
determine first user compliance with the customized exercise recommendation based on a comparison of the subsequent first user exercise information and the expected progress data (mental process, evaluation of observed/collected information);
associate the customized exercise recommendation with a score identifying a degree of effectiveness based on the determined first user compliance and the comparison of the subsequent first user exercise information and the expected progress data (mental process, evaluation, e.g., modifying/updating training set of historical data);
obtain first user exercise goal information (mental process, observation/collect information; organizing human activity, interaction between individuals);
determine a subsequent first user exercise recommendation based on the first user exercise goal information, the collected exercise information, the classification model, and the expected progress data (mental process, evaluation based on collected data including training set);
determine a first user movement pattern based on the collected exercise information (mental process, evaluation);
compare the first user movement pattern to a predetermined movement pattern to determine a machine-specific recommendation (mental process, evaluation);
present the machine-specific recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
as recited in claims 2-7;
obtaining…exercise information for a plurality of user workout sessions, wherein the exercise information comprises a duration of a workout, a level of workout, or a number of repetitions performed (mental process, observation/collect information; organizing human activity, interaction between individuals);
identifying first exercise information from a first user workout session of the plurality of user workout sessions (mental process, evaluation of observed/collected information);
identifying first user information corresponding to the first user workout session (mental process, evaluation of observed/collected information);
determining, based on the first user information or the first exercise information, a first category corresponding to a first set of capabilities (mental process, evaluation of observed/collected information);
computing expected progress data comprising a sequence of categories defining a category progression from the first category corresponding to the first set of capabilities to a second category corresponding to a second set of capabilities (mental process, evaluation of observed/collected information);
determining a customized exercise recommendation from a plurality of exercise recommendations based on the first category and the sequence of categories identified by the expected progress data (mental process, evaluation of observed/collected information);
providing, to a user device, the customized exercise recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching); and
adjusting the expected progress data based on tracking, using the exercise information for the plurality of user workout sessions, progress of a plurality of users through a plurality of category progressions, wherein the tracking indicates whether a respective category progression of the plurality of category progressions was achieved by another user (mental process, evaluation of observed/collected information);
tracking subsequent first user exercise information (mental process, observe and evaluate; organizing human activity, interaction between individuals);
presenting a subsequent first user exercise recommendation, the subsequent first user exercise recommendation being identified from the plurality of exercise recommendations based on the first user information and a comparison between the subsequent first user exercise information and the expected progress data (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
generating a classification model based on the collected exercise information (mental process, evaluation, e.g., training set of historical data for which category membership is known);
determining a first user exercise category based on the first user information and the classification model (mental process, evaluation, e.g., classify input data based on a training set of historical data for which category membership is known; Spec, par. 0026: classifications, in some cases, may be assigned manually), wherein identifying the plurality of exercise recommendations from which the customized exercise recommendation is determined is based on the first user exercise category (further defines previously identified exception above);
receiving subsequent first user exercise information (mental process, observation/collect information; organizing human activity, interaction between individuals);
determining first user compliance with the customized exercise recommendation based on a comparison of the subsequent first user exercise information and the expected progress data (mental process, evaluation);
updating the classification model based on the determined first user compliance (mental process, evaluation, e.g., modifying/updating training set of historical data);
obtaining first user exercise goal information (mental process, observation/collect information; organizing human activity, interaction between individuals);
determining a subsequent first user exercise recommendation based on the first user exercise goal information, the collected exercise information, the classification model, and the expected progress data (mental process, evaluation based on collected data including training set);
determining a first user movement pattern based on the collected exercise information (mental process, evaluation);
comparing the first user movement pattern to a predetermined movement pattern to determine a machine-specific recommendation (mental process, evaluation);
presenting the machine-specific recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
as recited in claims 9-14; and
obtaining…exercise information for a plurality of user workout sessions, wherein the exercise information comprises a duration of a workout, a level of workout, or a number of repetitions performed (mental process, observation/collect information; organizing human activity, interaction between individuals);
identifying…first exercise information from a first user workout session of the plurality of user workout sessions (mental process, evaluation of observed/collected information);
identifying… first user information corresponding to the first user workout session (mental process, evaluation of observed/collected information);
determining…based on the first user information or the first exercise information, a first category corresponding to a first set of capabilities (mental process, evaluation of observed/collected information);
computing…expected progress data comprising a sequence of categories defining a category progression from the first category corresponding to the first set of capabilities to a second category corresponding to a second set of capabilities (mental process, evaluation of observed/collected information);
determining…a customized exercise recommendation from a plurality of exercise recommendations based on the first category and the sequence of categories identified by the expected progress data (mental process, evaluation of observed/collected information);
providing…the customized exercise recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching); and
adjusting…the expected progress data based on tracking, using the exercise information for the plurality of user workout sessions, progress of a plurality of users through a plurality of category progressions, wherein the tracking indicates whether a respective category progression of the plurality of category progressions was achieved by another user (mental process, evaluation of observed/collected information);
tracking subsequent first user exercise information (mental process, observe and evaluate; organizing human activity, interaction between individuals);
presenting a subsequent first user exercise recommendation, the subsequent first user exercise recommendation being identified from the plurality of exercise recommendations based on the first user information and a comparison between the subsequent first user exercise information and the expected progress data (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
generating a classification model based on the collected exercise information (mental process, evaluation, e.g., training set of historical data for which category membership is known);
determining a first user exercise category based on the first user information and the classification model (mental process, evaluation, e.g., classify input data based on a training set of historical data for which category membership is known; Spec, par. 0026: classifications, in some cases, may be assigned manually),
wherein identifying the plurality of exercise recommendations from which the customized exercise recommendation is determined is based on the first user exercise category (further defines previously identified exception above);
receiving subsequent first user exercise information (mental process, observation/collect information; organizing human activity, interaction between individuals);
determining first user compliance with the customized exercise recommendation based on a comparison of the subsequent first user exercise information and the expected progress data (mental process, evaluation);
associating the customized exercise recommendation with a score identifying a degree of effectiveness based on the determined first user compliance and the comparison of the subsequent first user exercise information and the expected progress data (mental process, evaluation, e.g., modifying/updating training set of historical data);
determining a first user movement pattern based on the collected exercise information (mental process, evaluation);
comparing the first user movement pattern to a predetermined movement pattern to determine a machine-specific recommendation (mental process, evaluation); and
presenting the machine-specific recommendation (mental process, evaluation or judgement/opinion; organizing human activity, interaction between individuals, teaching);
tracking… effectiveness of the customized exercise recommendation based at least in part on obtaining subsequent exercise information corresponding to a first user (mental process, observe and evaluate; organizing human activity, interaction between individuals); and
modifying…data associated with the customized exercise recommendation, thereby affecting whether the customized exercise recommendation is subsequently provided to a second user (mental process, evaluation of observed/collected information);
wherein the data associated with the customized exercise recommendation is a score that indicates the effectiveness of the customized exercise recommendation (further defines previously identified exception above),
as recited in claims 16-19 and 21-24.
The steps identified above are akin to mental processes, and/or organizing human activity, and thus fall within an enumerated category of abstract ideas. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited steps above, the use of such physical aid does not negate the mental nature of these limitations. Therefore, the claims recite an abstract idea (Step 2A, Prong 1: YES).
STEP 2A, Prong 2 = NO: This judicial exception is not integrated into a practical application.
To the extent the claims recite additional elements related to defining a computer environment to implement the abstract idea above (i.e., A system, comprising: one or more processors; one or more device sensors; and one or more memories storing computer-readable instructions that, upon execution by the one or more processors, cause the system to at least: perform the steps identified as an abstract idea under Prong 1; A non-transitory computer-readable storage medium comprising computer- readable instructions that, upon execution by one or more processors of a service provider processing device, cause the one or more processors to perform operations comprising: the steps identified as an abstract idea under Prong 1; and the method being computer-implemented, including performing various steps under Prong 1 by the computing system and to a user device), including additional elements defining a manner of obtaining the data (i.e., by the one or more sensor devices, wherein the one or more device sensors comprise at least one of an accelerometer, a gyroscope, a heart rate monitor, or an oxygen sensor), these additional elements are recited at a high level of generality, i.e., as generic computer components, such that they do not amount to a particular machine or technical improvement thereof, nor does it represent an improvement in any other technology. Further, the specification neither discloses an assertion of a technological improvement, nor does the specification recite sufficient details such that one of ordinary skill in the art would recognize the claimed invention provides an improvement in the functioning of a computer, or an improvement to other technology or a technical field. Likewise, the claims do not reflect a technical improvement recited in the specification. Rather, the claimed computer and associated components amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). According to the specification, “the machine-learning model 202” may be improved by retraining the model with collected fitness-related information (par. 0046), but neither the specification nor the claims reflect any particular details how that result is achieved. Further, to the extent the recited classification model being generated based on collected exercise information and updated based on user compliance is interpreted as a computer model, which Examiner does not concede, this type of limitation merely confines the use of the abstract idea to a particular technological environment (e.g., machine learning model) without sufficient detail how to achieve the stated result, and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Likewise, the claimed determining step is defined generically as being based on data including the classification model, without defining how this is accomplished, and thus amounts to a recitation of an outcome. See MPEP 2106.05(f). As indicated under Prong 1, the plain meaning of the “determining” step encompasses mental evaluation, i.e., a mental determination based on predefined data or classification scheme, and thus the recited classification model merely confines the use of the abstract idea to a particular technological environment (e.g., machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further, use of the claimed computer, including the claimed sensor devices, to perform the collecting and presenting steps amounts to insignificant pre and post solution activity as mere data gathering and output is recited at a high level of generality with no particular configuration of sensors or particular manipulation of the raw data. See MPEP 2106.05(g). Therefore, the recited additional elements amount to mere instructions to implement the abstract idea in a computer environment, i.e., field of use, and thus do not integrate the judicial exception into a practical application.
It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the physical components identified above does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Therefore, the claims are directed to an abstract idea (Step 2A, Prong 2: YES).
STEP 2B = NO: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as provided under Prong 2, the additional elements are recited at a high level of generality, and for the purpose of insignificant pre and post-solution activity. Moreover, the specification of the instant application further demonstrates that the additional elements are recited for their well-understood, routine and conventional functionality, which refers to elements of the computer system in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)(e.g., see par. 0069 which describes the computer components in the same generic manner as claimed; see par. 0026 which generically defines a “classification model” as a machine-learning technique, which may utilize any suitable supervised machine-learning techniques; par. 0049: the user device 304 (e.g., a touch display, a microphone, a mouse, or any suitable input/output (I/O) device in communication with the user device 304). Likewise, outputs (e.g., customized exercise-related recommendations, feedback information, exercise information, user-profile information, vital sign information, etc.) may be provided to the user via the user device 304 (e.g., on a display, audibly, via haptic feedback (e.g., vibration), etc.); par. 0073: The sensor(s) 446 may be any type of sensing device such as, but not limited to, an accelerometer, a gyroscope, a camera (or other image-capturing device), a sensor configured to collect vital sign information (e.g., a heart-rate monitor, an oxygen monitor, a blood pressure monitor, etc.), or the like). As indicated under Prong 2, these additional elements defining the computer environment and sensors to collect data lack sufficient detail that one of ordinary skill in the art would recognize them as providing a technical improvement, and thus do not offer meaning limitations beyond generally linking the exception to a field of use, and thus amount to merely automating a manual process, which the courts have held to be insufficient in showing an improvement in computer-functionality. See Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); see also LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).
Therefore, the claims are not directed to significantly more than the abstract idea (Step 2B: NO).
Therefore, claims 2-7, 9-14, 16-19 and 21-24 are not directed to patent eligible subject matter.
RESPONSE TO ARGUMENTS
35 USC § 101 – Rejections
Applicant's arguments filed 8/28/26 have been fully considered but they are not persuasive. In response to Applicant’s arguments to the new claim language, Applicant is directed to the revised grounds of rejection, as necessitated by claim amendment. Accordingly, Applicant’s arguments are no persuasive.
Under Step 2A, Prong 1, Applicant argues the claims are patent eligible because the claims do not recite any judicial exceptions under Step 2A, Prong One because the claimed operations are not practically performable in the human mind and do not recite a method of organizing human activity. Applicant is directed to the analysis under Prong 1 which demonstrates the specific abstract ideas recited in the claim.
In response to Applicant’s argument that “claim 16 recites a specific computing-system technique that obtains machine-generated exercise information from one or more device sensors for a plurality of user workout sessions, assigns a first category corresponding to a first set of capabilities, computes category-progression data between capability-specific categories, determines a customized recommendation based on that computed progression, and adaptively adjusts expected progress data based on whether other users actually achieve their respective category progressions”, that “a human cannot practically obtain, process, and track exercise information from one or more device sensors for a plurality of workout sessions; determine corresponding categories and capability sets; compute expected progress data comprising sequences of categories across a plurality of users; track whether plural users achieve respective category progressions; and adjust the expected progress data based on the tracked results” and that “claim thus recites operations involving machine-generated sensor inputs and adaptive population-level processing that cannot be meaningfully characterized as mental steps”, and that “[c]laim 16 requires sensor- derived data for a plurality of workout sessions, capability-based categories, computed category- progressions, cross-user tracking of progression achievement, and adaptive adjustment of expected progress data”, Applicant has identified the abstract idea itself which but for the recitation of physical, generic computer components, is practically performed by mental process/certain methods of organizing human activity. Applicant’s argument that a person could not perform these steps by mental process is conclusory, as the claims do not recite sufficient detail that precludes performance by mental process. To the extent these steps are defined as being performed by a computing device or sensors, these are addressed under Step 2A, Prong 2 and Step 2B, and found to not integrate the judicial exception or amount to significantly more. Therefore, Applicant’s argument is not persuasive.
In response to Applicant’s argument that “[c]laim 16 does not claim a mathematical relationship, formula, equation, or calculation”, the rejection does not rely on this characterization of the claims.
In response to Applicant’s argument that “claim 16 does not recite a method of organizing human activity. The claim is not directed to a commercial practice, legal interaction, economic principle, sales activity, advertising practice, or management of interpersonal conduct”, the Applicant has identified non-limiting examples of what may constitute certain methods of organizing human activity. Further, as indicated under Prong 1, certain limitations are interpreted under the broadest reasonable interpretation as encompassing a step that could be performed through interactions or teaching between individuals, e.g., the presenting steps. Applicant is directed to the analysis under Prong 2 which expressly identifies each step that falls under the certain methods of organizing human activity judicial exception grouping. Therefore, Applicant’s argument is not persuasive.
Under Step 2A, Prong 2, Applicant argues even if, arguendo, the claims recite an abstract idea, the amended claims integrate any alleged abstract idea into a practical application by improving a sensor-informed computing system for individualized exercise recommendation and for adaptive updating of expected progress data.
Applicant contends that “[t]he disclosed technique addresses this deficiency by using exercise information obtained from device sensors for a plurality of workout sessions to determine capability-based categories, compute expected progress data defining a category progression between capability sets, identify customized recommendations based on the category progression, and adjust the expected progress data using tracked category-progression outcomes across a plurality of users”, that “the amended claims do not merely collect exercise information and provide a recommendation”, that “claim 16 recites a particular feedback architecture in which sensor-derived exercise information is used in conjunction with user information to determine a category corresponding to a capability set; expected progress data is computed as a sequence of categories defining a progression to another capability set; a customized recommendation is determined using that category progression; and the expected progress data is subsequently adjusted based on whether other users achieved respective category progressions”. However, the steps identified by Applicant encompass the abstract idea itself, and thus are not an additional element, nor do they represent a technical improvement of any kind. At best, this is an improvement to the mental process itself. Therefore, Applicant’s argument is not persuasive.
Applicant contends this is a technical improvement because “the recommendation system does not rely upon fixed, generic recommendations or static expected progress information”, but “[i]nstead, the system improves future recommendation determinations through adaptive adjustment of the expected progress data using tracked sensor-based exercise outcomes from a plurality of users” and “The adjusted expected progress data can better reflect whether particular category progressions are actually achieved, enabling the system to generate more realistic and more effective category-progressions and customized recommendations”. However, the steps identified by Applicant encompass the abstract idea itself, and thus are not an additional element, nor do they represent a technical improvement of any kind. At best, this is an improvement to the mental process itself. Therefore, Applicant’s argument is not persuasive.
Under Step 2B, Applicant argues even if, arguendo, the claims are abstract, the claims amount to significantly more than the abstract idea and, thus, are patent eligible.
Applicant further argues that “claim 16 recites a particular ordered combination in which a computer system: Obtains exercise information from one or more device sensors for a plurality of user workout sessions; Determines a first category corresponding to a first set of capabilities based on first user information or first exercise information; Computes expected progress data comprising a sequence of categories defining a category progression from the first category and first capability set to a second category and second capability set; Determines a customized exercise recommendation from a plurality of exercise recommendations based on the first category and the category sequence identified by the expected progress data; Provides the customized exercise recommendation to a user device; and Adjusts the expected progress data by tracking, based on the sensor- derived exercise information, whether users achieved respective category progressions” and that “[t]his arrangement is not a generic instruction to collect information, analyze it, and display a result” but rather “require specific sensor-based data inputs, a specific category-and- capability framework, a specific category-progression data structure, customized recommendation determination using that structure, and an adaptive feedback mechanism that adjusts expected progress data based on plural-user progression outcomes”. Applicant argues this “requires the computing system to adjust expected progress data according to tracked information indicating whether category progressions were achieved by other users” which “provides a concrete feedback mechanism for improving the progress-data framework applied in future exercise recommendation determinations”. Applicant argues this claimed arrangement is analogous to the non-conventional arrangement of known components recognized as an inventive concept in BASCOM because “the claims recite a particular arrangement that uses sensor-derived exercise information, capability-based categories, category progressions, multi-user progression tracking, and adaptive expected-progress-data adjustment to improve future operation of the exercise recommendation system” and unlike Electric Power Group because “amended claim 16 recites a concrete sensor-based and adaptive computing technique that uses tracked exercise outcomes across a plurality of users to update the expected progress data used for subsequent recommendation determinations”. However, the steps identified by Applicant encompass the abstract idea itself, and thus are not an additional element, nor do they represent a technical improvement of any kind. At best, this is an improvement to the mental process itself. The claims do not recite any particular configuration, as in BASCOM. Further, as indicated under Prong 1, the claims recite several steps that can be classified as a collecting step, an analysis of the collected data step, or a displaying a result of the analysis step. The recitation of a computer or sensors to accomplish does not offer meaningful limitations beyond generally linking the abstract idea to a generic technological environment, which as evidenced by Applicant’s specification which discloses these additional elements in the same generic manner (see Step 2B), and thus do not represent a technical improvement but rather rely on well-understood, routine, and conventional components performing their known functionality. Therefore, Applicant’s argument is not persuasive.
Applicant argues “claimed solution is necessarily rooted in computer technology because the collection and processing of device-sensor data across a plurality of user workout sessions, the computation of category-progressions between capability sets, and the adaptive updating of expected progress data based on progression outcomes across a plurality of users cannot practically be performed through conventional exercise coaching or manual recordkeeping. The claims therefore do not merely take a preexisting human practice and perform it on a generic computer.” This is not persuasive, as the steps related to collecting and processing the collected data can be practically performed by mental observation and mental evaluation.
Applicant further argues that “the Office has not identified evidence establishing that the ordered combination of the amended claim elements was well-understood, routine, and conventional”, that “[t]he question of whether a claim element or combination of elements is well-understood, routine, and conventional to a skilled artisan in the relevant field is a question of fact”, and that “[t]he mere fact that individual components may have been known does not establish that the claimed arrangement of those components was routine or conventional”, and that “The Office has not yet identified any evidence at step 2B of the Alice analysis of a "conventional" nature of the ordered combination of the amended claims elements.” Applicant is directed the analysis under Step 2B which provides Birkheimer evidence to show the additional elements recited are relied upon for their well-understood, routine, and conventional functionality. Further, Applicant has not identified any non-conventional arrangement of additional elements. The focus of Applicant’s arguments is on the abstract idea itself. Therefore, Applicant’s argument is not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Hull whose telephone number is 571-272-0996. The examiner can normally be reached on Monday-Friday from 8:00am to 5:00pm MST.
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/JAMES B HULL/Primary Examiner, Art Unit 3715
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