DETAILED ACTION
Notice of Pre-AIA or AIA Status
This action is in response to the amendment filed on 11/05/2021. Claims 1-15 are pending in the case. All claims are examined and rejected accordingly.
2. This action is responsive to the Amendment filed 12/29/2025. Claims 1-15 are pending in the case.
Applicant Response
3. In Applicant’s response dated 12/29/2025, Applicant amended Claims 1, 6 and 11 and argued against all objections and rejections previously set forth in the Office Action dated 08/27/2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
According to the first part of the analysis, in the instant case, claim 1 is directed to a method claim, claim 6 is directed to a system claim, and claim 10 is directed toward a non-transitory machine-readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Independent claims 1 recite:
presenting, by a processor, questionnaire to a user corresponding to first set of elements and second set of elements (This step recites collecting information form a user and gathering data about human behavior which falls into mental process grouping of abstract ideas that can be performed in the human mind or with a pen and paper.),
receiving, by the processor, responses for the first set of elements and second set of elements from the user via a user device (This step recites collecting information form a user data and gathering about human behavior which falls into mental process grouping of abstract ideas that can be performed in the human mind or with a pen and paper.),
mapping, by the processor, the responses with predetermined parameters, wherein the responses are mapped by assigning weightages for each of the predetermined parameters (This step recites arranging and mapping information about a user data gathering about human behavior and context falls into mental concepts grouping of abstract ideas.)
calculating, by the processor, a feature value of the user corresponding to the responses and the weightages assigned (This step recites arranging and mapping information about a user data gathering about human behavior and context falls into mathematical concepts grouping of abstract ideas.)
calculating, by the processor, a benchmark score of the user using a trained machine learning model, wherein the trained machine learning model is trained on performance data from elite players in the sport to identify patterns correlating personality aspects and ecosystem factors with elite performance, wherein the benchmark score is calculated as a function of feature value of the user distant from an elite player in the sport (This step recites arranging and mapping information about a user data gathering about human behavior and context falls into mental concepts grouping of abstract ideas.)
correlating, by the processor, the benchmark score against a benchmark threshold to obtain deviation of the benchmark score from the benchmark threshold (This step recites arranging and mapping information about a user data gathering about human behavior and context falls into mathematical concepts grouping of abstract ideas.)
generating, by the processor, a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements(This step recites arranging and mapping information about a user data gathering about human behavior and context falls into mathematical concepts grouping of abstract ideas.)
This can be exemplified by an evaluation of data and the generation of a recommendation based on an evaluation or judgement, which falls within the “Mental Processes” groupings of abstract ideas. The calculating, mapping and correlating steps cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor” or “by the processor”, nothing in the claim elements precludes the steps from practically being performed in the human mind.
The mere nominal recitation of a generic processor does not take the claim limitations out of the mental processes grouping. Thus, the claim recites a mental process. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the above-indicated limitations are merely instructions to implement the abstract idea on a computer/processor and require no more than a generic computer to perform generic computer functions. The claim use generic computing components ( processor, user device , visual representation) to implement evaluation and advice about athlete performance . There is no concreate technological improvement to the sports equipment or improving the ML model to recommend effective athlete performance. Furthermore, the additional element of “presenting, by a processor, questionnaire to a user corresponding to first set of elements and second set of elements” (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of presenting offers as identified by the court (MPEP 2106.05(d)(ll)(iv)))); receiving, by the processor, responses for the first set of elements and second set of elements from the user via a user device (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i)))); wherein the trained machine learning model is trained on performance data from elite players in the sport to identify patterns correlating personality aspects and ecosystem factors with elite performance and providing, by the processor, recommendations to the user based on the deviations in order to improve performance of the wherein the recommendations are intended to address areas of improvement identified in the first set of elements and the second set of elements” amounts to no more than adding insignificant extra-solution activity of mere data gathering. The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer/processor in combination with limitations that are generally linking the use of the judicial exception to a particular technological environment or field of use that are implemented to perform the disclosed abstract idea above.Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
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 integration of the abstract idea into a practical application, the additional element of using a to perform the generation steps described above amounts to no more than mere instructions to apply the exception using generic computer components. The “presenting, receiving and providing” step is further considered well-understood, routine, and conventional. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea.
Thus, these independent claims are not patent eligible.
The dependent claims respectively recite additional limitations of “comparing the benchmark score with the benchmark threshold (claims 2, 7 and 12)”, “the benchmark score is calculated using a supervised or unsupervised learning method (claims 3, 8 and 13)”, “the first set of elements comprise passion, perseverance, ownership, self-control, social ability and learning style of the user. (Claims 4, 9 and 14)”, the second set of elements comprise training, game analysis, fitness, nutrition, hydration, rest, recovery, sleep, parenting practices, role models, and influencers. (Claims 5, 10 and 15)”.
These above-indicated additional limitations also constitute concepts performed in the human mind. The limitation that recites the corelating and comparing benchmark score (claims 2 and 4-5) fall within the “Mental Processes” groupings of abstract ideas and the benchmark calculation limitation (claims 3, 8, and 13) fall within the “Mental Processes” groupings of abstract ideas and steps involving mathematical calculations groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements of “the first set of elements comprise passion, perseverance, ownership, self-control, social ability and learning style of the user”, (claims 4, 9 and 14)”, “the second set of elements comprise training, game analysis, fitness, nutrition, hydration, rest, recovery, sleep, parenting practices, role models, and influencers”, (claims 5, 10 and 15)”) both amount to no more than adding insignificant extra-solution activity related to inputting data. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Additional element of “the benchmark score is calculated using a supervised or unsupervised learning method,” (claims 3, 8 and 13)” amounts to mere instructions to apply an exception because the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished. Thus, it is an equivalent of “apply it”. See MPEP 2106.05(f) (1). Furthermore, the additional element of using a machine learning procedure (in claims 3, 8 and 13) with no specific practical application merely links the use of an exception to a technological environment and still does not impose any meaningful limits on practicing the abstract idea.
The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional “presenting”, “receiving “and “providing” steps are further considered well-understood, routine, and conventional. Mere insignificant extra-solution activity cannot provide an inventive concept. Neither can adding an equivalent of “apply it” nor can the link of the use of the abstract idea to a certain technological environment. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea.
Thus, these dependent claims are not patent eligible.
Examiner Comments
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.
Claim Rejections - 35 USC § 103
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.
Claims 1-2,4-7, 9-11 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over DiPaolo (US Pat. 10974122 B1: Pub. Date: 2021-04-13) in view of SAFAR (US Pub. No. US 20150018988 A1, Pub date 2015-01-15) in further view Brush (US Pub. No. US 20220080263 A1, Pub date 2022-03-17) in view of MALPANI (US Pub. No. US 20220114525 A1, Pub date 2022-04-14)
Regarding independent Claim 1,
DiPaolo teaches a method of assessing data corresponding to performance of a user playing a sport and providing recommendations for improving the performance (see Abstract: describing a system that evaluate player performance and develop player skill), the method comprising steps of:
presenting, by a processor, questionnaire to a user corresponding to first set of elements and second set of elements (see DiPaolo: Fig.7C-7D, Col.14, Line 55-60, “an example of a screen for athletes 82 that presents athletes 82 with a questionnaire 478 comprised of multiple-choice questions 479.” , see Fig.5b, Col.11, Line 5-9, describing input 410 )( questionnaires) organized into development categories 500 such as performance psychology 510 (first set of elements), personal development 520 (second set of elements), and leadership 530.), wherein the first set of elements correspond to personality aspects of the user and the second set of elements correspond to an ecosystem of the user playing the sport (see DiPaolo: Fig.5b, Col.11, Line 5-9, describing input 410 )( questionnaires) organized into development categories 500 such as performance psychology 510 (first set of elements), personal development 520 (second set of elements), and leadership 530.),
receiving, by the processor, responses for the first set of elements and second set of elements from the user via a user device (see DiPaolo: Fig.7k-8y, Col. 13, Line 6-11, “illustrate examples of screens displaying objectives 468, requesting athletes 82 to perform certain exercises 470, and then prompting athletes 82 to submit answers 430 to certain questions 479.”) see also examples of screens of answers to questionnaire Fig. 7k-7o are examples of screens that explain certain objectives, communicate certain exercises to the user, and submits certain questions to the user for which the user can provide answers. FIGS. 7k-7o relate to the skill area of BSC and the development category of performance psychology.”)
DiPaolo does not teach the method comprising steps of:
mapping, by the processor, the responses with predetermined parameters, wherein the responses are mapped by assigning weightages for each of the predetermined parameters based on the effect an element has on the user's ability to understand and improve skills in the sport;
calculating, by the processor, a feature value of the user corresponding to the responses and the weightages assigned;
calculating, by the processor, a benchmark score of the user using a trained machine learning model, wherein the trained machine learning model is trained on performance data from elite players in the sport to identify patterns correlating personality aspects and ecosystem factors with elite performance, wherein the benchmark score is calculated as a function of feature value of the user distant from an elite player in the sport;
correlating, by the processor, the benchmark score against a benchmark threshold to obtain deviation of the benchmark score from the benchmark threshold for each of the first set of elements and the second set of elements;
generating, by the processor, a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements; and
providing, by the processor, recommendations to the user based on the deviations in order to improve performance of the sport, wherein the recommendations are intended to address areas of improvement identified in the first set of elements and the second set of elements.
However, SAFAR teaches the method comprising steps of:
mapping, by the processor, the responses with predetermined parameters (see SAFAR: Fig.1, [0097], “dataset comprises data values pertaining to a plurality of attributes for at least two players mapping to a range of maximum and minimum value”), wherein the responses are mapped by assigning weightages for each of the predetermined parameters based on the effect an element has on the user's ability to understand and improve skills in the sport (see SAFAR: Fig.1, [0057], “The values corresponding to a plurality of attributes for at least one top performing player, and the weightage to be assigned to said plurality of attributes as provided by the expert user”)
calculating, by the processor, a feature value of the user corresponding to the responses and the weightages (see SAFAR: Fig.1, [0052], “Business rule is a mathematical and/or logical expression representing the relation between the combined the effect of a plurality of attributes, the weightage accorded to the plurality of attributes and the PPL value of a player.”)
correlating, by the processor, the benchmark score against a benchmark threshold to obtain deviation of the benchmark score from the benchmark threshold for each of the first set of element and second set of elements (see SAFAR: Fig.5-7, [0097], “values for first dataset may correspond to two datasets, determined by taking into consideration performances of top player in that sport for a predetermined duration in the past, and the worst performances of a player in that sport for a predetermined duration in the past. Thus, the first dataset comprises data values pertaining to a plurality of attributes for at least two players mapping to a range of maximum and minimum values. The maximum and minimum values may correspond to the best and worst performance of one or more players.”)
providing, by the processor, recommendations to the user based on the deviations in order to improve performance of the sport, wherein the recommendations are intended to address areas of improvement identified in the first set of elements and the second set of elements (see SAFAR: Fig.1, [0023], “determine the areas which need to be specially focused on to improve the performance of an individual athlete or a team in the respective sport.”)
Because both DiPaolo and SAFAR are in the same/similar field of player/game performance evaluation, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of DiPaolo to include the system that provide athlete or player improvement recommendation by calculation assessment scores as taught by SAFAR. After modification of DiPaolo, the response to assessment questions can be manipulated to provide a recommendation to the athlete as taught by SAFAR. One would have been motivated to make such a combination in order to provide users efficient and effective training design of training program to help the players to excel in their performance. (see SAFAR [0006])
DiPaolo and SAFAR does not teach the system wherein:
calculating, by the processor, a benchmark score of the user using a trained machine learning model, wherein the trained machine learning model is trained on performance data from elite players in the sport to identify patterns correlating personality aspects and ecosystem factors with elite performance, wherein the benchmark score is calculated as a function of feature value of the user distant from an elite player in the sport;
generating, by the processor, a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements.
However, Brush teaches the method comprising the step of:
calculating, by the processor, a benchmark score of the user using a trained machine learning model (see Brush: Fig.12, [0422], “At step 1220, the one or more computing systems may analyze, by a machine-learning model, the optical sensor data to identify the one or more players and one or more actions during the athletic event. At step 1230, the one or more computing systems may calculate one or more player metrics for one or more players based on the user sensor data and the identified actions.”), wherein the trained machine learning model is trained on performance data from elite players in the sport to identify patterns correlating personality aspects and ecosystem factors with elite performance (see Brush: Fig.12, [0422], “At step 1240, the one or more computing systems may normalize the one or more player metrics for the one or more players based on one or more weighted parameters and one or more other player metrics (elite players) corresponding to the one or more players. At step 1250, the one or more computing systems may provide a report to one or more users about the one or more normalized player metrics for the one or more players.”) , wherein the benchmark score is calculated as a function of feature value of the user distant from an elite player in the sport (see Brush: Fig.12, [0027], “the Sports Operating System may analyze, using a machine-learning model, the optical sensor data to identify one or more players and one or more actions during an athletic event. Using proprietary machine-learning algorithms which are contextual for player positions. The contextual nature of the algorithms is one differentiating factor since each position has been normalized by weighting metrics and statistics by their role and responsibility, yielding insights into the degree of talent and performance of each player. The performance of a player can be based on their actions, captured in video, against actions of other players of the same, age, level, position, etc. The same approaches can be used to benchmark the player against more talented players (elite players) or those who are of similar ability level.”).
Because both DiPaolo, SAFAR, Brush and MALPANI are in the same/similar field of player/game performance evaluation, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the performance evaluation framework teaching of DiPaolo to include the machine learning based metric normalization and comparative analytics technique of Brush. One would have been motivated to make such a combination in order to improve the objectivity, predictive accuracy and cross-athlete comparability of athlete performance metric and also provide athletes with an efficient and effective training program to help the players to excel in their performance.
DiPaolo, SAFAR and Brush does not teach the system wherein:
generating, by the processor, a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements.
However, MALPANI teaches the method comprising the step of:
generating, by the processor, a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements (see MALPANI: Fig.5A-C, “three exemplary benchmark visualizations 502, 504, and 506 are illustrated. In some examples, a user at a company may be presented with one or more of the visualizations 502-506 at one time. The Employee Engagement visualization 502 includes a portion 508 that presents a current performance metric of the company, including a direction in which the performance metric is changing (the up arrow) and/or a graph illustrating the change of the performance metric, as well as text that describes what the performance metric indicates. The visualization 502 further includes portion 510 including a peer group performance metric and a portion 512 that includes a button or other activatable interface component that enables a viewer to request additional context or information about the benchmark being displayed.”)
Because both DiPaolo, SAFAR and MALPANI are in the same/similar field of player/game performance evaluation, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of DiPaolo to include the system that generate a visual representation showing the user's performance relative to the benchmark threshold for each of the first set of elements and the second set of elements as taught by SAFAR. One would have been motivated to make such a combination in order to provide users efficient and effective training design of training program to help the players to excel in their performance.
Regarding Claim 2,
As shown above, DiPaolo, SAFAR, Brush and MALPANI teaches all the limitations of Claim1. SAFAR further teaches the method wherein the step of correlating comprising: comparing the benchmark score with the benchmark threshold (see DiPaolo: Fig.7g, Col.14, “FIG. 7g is an example of a screen that further tabulates the scores 474 for the three development categories 500 into an aggregated score 474.”)
Regarding Claim 4,
As shown above, DiPaolo, SAFAR, Brush and MALPANI teaches all the limitations of Claim1. DiPaolo further teaches the method wherein the first set of elements comprise passion, perseverance, ownership, self-control, social ability and learning style of the user (see DiPaolo: Fig.5b, Col. 12, Line 60-65, “The performance psychology category 510 will include the skill areas 550 of BSC 562, SPF 564, and MDH 566.”) See also Fig.4, [0069], “400 denotes the plurality of attributes comprising of: physical attributes 401, mental attributes 403, social attributes 405, and sport attributes 407. For attributes which are difficult to quantify, such as personality type, drug issues, the user is provided with a questionnaire type of dropdown list comprising of predetermined choices or Boolean operators such as yes or no, wherein the expert user/user can easily select the values corresponding to the top performing player/player respectively. Based on the weightages accorded to different attributes and a mathematical and/or logical expression combining the plurality of attributes in the business rule, a PPL value is computed.”)
See also SAFAR Fig.4, listing various attributes comprised in a dataset needed to compute a PPL value.
Regarding Claim 5,
As shown above, DiPaolo, SAFAR, Brush and MALPANI teaches all the limitations of Claim1. DiPaolo further teaches the method wherein the second set of elements comprise training, game analysis, fitness, nutrition, hydration, rest, recovery, sleep, parenting practices, role models, and influencers (see DiPaolo: Fig.5b, Col. 12, Line 64-67, “he personal development category 520 will include the skill areas 550 of BSA 572, CSR 574, and ECS 576.”), See also Fig.4, [0069], “400 denotes the plurality of attributes comprising of: physical attributes 401, mental attributes 403, social attributes 405, and sport attributes 407. For attributes which are difficult to quantify, such as personality type, drug issues, the user is provided with a questionnaire type of dropdown list comprising of predetermined choices or Boolean operators such as yes or no, wherein the expert user/user can easily select the values corresponding to the top performing player/player respectively. Based on the weightages accorded to different attributes and a mathematical and/or logical expression combining the plurality of attributes in the business rule, a PPL value is computed.”), See also SAFAR Fig.4, listing various attributes comprised in a dataset needed to compute a PPL value.
Regarding independent Claim 6,
Claim 6 is directed to computer program product claim and has similar/same claim limitation as Claim 1 and is rejected under the same rationale.
Regarding Claim 7,
Claim 7 is directed to computer program product claim and has similar/same claim limitation as Claim 2 and is rejected under the same rationale.
Regarding Claim 9,
Claim 9 is directed to computer program product claim and has similar/same claim limitation as Claim 4 and is rejected under the same rationale.
Regarding Claim 10,
Claim 10 is directed to computer program product claim and has similar/same claim limitation as Claim 5 and is rejected under the same rationale.
Regarding independent Claim 11,
Claim 11 is directed to computer program product claim and has similar/same claim limitation as Claim 1 and is rejected under the same rationale.
Regarding Claim 12,
Claim 12 is directed to computer program product claim and has similar/same claim limitation as Claim 2 and is rejected under the same rationale.
Regarding Claim 14,
Claim 14 is directed to computer program product claim and has similar/same claim limitation as Claim 4 and is rejected under the same rationale.
Regarding Claim 15,
Claim 15 is directed to computer system claim and has similar/same claim limitation as Claim 5 and is rejected under the same rationale.
Claim 3, 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over DiPaolo in view of DiPaolo, SAFAR, Brush and MALPANI as applied to claims 1-2,4-7, 9-11 and 14-15 as shown above and in further view of Zhou (US Pub. No. US 20210308587 A1, Pub date 2021-10-07)
Regarding Claim 3,
As shown above, DiPaolo, SAFAR, Brush and MALPANI teaches all the limitations of Claim1. SAFAR further teaches using mathematical and/or logical expression representing the relation to calculate the weightage accorded to the plurality of attributes and the PPL value of a player”
(see SAFAR: Fig.1, [0052], “Business rule is a mathematical and/or logical expression representing the relation between the combined the effect of a plurality of attributes, the weightage accorded to the plurality of attributes and the PPL value of a player.”). Both DiPaolo and SAFAR does not explicitly teach or disclose the method wherein the benchmark score is calculated using a supervised or unsupervised learning method.
However, Zhou teaches the method wherein the benchmark score is calculated using a supervised or unsupervised learning method (see Zhou: Fig.2, [0043], “the training the performance model may be supervised. In other cases, the training may be unsupervised or a combination of supervised and unsupervised.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of DiPaolo to include the system is calculate benchmark score using a supervised or unsupervised learning method as taught by Zhao. One would have been motivated to make such a combination in order to provide players efficient and predictable action recommendations to players to allow the player to improve gameplay performance using machine learning algorithm.
Regarding Claim 8,
Claim 8 is directed to computer program product claim and has similar/same claim limitation as Claim 3 and is rejected under the same rationale.
Regarding Claim 13,
Claim 13 is directed to computer program product claim and has similar/same claim limitation as Claim 3 and is rejected under the same rationale.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101,
Regarding the 35 U.S.C. 101 rejection for being directed non-statutory subject matter has been updated based on applicant amendments . Examiner notes that the claim is still ineligible under 35 U.S.C. 101 because it is directed to evaluating human performance using mathematical modeling and providing advice, implemented on generic computing hardware. Therefore, the 35 U.S.C. 101 rejection has been sustained.
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20160196758 A1
Causevic, Elvir
Title: Human performance optimization and training methods and systems
Description: The present application to the science of human performance generally, and to methods and systems for optimizing human performance using feedback.
US 20010034011 A1
Bouchard, Lisa
Title: System for aiding the selection of personnel
Description: The invention pertains to the field of selecting personnel by predicting the performance of candidates for employment. More particularly, the invention pertains to aiding hiring decisions by using measurements of behavioral and motivational characteristics of employment candidates to predict their performance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached on (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145