DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 (i.e., changing from AIA to pre-AIA ) 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.
This action is in response to the arguments filed on 02/17/2026. Claims 1-3, 6-13 and 16-20 are pending in the application and have been considered below.
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, 6-13 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
For Step 1, the claim is an apparatus so it does recite a statutory category of invention.
For Step 2A, Prong 1:
The claim recites the limitation of “pre-process the user profile, wherein preprocessing the user profile comprises at least a feature extraction process configured to reduce a dimensionality of the visual representation of user data.” The pre-process limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the pre-process step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “determine the aptitude measurement as a function of a trained aptitude machine-learning model, wherein the aptitude measurement comprises a comparison of an education level of the user to an expected education level.” The determine limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “determine, using a strategic machine-learning model
comprising [an inferencing system], a data structure as a function of the aptitude measurement determined [using the trained aptitude machine-learning model], wherein the data structure comprises g first parameter change[[s]], wherein the first parameter change comprises a feedback function wherein the feedback function configures alterations to the first parameter change as a function of user's desired aptitude measurement.” The determine limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set. The generating limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the sanitizing step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “determining a degree of membership of the aptitude measurement in output linguistic variables.” The determining limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time.” The selecting limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “defuzzifying an output [of the inferencing system} to generate the data structure comprising first parameter changes.” The defuzzifying limitation, as drafted, is a mathematical concept that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept. (MPEP 2106.04(a)(2)(III)(C)).
For Step 2A, Prong 2, the claim recites additional elements: processor, memory, “receive a user profile from a user, wherein the user profile comprises activity
metrics and an endpoint element, wherein the user profile further comprises at least an image component, wherein the at least an image component comprises a visual representation of user data, and wherein the endpoint element comprises a duration of time which the user desires to achieve a goal,” “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a pre-processed user profile input correlated to an aptitude measurement, ” inferencing system, “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures” and display the determined data structure and the first parameter change using a display device.
The processor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. MPEP 2106.05(f).
The “memory,” “dedicated hardware unit,” circuitry configured to perform signal processing operations, and” inferencing system” are generic computer components that amount to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The “receive a user profile from a user” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The recited “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a pre-processed user profile input correlated to an aptitude measurement “is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05(f).
The “display the data structure using a display device “step is an intended use and linked to the judicial exception.
Step 2B
The additional elements “processor, memory, inferencing system, “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a user profile input correlated to an aptitude measurement “and ,”inferencing system, “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures” do not amount to significantly more for the reasons set forth in step 2A above.
Additionally, under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receive a user profile from a user.” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The claim does 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 elements of “processor, memory, inferencing system and “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a user profile input correlated to an aptitude measurement” and ”inferencing system, “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2:
Claim 2, which incorporates the rejection of claim 1, recites further limitations such as “
identify an updated aptitude measurement as a function of the updated user profile; and
determine an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes” that are part of the abstract idea.
The claim recites an additional element:” receive an updated user profile as a function of the parameter changes.”
The” receive an updated user profile as a function of the parameter changes.”
step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 3:
Claim 3, which incorporates the rejection of claim 1, recites further limitations such as “
the aptitude measurement comprises a plurality of aptitude measurements; and
each of the aptitude measurements of the plurality of aptitude measurements is categorized into positive aptitude measurements and negative aptitude measurements” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 6:
Claim 6, which incorporates the rejection of claim 1, recites further limitations such as “
the aptitude measurement is reflected as a numerical score” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 7:
Claim 7, which incorporates the rejection of claim 1, recites further limitations such as “
the endpoint element comprises a goal of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 8:
Claim 8, which incorporates the rejection of claim 1, recites further limitations such as “
the aptitude measurement comprises a productivity score of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 9:
Claim 9, which incorporates the rejection of claim 1, recites further limitations such as “
… positively increase the score of the aptitude measurement” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 10:
Claim 10, which incorporates the rejection of claim 1, recites further limitations such as “
the activity metric comprises a task of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 11:
For Step 1, the claim is a method so it does recite a statutory category of invention.
For Step 2A, Prong 1:
The claim recites the limitation of “pre-process the user profile, wherein preprocessing the user profile comprises at least a feature extraction process configured to reduce a dimensionality of the visual representation of user data.” The pre-process limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the pre-process step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation “determining …the aptitude measurement as a function of a trained aptitude machine-learning model, wherein the aptitude measurement comprises a comparison of an education level of the user to an expected education level.” The determining limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identifying step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “determining, using a strategic machine-learning model comprising [an inferencing system], a data structure as a function of the aptitude measurement determined [using the trained aptitude machine-learning model], wherein the data structure comprises a first parameter change[[s]], wherein the first parameter change comprises a feedback function wherein the feedback function configures alterations to the first parameter change as a function of user's desired aptitude measurement.” The determining limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determining step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set. The generating limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the sanitizing step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “determining a degree of membership of the aptitude measurement in output linguistic variables.” The determining limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time.” The selecting limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the determine step from practically being performed in the human mind. This limitation is a mental process. (MPEP 2106.04(a)(2)(III)(C)).
The claim recites the limitation of “defuzzifying an output [of the inferencing system} to generate the data structure comprising first parameter changes.” The defuzzifying limitation, as drafted, is a mathematical concept that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept. (MPEP 2106.04(a)(2)(III)(C)).
For Step 2A, Prong 2, the claim recites additional elements: processor, inferencing system,” receive a user profile from a user, wherein the user profile comprises activity metrics and an endpoint element,” and “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures.”
The recited “train, by the [processor] an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a pre-processed user profile input correlated to an aptitude measurement” is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05(f).
The processor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. (MPEP 2106.05(f)).
The “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures “is a generic computer component that amount to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The “receiving…a user profile from a user…” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The recited “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a user profile input correlated to an aptitude measurement “is a generic training recitation that may amount to a generic computer component to apply an abstract idea under MPEP 2106.05(f).
Step 2B
The additional elements processor, inferencing system, “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a user profile input correlated to an aptitude measurement” and “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures” do not amount to significantly more for the reasons set forth in step 2A above.
Additionally, under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receive a user profile from a user…” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The claim does 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 elements processor, inferencing system, “train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a user profile input correlated to an aptitude measurement” and “configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12:
Claim 12, which incorporates the rejection of claim 11, recites further limitations such as “identify an updated aptitude measurement as a function of the updated user profile; and determine an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes” that are part of the abstract idea.
The claim recites an additional element:” receive an updated user profile as a function of the parameter changes.”
The” receive an updated user profile as a function of the parameter changes.”
step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 13:
Claim 13, which incorporates the rejection of claim 11, recites further limitations such as “the aptitude measurement comprises a plurality of aptitude measurements; and
each of the aptitude measurements of the plurality of aptitude measurements is categorized into positive aptitude measurements and negative aptitude measurements” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 16:
Claim 16, which incorporates the rejection of claim 11, recites further limitations such as “the aptitude measurement is reflected as a numerical score” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 17:
Claim 17, which incorporates the rejection of claim 11, recites further limitations such as “the endpoint element comprises a goal of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 18:
Claim 18, which incorporates the rejection of claim 11, recites further limitations such as “the aptitude measurement comprises a productivity score of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 19:
Claim 19, which incorporates the rejection of claim 11, recites further limitations such as “… positively increase the score of the aptitude measurement” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 20:
Claim 20, which incorporates the rejection of claim 11, recites further limitations such as “the activity metric comprises a task of the user” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
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 (i.e., changing from AIA to pre-AIA ) 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 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 nonobviousness.
Claims 1, 3, 6-7, 10-11, 13. 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal, III (US 2016/0086500 A1, hereinafter referred to as Kaleal), in view of Willardson et al. (US 2024/0031177 A1, hereinafter referred to as Willardson), and further in view of BENNETT et al. (US 2023/0218198 A1, hereinafter referred to as BENNETT), and Hamedi et al. (US 2020/0034887 A1, hereinafter referred to as Hamedi), and Yao et al. (US 2013/0103630 A1, hereinafter referred to as Yao).
As to claim 1, Kaleal teaches an apparatus for data structure generation using machine learning, wherein the apparatus comprises:
a processor (paragraph [0061], processor); and
a memory communicatively connected to the processor (paragraph [0061], memory), wherein the memory contains instructions configuring the processor to:
receive a user profile from a user, wherein the user profile comprises activity metrics and an endpoint element (paragraph [0009] receiving user profile information in association with employment of an avatar guidance system for physical fitness purposes; [0039] In one aspect, the reference physical and physiological activity metrics for the specific routine, program, action or task that is monitored for a user can be tailored or calibrated to fit the user's personal physical capabilities, functions and goals;
[0042] During performance of a fitness routine, a user's physiological and movement data is collected and compared to reference physical and physiological activity metrics know for the specific fitness routine in view of personal information for the user (e.g., health restrictions, preferences, goals, etc.) to determine whether an avatar response is warranted and if so, what the avatar response should be (e.g., a specific verbal and/or visual command), wherein the user profile further comprises at least an image component, wherein the at least an image component comprises a visual representation of user data, and wherein the endpoint element comprises a duration of time which the user desires to achieve a goal (paragraphs [0045]-[0046]
The avatar visualization system is further configured to generate a visual representation of the user that is a prediction of how the user will appear at a future point in time
based on performance of a health and fitness program by the user. In particular, the avatar visualization system can generate a visual representation or replica (e.g., an avatar) of a user based on currently received appearance information for the
user, currently received physical and physiological activity data for the user (e.g., physiological data and/or movement data), and known health information for the user (e.g., physical measurements, physical conditions, physical capabilities,
etc.) …The user can further be provided with a visual representation that demonstrates how the user will predicatively look after performance/completion of the program
if the user adheres to the requirements of the program. In addition, the user can select various time points in the program (e.g., after week 1, after week 2, after week 3, etc.)
and the avatar visualization system can generate a visual representation of the user that demonstrates how the user will predicatively look at the respective time points if the user adheres to the requirements of the program; wherein using the broadest reasonable interpretation, Examiner interprets the “various time points in the program (e.g., after week 1, after week 2, after week 3, etc.)” to include the “duration of time to achieve a goal (i.e. complete the program)).
Willardson teaches:
train an aptitude machine-learning model using aptitude training data wherein the
aptitude training data comprises at least a [pre-processed] user profile input correlated to an aptitude measurement (paragraph [0034] A "machine learning process," as used in this disclosure, is a process that automatedly uses a body of data known as "training data" and/or a "training set" to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs…A skill classifier may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency. A user 108 may have a plurality of skills, but machine-learning process 120 may only identify skills relevant to completed task 124. Alternatively, machine-learning process 120 may identify all skills associated with user data 144. Inputs to the machine-learning model may include completed task 124, skill classifier 128 and the like; [0041] Machine-learning process 120 may be trained using training data that includes previous input-output combinations from the model, various skills, various endpoint paths, various tasks, and the like. Training data may be gathered through previous iterations of the machine-learning process. Skill classifier 128 may output an endpoint path 140 based on endpoint 132 and completed task 124 extracted from the user data 144);
determine the aptitude measurement as a function of a trained aptitude machine-learning model, wherein the aptitude measurement comprises a comparison of an education level of the user to an expected education level (paragraphs [0034] A skill classifier may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency. A user 108 may have a plurality of skills, but machine-learning process 120 may only identify skills relevant to completed task 124. Alternatively, machine-learning process 120 may identify all skills associated with user data 144. Inputs to the machine-learning model may include completed task 124, skill classifier 128 and the like...); [0036] …user data 144 set may be scored to determine the skills of the user 108. For example, a completed task 124 may be scored with a numerical integer between 1-10, wherein 10 means that there are no deficiencies in the user's habits and 1 is total deficiency in the user's habits);
determine, using a strategic machine-learning model [comprising an inferencing system], a data structure as a function of the aptitude measurement determined using the trained aptitude machine-learning model, wherein the data structure comprises first parameter changes (paragraphs [0034]-[0035] A skill classifier (i.e. trained aptitude machine-learning model/ strategic machine-learning model) may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency; wherein Examiner interprets “the strength data and deficiency data” and “recommendations to further refine and strengthen its recommendations” to include “parameter changes”; [0038], scoring function may use algorithmic and/or aggregative processes to determine endpoint; [0041]).
However, Kaleal and Willardson fail to explicit teach:
pre-process the user profile, wherein preprocessing the user profile comprises at
least a feature extraction process configured to reduce a dimensionality of
the visual representation of user data; and
wherein the first parameter change comprises a feedback function wherein the feedback function configures alterations to the first parameter change as a function of user's desired aptitude measurement;
a machine-learning model comprising an inferencing system;
wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures:
generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set:
determining a degree of membership of the aptitude measurement in output linguistic variables:
selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time: and
defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes; and
display the determined data structure and the first parameter change using a display device.
BENNETT, in combination with Kaleal and Willardson, teaches:
pre-process the user profile, wherein preprocessing the user profile comprises at
least a feature extraction process configured to reduce a dimensionality of
the visual representation of user data (paragraphs [0131]-[0132] pre-processing layer 302; [0175], the graphic portions 410 (e.g., the first graphic portion 410A and the second graphic portion 410B) and the graphic element 406 of the neurofeedback GUI 400 of FIG. 4B are visual representations of recorded brain activity data that has been reduced using a dimensionality reduction function).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal and Willardson, to add dimension reduction to the combination system of Kaleal and Willardson, as taught by BENNETT, above. The modification would have been obvious because one of ordinary skill would be motivated to improve the accuracy and responsiveness of the computer system and to yield lower dimensional features that can capture most of the variance in the higher dimensional features, as taught by BENNETT ([0005] and [0175]).
Hamedi, in combination with Kaleal and Willardson and BENNETT, teaches:
wherein the first parameter change comprises a feedback function wherein the feedback function configures alterations to the first parameter change as a function of user's desired aptitude measurement (paragraphs [0162], a fluctuation may be meaningful to the user if it is based on a historical context, system usage and user performance, other current activities happening on the system or are being currently performed by the user, or feedback of other users of the system. The fluctuation could also be considered meaningful based on past searches, tracked fluctuations, criteria for custom author crowds, and/or user goals….; [0245] Using the system 1000, real-time adjustments can be made to the content items, based on real time feedback. This real-time feedback can be based on response data …. or based on activity data within the target audience, which may include the audience computing devices 1010, or a combination of both of these datasets.; [0296] …the system 1000 may capture feedback data from the user, or from other systems utilized by the user, such as "thumbs up" or "thumbs down" preference feedback. The system 1000 may also capture feedback related to a number of business performance metrics. Further, the system 1000 may incorporate this feedback data or a feedback loop based on the postings of the user and the actual performance; where, in combination with Willardson, the desired measurement is a desired aptitude measurement of Willardson).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson and BENNETT to add feedbacks to the combination system of Kaleal, Willardson and BENNETT, as taught by Hamedi, above. The modification would have been obvious because one of ordinary skill would be motivated to modify Willardson by including user feedbacks of Hamedi, regarding the user's desired aptitude measurement of Hamedi.
However, Kaleal, Willardson, BENNETT and Hamedi, fail to explicit teach:
a machine-learning model comprising an inferencing system;
wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures;
generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set;
determining a degree of membership of the aptitude measurement in output linguistic variables;
selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time; and
defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes; and
display the determined data structure and the first parameter change using a display device.
Yao, in combination with Kaleal, Willardson, BENNETT and Hamedi, teaches:
a machine-learning model comprising an inferencing system (paragraphs [0043]-[0044] and [0059] fuzzy inference system);
wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures (paragraphs [0086]-[[0091]…The LVs may be the same for each input,
representing for example Negative Large, Near Zero or Positive Large. The LVs appropriate to each variable and the names they are given, are a matter of design choice. In a neuro-fuzzy system, the membership functions applied are typically learned through training. (Learning could in principle be constrained by fixing the membership functions while learning the rule strengths, or vice versa...);
generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set (paragraphs [0088]-[0090] In Layer 4 (implication), in which the apparatus calculates the truncated fuzzy membership function for the output in each rule, again in a well-known manner. [0091] Layer 5 (aggregation) maps the fuzzy membership functions for all rules in the optimized system onto an aggregated fuzzy membership function representing the combined output for all the rules…);
determining a degree of membership of the aptitude measurement in output linguistic variables (paragraphs [0088]-[0089] In Layer 2 the fuzzy membership degrees of the inputs in each rule are mapped onto a firing strength for this rule, which is based on Conjunction (min) fuzzy rule bases in a well-known manner..);
[0089] In Layer 3 the ONR optimization is performed to remove redundancy by making the rule base monotonic. CS (conventional system) illustrates that there are Re rules in the fuzzy rule base without optimization, while OS (optimized system) represents the optimized system of only q rules, that is, one for each linguistic value of the output.);
selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time (paragraphs [0006]-[0016] The rule array in a preferred embodiment comprises a set of binary values, whereby the vector sum step places each of the membership degree values, which initially all lie in a predetermined numerical range, into one of two distinct numerical ranges, according to a corresponding binary value in the rule array…The step ( c) may comprise selecting a minimum element in said vector sum as the firing strength for the rule represented by said rule array. Taking the minimum implements the most common or 'conjunction' method of determining firing strength in fuzzy logic, but other functions are possible. 'Minimum' in this context refers to the minimum of the membership degrees represented by the elements of the array, regardless of the format in which they are physically represented within the apparatus…); and
defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes (paragraphs [0061] defuzzification interface 124; [0092]-[0094] Layer 6 ( defuzzification) maps the aggregated fuzzy membership function for an output in the system onto a crisp value for this output…; and [0116]); and
display the determined data structure and the first parameter change using a display device (paragraphs [0061] defuzzification interface 124).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT and Hamedi to add an inferencing system to the combination system of Kaleal, Willardson, BENNETT and Hamedi, as taught by Yao, above. The modification would have been obvious because one of ordinary skill would be motivated to have the fuzzy processing able to adapt to different regimes in real time has important benefits in reducing the complexity, and speeding up the process time, as taught by Yao ([0069]).
.
As to claim 3, which incorporates the rejection of claim 1, Willardson discloses wherein:
the aptitude measurement comprises a plurality of aptitude measurements (paragraphs [0065]- [0066], wherein Examiner interprets strength 532/strengths and “deficiency training data" as aptitude measurements);
and each of the aptitude measurements of the plurality of aptitude measurements is categorized into positive aptitude measurements and negative aptitude measurements (paragraphs [0065]- [0066] …], wherein Examiner interprets strength 532/strengths (i.e., positive aptitude measurements) and “deficiency training data"(i.e., negative aptitude measurements).
As to claim 6, which incorporates the rejection of claim 1, Willardson discloses wherein the aptitude measurement is reflected as a numerical score (paragraphs [0036]-[0038], a completed task 124 may be scored with a numerical integer between 1-10, wherein 10 means that there are no deficiencies in the user's habits and 1 is total
deficiency in the user's habits; scoring function).
As to claim 7, which incorporates the rejection of claim 1, Willardson discloses wherein the endpoint element comprises a goal of the user (paragraphs [0033] The term "endpoint resultant" as used herein, refers to the projected goal or task that the user wants to complete; [0035] The term "endpoint," as used herein, refers to a goal of a user. An endpoint may be a predetermined goal or a personalized goal that user 108 wants to achieve. For instance, if a user wants to become better at mathematics, their endpoint could be to master the topic of algebra).
As to claim 10, which incorporates the rejection of claim 1, Willardson discloses wherein the activity metric comprises a task of the user (paragraph [0033]
The term "task" refers to a piece of work to be done or undertaken. For instance, a user may be a child with an interest in learning more about algebra. A useful task for the user to engage with would be basic level algebra problems. The term "endpoint resultant" as used herein, refers to the projected goal or task that the user wants to complete.).
As to claim 11, Kaleal teaches a method for generation of a data structure using machine learning, wherein the method comprises:
receiving, by a processor (paragraph [0061], processor) a user profile from a user, wherein the user profile comprises activity metrics and an endpoint element (paragraph [0009] receiving user profile information in association with employment of an avatar guidance system for physical fitness purposes; [0039] In one aspect, the reference physical and physiological activity metrics for the specific routine, program, action or task that is monitored for a user can be tailored or calibrated to fit the user's personal physical capabilities, functions and goals; [0042] During performance of a fitness routine, a user's physiological and movement data is collected and compared to reference physical and physiological activity metrics know for the specific fitness routine in view of personal information for the user (e.g., health restrictions, preferences, goals, etc.) to determine whether an avatar response is warranted and if so, what the avatar response should be (e.g., a specific verbal and/or visual command), wherein the user profile further comprises at least an image component, wherein the at least an image component comprises a visual representation of user data, and wherein the endpoint element comprises a duration of time which the user desires to achieve a goal (paragraphs [0045]-[0046] The avatar visualization system is further configured to generate a visual representation of the user that is a prediction of how the user will appear at a future point in time based on performance of a health and fitness program by the user. In particular, the avatar visualization system can generate a visual representation or replica (e.g., an avatar) of a user based on currently received appearance information for the user, currently received physical and physiological activity data for the user (e.g., physiological data and/or movement data), and known health information for the user (e.g., physical measurements, physical conditions, physical capabilities, etc.) …The user can further be provided with a visual representation that demonstrates how the user will predicatively look after performance/completion of the program if the user adheres to the requirements of the program. In addition, the user can select various time points in the program (e.g., after week 1, after week 2, after week 3, etc.) and the avatar visualization system can generate a visual representation of the user that demonstrates how the user will predicatively look at the respective time points if the user adheres to the requirements of the program; wherein using the broadest reasonable interpretation, Examiner interprets the “various time points in the program (e.g., after week 1, after week 2, after week 3, etc.)” to include the “duration of time to achieve a goal (i.e. complete the program)).
Willardson teaches:
training, by the processor, an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a [pre-processed] user profile input correlated to an aptitude measurement (paragraph [0034] A "machine learning process," as used in this disclosure, is a process that automatedly uses a body of data known as "training data" and/or a "training set" to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs…A skill classifier may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency. A user 108 may have a plurality of skills, but machine-learning process 120 may only identify skills relevant to completed task 124. Alternatively, machine-learning process 120 may identify all skills associated with user data 144 . Inputs to the machine-learning model may include completed task 124, skill classifier 128 and the like…; [0041] Machine-learning process 120 may be trained using training data that includes previous input-output combinations from the model, various skills, various endpoint paths, various tasks, and the like. Training data may be gathered through previous iterations of the machine-learning process. Skill classifier 128 may output an endpoint path 140 based on endpoint 132 and completed task 124 extracted from the user data 144);
determining, by the processor, the aptitude measurement as a function of a trained aptitude machine-learning model, wherein the aptitude measurement comprises a comparison of an education level of the user to an expected education level (paragraphs [0034] A skill classifier may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency. A user 108 may have a plurality of skills, but machine-learning process 120 may only identify skills relevant to completed task 124. Alternatively, machine-learning process 120 may identify all skills associated with user data 144. Inputs to the machine-learning model may include completed task 124, skill classifier 128 and the like...); [0036] …user data 144 set may be scored to determine the skills of the user 108. For example, a completed task 124 may be scored with a numerical integer between 1-10, wherein 10 means that there are no deficiencies in the user's habits and 1 is total deficiency in the user's habits);
determine, using a strategic machine-learning model[comprising an inferencing system], a data structure as a function of the aptitude measurement determined using the trained aptitude machine-learning model, wherein the data structure comprises first parameter changes (paragraphs [0034]-[0035] A skillclassifier (i.e. trained aptitude machine-learning model/ strategic machine-learning model) may use user data 144 to group data. There may be at least strength data and deficiency data contained within information gained from user data 144. User data 144 may include deficiencies relevant to an objective. For example, the user data 144 may include examples such as a deficiency for a user 108 that wants to learn algebra but it struggling to isolate a necessary variable within the function. Skill classifier 128 may be able to identify the weakness and generate a path to work on this deficiency; wherein Examiner interprets “the strength data and deficiency data” and “recommendations to further refine and strengthen its recommendations” to include “parameter changes”; [0038], scoring function may use algorithmic and/or aggregative processes to determine endpoint; [0041]), wherein determining the data structure comprises:
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson and BENNETT to add feedbacks to the combination system of Kaleal, Willardson and BENNETT, as taught by Hamedi, above. The modification would have been obvious because one of ordinary skill would be motivated to modify Willardson by including user feedbacks of Hamedi, regarding the user's desired aptitude measurement of Hamedi.
However, Kaleal, Willardson, BENNETT and Hamedi, fail to explicit teach:
a machine-learning model comprising an inferencing system;
wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures;
generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set;
determining a degree of membership of the aptitude measurement in output linguistic variables;
selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time; and
defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes; and
displaying the determined data structure and the first parameter change using a display device.
Yao, in combination with Kaleal, Willardson, BENNETT and Hamedi, teaches:
a machine-learning model comprising an inferencing system (paragraphs [0043]-[0044] and [0059] fuzzy inference system);
wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures (paragraphs [0086]-[[0091]…The LVs may be the same for each input,
representing for example Negative Large, Near Zero or Positive Large. The LVs appropriate to each variable and the names they are given, are a matter of design choice. In a neuro-fuzzy system, the membership functions applied are typically learned through training. (Learning could in principle be constrained by fixing the membership functions while learning the rule strengths, or vice versa...);
generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set (paragraphs [0088]-[0090] In Layer 4 (implication), in which the apparatus calculates the truncated fuzzy membership function for the output in each rule, again in a well-known manner. [0091] Layer 5 (aggregation) maps the fuzzy membership functions for all rules in the optimized system onto an aggregated fuzzy membership function representing the combined output for all the rules…);
determining a degree of membership of the aptitude measurement in output linguistic variables (paragraphs [0088]-[0089] In Layer 2 the fuzzy membership degrees of the inputs in each rule are mapped onto a firing strength for this rule, which is based on Conjunction (min) fuzzy rule bases in a well-known manner..);
[0089] In Layer 3 the ONR optimization is performed to remove redundancy by making the rule base monotonic. CS (conventional system) illustrates that there are Re rules in the fuzzy rule base without optimization, while OS (optimized system) represents the optimized system of only q rules, that is, one for each linguistic value of the output.);
selecting, based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time (paragraphs [0006]-[0016] The rule array in a preferred embodiment comprises a set of binary values, whereby the vector sum step places each of the membership degree values, which initially all lie in a predetermined numerical range, into one of two distinct numerical ranges, according to a corresponding binary value in the rule array…The step ( c) may comprise selecting a minimum element in said vector sum as the firing strength for the rule represented by said rule array. Taking the minimum implements the most common or 'conjunction' method of determining firing strength in fuzzy logic, but other functions are possible. 'Minimum' in this context refers to the minimum of the membership degrees represented by the elements of the array, regardless of the format in which they are physically represented within the apparatus…); and
defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes (paragraphs [0061] defuzzification interface 124; [0092]-[0094] Layer 6 ( defuzzification) maps the aggregated fuzzy membership function for an output in the system onto a crisp value for this output…; and [0116]); and
displaying the determined data structure and the first parameter change using a display device (paragraphs [0061] defuzzification interface 124).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT and Hamedi to add an inferencing system to the combination system of Kaleal, Willardson, BENNETT and Hamedi, as taught by Yao, above. The modification would have been obvious because one of ordinary skill would be motivated to have the fuzzy processing able to adapt to different regimes in real time has important benefits in reducing the complexity, and speeding up the process time, as taught by Yao ([0069]).
.
As to claim 13, which incorporates the rejection of claim 11, Willardson discloses wherein:
the aptitude measurement comprises a plurality of aptitude measurements (paragraphs [0065]- [0066], wherein Examiner interprets strength 532/strengths and “deficiency training data" as aptitude measurements);
and each of the aptitude measurements of the plurality of aptitude measurements is categorized into positive aptitude measurements and negative aptitude measurements (paragraphs [0065]- [0066] …], wherein Examiner interprets strength 532/strengths (i.e., positive aptitude measurements) and “deficiency training data"(i.e., negative aptitude measurements).
As to claim 16, which incorporates the rejection of claim 11, Willardson discloses wherein the aptitude measurement is reflected as a numerical score (paragraphs [0036]-[0038], a completed task 124 may be scored with a numerical integer between 1-10, wherein 10 means that there are no deficiencies in the user's habits and 1 is total
deficiency in the user's habits; scoring function).
As to claim 17, which incorporates the rejection of claim 11, Willardson discloses wherein the endpoint element comprises a goal of the user (paragraphs [0033] The term "endpoint resultant" as used herein, refers to the projected goal or task that the user wants to complete; [0035] The term "endpoint," as used herein, refers to a goal of a user. An endpoint may be a predetermined goal or a personalized goal that user 108 wants to achieve. For instance, if a user wants to become better at mathematics, their endpoint could be to master the topic of algebra).
As to claim 20, which incorporates the rejection of claim 11, Willardson discloses wherein the activity metric comprises a task of the user (paragraph [0033]
The term "task" refers to a piece of work to be done or undertaken. For instance, a user may be a child with an interest in learning more about algebra. A useful task for the user to engage with would be basic level algebra problems. The term "endpoint resultant" as used herein, refers to the projected goal or task that the user wants to complete.).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal, III (US 2016/0086500 A1, hereinafter referred to as Kaleal), in view of Willardson et al. (US 2024/0031177 A1, hereinafter referred to as Willardson), and further in view of BENNETT et al. (US 2023/0218198 A1, hereinafter referred to as BENNETT), and Hamedi et al. (US 2020/0034887 A1, hereinafter referred to as Hamedi), and Yao et al. (US 2013/0103630 A1, hereinafter referred to as Yao), and BURLING et al. (US 2020/0005660 A1, hereinafter referred to as BURLING).
As to claim 2, which incorporates the rejection of claim 1, Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the memory contains instructions further configuring the processor to:
receive an updated user profile as a function of the parameter changes;
identify an updated aptitude measurement as a function of the updated user profile; and
determine an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes.
BURLING, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches:
receive an updated user profile as a function of the parameter changes (paragraphs [0047]- [0048], user profile is updated for the aptitude assertion. For instance, if the asserted aptitude is banking compliance management, elements could include consumer compliance, Bank Secrecy Act compliance, Community Reinvestment Act compliance, among other elements, while another aptitude may not have elements);
identify an updated aptitude measurement as a function of the updated user profile; and determine an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes (paragraphs [0050]-[0054]…If the asserted aptitude elements exist in the system, the method proceeds to step 260, in which the aptitude elements are recorded in the system for the user and then the method proceeds to step 265 in which the user profile is updated for the aptitude element(s) assertion…The method 200 then proceeds to step 270 where the method 200 assesses whether the user has configured its user profile to authorize transmission of the aptitude assertion and any aptitude elements to other systems, including, for example, another profile in another system or to other applications such as LinkedIn or Facebook; [0059] A record of validation activity of the aptitude and/or aptitude elements is recorded and then to step 324 in which the user profile is updated to indicate the assessment and validation of the aptitude and/or aptitude elements…; [0091]-[0092] …the aptitude management system transmits work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile and then displays work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile on the user computing system at step 880).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao to add an updated data structure to the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao, as taught by BURLING, above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to have enhancement opportunity results to the user associated with the updated user profile and then displays work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile, as suggested by BURLING ([0092).
As to claim 12, which incorporates the rejection of claim 11, Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the memory contains instructions further configuring the processor to:
receiving an updated user profile as a function of the parameter changes;
identify an updated aptitude measurement as a function of the updated user profile; and
determining an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes.
BURLING, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches:
receiving an updated user profile as a function of the parameter changes (paragraphs [0047]- [0048] … user profile is updated for the aptitude assertion. For instance, if the asserted aptitude is banking compliance management, elements could include consumer compliance, Bank Secrecy Act compliance, Community Reinvestment Act compliance, among other elements, while another aptitude may not have elements);
identifying an updated aptitude measurement as a function of the updated user profile; and determining an updated data structure as a function of the updated aptitude parameter, where the updated data structure comprises first parameters changes and second parameter changes (paragraphs [0050]-[0054]…If the asserted aptitude elements exist in the system, the method proceeds to step 260, in which the aptitude elements are recorded in the system for the user and then the method proceeds to step 265 in which the user profile is updated for the aptitude element(s) assertion…The method 200 then proceeds to step 270 where the method 200 assesses whether the user has configured its user profile to authorize transmission of the aptitude assertion and any aptitude elements to other systems, including, for example, another profile in another system or to other applications such as LinkedIn or Facebook; [0059]…. A record of validation activity of the aptitude and/or aptitude elements is recorded and then to step 324 in which the user profile is updated to indicate the assessment and validation of the aptitude and/or aptitude elements…; [0091]-[0092], the aptitude management system transmits work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile and then displays work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile on the user computing system at step 880).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao add an updated data structure to the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao, as taught by BURLING, above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to have enhancement opportunity results to the user associated with the updated user profile and then displays work opportunity gaps and enhancement opportunity results to the user associated with the updated user profile, as suggested by BURLING ([0092).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal, III (US 2016/0086500 A1, hereinafter referred to as Kaleal), in view of Willardson et al. (US 2024/0031177 A1, hereinafter referred to as Willardson), and further in view of BENNETT et al. (US 2023/0218198 A1, hereinafter referred to as BENNETT), and Hamedi et al. (US 2020/0034887 A1, hereinafter referred to as Hamedi), and Yao et al. (US 2013/0103630 A1, hereinafter referred to as Yao), and Hull (US 2014/0358606 A1, hereinafter referred to as Hull).
As to claim 8, which incorporates the rejection of claim 1, Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the aptitude measurement comprises a productivity score of the user.
Hull, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches wherein the aptitude measurement comprises a productivity score of the user (paragraphs [0008] and [0023], the collaboration tools may record the completion of 100 tasks by a given team within a short period of time, whereas other teams have completed at most 60 tasks within the same period of time. This may allow system 130 to assign a higher "productivity" score for the individuals of the given team, at least when working together).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao to add a productivity score to the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao, as taught by Hull, above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to promote communication within the organization and especially within teams of employees, and reflects activity and accomplishments of teams and/or individuals, as suggested by Hull ([0022).
As to claim 18, which incorporates the rejection of claim 11, Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the aptitude measurement comprises a productivity score of the user.
Hull, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches wherein the aptitude measurement comprises a productivity score of the user (paragraphs [0008] and [0023], the collaboration tools may record the completion of 100 tasks by a given team within a short period of time, whereas other teams have completed at most 60 tasks within the same period of time. This may allow system 130 to assign a higher "productivity" score for the individuals of the given team, at least when working together).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao to add a productivity score to the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao, as taught by Hull, above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to promote communication within the organization and especially within teams of employees, and reflects activity and accomplishments of teams and/or individuals, as suggested by Hull ([0022).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal, III (US 2016/0086500 A1, hereinafter referred to as Kaleal), in view of Willardson et al. (US 2024/0031177 A1, hereinafter referred to as Willardson), and further in view of BENNETT et al. (US 2023/0218198 A1, hereinafter referred to as BENNETT), and Hamedi et al. (US 2020/0034887 A1, hereinafter referred to as Hamedi), and Yao et al. (US 2013/0103630 A1, hereinafter referred to as Yao), and Okamoto et al. (US 2002/0156674 A1, hereinafter referred to as Okamoto).
As to claim 9, which incorporates the rejection of claim 1, Willardson teaches parameter changes but Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the parameter changes comprise one or more instructions to positively increase the score of the aptitude measurement.
Okamoto, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches one or more instructions to positively increase the score of the aptitude measurement (paragraph [0052] However, when an especially required recruiting condition is listed, a member who satisfies that condition can be selected by increasing the score awarded in that case. For example, according to the recruiting conditions in FIG. 3, the highest aptitude level is 50 points (this is obtained by adding the highest scores (ten points) for the five condition items)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao to increase the score of the aptitude measurement to the combination system Kaleal, Willardson, BENNETT, Hamedi and Yao, above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to efficiently search for and apply for a job for which he or she will probably be hired, and the business provider can obtain a superior employee whose skills match the recruiting conditions, as suggested by Okamoto) ([0067).
As to claim 19, which incorporates the rejection of claim 11, Willardson teaches parameter changes but Kaleal, Willardson, BENNETT, Hamedi and Yao fail to explicitly teach wherein the parameter changes comprise one or more instructions to positively increase the score of the aptitude measurement.
Okamoto, in combination with Kaleal, Willardson, BENNETT, Hamedi and Yao, teaches one or more instructions to positively increase the score of the aptitude measurement (paragraph [0052] …However, when an especially required recruiting condition is listed, a member who satisfies that condition can be selected by increasing the score awarded in that case. For example, according to the recruiting conditions in FIG. 3, the highest aptitude level is 50 points (this is obtained by adding the highest scores (ten points) for the five condition items) …).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Kaleal, Willardson, BENNETT, Hamedi and Yao to increase the score of the aptitude measurement to the combination system Kaleal, Willardson, BENNETT, Hamedi and Yao, as taught by Okamoto above. The modification would have been obvious because one of ordinary skill would be motivated to have user to allow user to efficiently search for and apply for a job for which he or she will probably be hired, and the business provider can obtain a superior employee whose skills match the recruiting conditions, as suggested by Okamoto) ([0067).
Response to Applicant’s arguments
Applicant's arguments on file on 02/17/2026 with respect to claims 1,3-7, 9-14 and 16-20 are moot in view of new ground(s) of rejection.
Claims Rejected Under 35 U.S.C. § 101
Applicant submits that, according to MPEP 2106.04, independent claims 1 and 11, and their dependent claims, are allowable under Step 2A and/or 2B of the eligibility analysis, as discussed further below in this paper.
Argument (page 6)
Claim 1 is directly analogous to Example 39 in that it recites a multi-stage, computer implemented machine-learning pipeline that cannot practically be carried out in the human mind.
In particular, claim 1 first recites "train an aptitude machine-learning model using aptitude training data wherein the aptitude training data comprises at least a pre-processed user profile input correlated to an aptitude measurement; determine the aptitude measurement as a function of a trained aptitude machine-learning model", and then further recites "determine, using a strategic machine-learning model comprising an inferencing system, a data structure as a function of the aptitude measurement ... wherein determining the data structure comprises:
configuring the inferencing system using strategic training data as an input vector including a plurality of aptitude measurements correlated to endpoint elements and an output vector including a plurality of examples of data structures; generating a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set; determining a degree of membership of the aptitude measurement in output linguistic variables; selecting, based on the degree of membership, a data structure whose first parameter changes comprise
instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time; and defuzzifying an output of the inferencing system to generate the data structure comprising first parameter changes." Just as Example 39's claim recites creating successive training sets and training a neural network in multiple stages, claim 1 recites constructing vectorized training data, configuring an inferencing system with that data, computing degrees of membership in output linguistic variables, and defuzzifying the inferencing system's output to produce a machine generated data structure. Applicant respectfully submits that, for the same reasons the Office recognized in Example 39 that limitations such as "training the neural network in a first stage using the first training set" do not recite a judicial exception, the above-quoted limitations of claim 1 likewise do not recite "mental processes," because they describe multi-step machine learning configuration and inferencing operations that are not practically performable in the
human mind.
Examiner response
Examiner respectfully disagrees. Example 39 was held eligible as it did not recite any judicial exception, and in this case, judicial exception has been identified.
The analysis of the training in example 39 is not analogous to the analysis of training steps in the instant claims. This is because in example 39, the training limitations were not required to be analyzed under step 2A prong 2 or step 2B as it was determined in step 2A prong 1 that the claim did not recite any judicial exception.
On the other hand, in the instant claim as analyzed in the rejection, claims do recite a judicial exception and do not involve any training steps.
Step 2A, Prong one
Argument (pages 7-8)
Similar to the claims found eligible in Enfish, Applicant asserts that, as amended, claim 1 is directed to a specific improvement in how the recited processor is configured to store and manipulate data, not to generic "implementation via computers" of a mental process as in Alice.
Claim 1 does not simply add a data-processing system or communications controller to an abstract idea; instead, it configures the processor and memory with a particular internal architecture and control flow: the processor is (i) configured to receive a user profile that includes high-dimensional inputs such as activity metrics, an endpoint element representing a duration of time to achieve a goal, and an image component; (ii) configured to pre-process that user profile via feature extraction that reduces the dimensionality of the visual representation of user data; (iii) configured to train an aptitude machine-learning model and compute an aptitude measurement; and, critically, (iv) configured to operate a strategic machine-learning model comprising an inferencing system that uses strategic training data organized into an input vector (plurality of aptitude measurements correlated to endpoint elements) and an output vector
(plurality of examples of data structures), generates rules with linguistic variables representing data structures, computes degrees of membership of the aptitude measurement in output linguistic variables, selects, based on those membership values, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time, and defuzzifies the inferencing system's output to generate the data structure comprising the first parameter changes. This is not a bare recital of a computer as a conduit for an abstract idea; it is a specific logical structure and sequence of operations that changes how the processor organizes, represents, and transforms user profile and training data (via vectorization,
linguistic-variable rule application, membership evaluation, and defuzzification) to generate the claimed data structure. As in Enfish, where the self-referential table was found to improve the functioning of the computer itself rather than merely using a computer as a tool, the present claim's defined inferencing architecture and data representations constitute a concrete improvement in the way the processor processes and structures data, and thus integrate any alleged judicial exception into a practical application under Step 2A, Prong Two, rather than reciting the kind of generic computer implementation that Alice held insufficient..
Examiner response
Examiner respectfully disagrees. The claim does recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. (MPEP 2106.04(a)(2)).
The claimed “pre-process” is an observation or evaluation based on the user profile, wherein preprocessing the user profile comprises at least a feature extraction process configured to reduce a dimensionality of the visual representation of user data.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person compares an education level of the user to an expected education level. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “determine” is an observation or evaluation based on the aptitude measurement as a function of a trained aptitude machine-learning model, wherein the aptitude measurement comprises a comparison of an education level of the user to an expected education level.
This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person compares an education level of the user to an expected education level. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “determine” is an observation or evaluation based on using a strategic machine-learning model comprising [an inferencing system], a data structure as a function of the aptitude measurement determined [using the trained aptitude machine-learning model], wherein the data structure comprises a first parameter change[[s]], wherein the first parameter change comprises a feedback function wherein the feedback function configures alterations to the first parameter change as a function of user's desired aptitude measurement.” This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines an aptitude measurement. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “generating” is an observation or evaluation based on a set of rules including linguistic variables that represent one or more data structures, wherein the plurality of aptitude measurements and the plurality of examples of data structures each represent a set. This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person is generating or creating rules. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “determine” is an observation or evaluation based on a degree of membership of the aptitude measurement in output linguistic variables. This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person determines a group membership. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “selecting” is an observation or evaluation based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative
category to a positive category within a predetermined duration of time. This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person selecting a group membership. Such mental observations or evaluations fall within the “mental processes” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an observation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
The claimed “defuzzifying” is an observation or evaluation based on the degree of membership, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time. This type of observation or evaluation is an act that can be practically performed in the human mind, similar to the mental thought processes that occur when a person uses a mathematical concept to convert a fuzzy output. (a range of possibilities) into a single, precise numerical value. Such evaluations fall within the “mathematical concept” grouping of abstract idea set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52.
Examiner interpreted this limitation as an evaluation. See MPEP 2106.04(a), particularly MPEP 2106.04(a)(2)(III)(C).
Accordingly, Examiner respectfully submits that the limitations of claim 1 are directed to the abstract idea of a mental process under Step 2A, Prong One.
Argument (pages 8-9)
In addition, the USPTO's recent precedential decision in Ex parte Desjardins et al.,
Appeal No. 2024-000567, Application 16/319,040, ARP Decision on Request for Rehearing (P.T.A.B. Sept. 26, 2025) (Precedential, Nov. 4, 2025), confirms that claims reciting specific improvements to the training and operation of machine-learning models are subject matter eligible under Step 2A, Prong Two. In Desjardins, the Appeals Review Panel interpreted the 2019 PEG and MPEP §§ 2106.04(d)(l) and 2106.05(a) and held that "claims directed to an improvement in the functioning of a computer or an improvement to other technology or technical field are patent eligible," relying on the specification's description of improvements in training the machine-learning model itself ( e.g., allowing AI systems to use less storage and enabling reduced system complexity) and on claim language that reflected that improvement ( e.g., adjusting parameter values to optimize performance on a second task while protecting performance on a first task). Under Desjardins, which is precedential and therefore binding on the Office, see also SEC v. Chenery Corp., 332 U.S. 194 (1947) ("Chenery II") (recognizing agencies' ability to establish generally applicable rules through adjudicative decisions), the
Office must give effect to the principle that claims reciting such specific improvements to
machine-learning training and operation integrate any alleged abstract idea into a practical application. Accordingly, the Examiner cannot simply characterize these limitations as "generic computer implementation" or disregard the Desjardins-style analysis.
Accordingly, Applicant submits that claim 1, and the other claims, are not directed to an
abstract idea of a mental process.
Examiner response
Examiner respectfully disagrees. In Desjardins, Appellant identifies certain limitations of independent claim 1 and asserts that the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training,” citing paragraph 21 of the Specification for support. The board agreed with the Appellant.
Unlike Desjardins, the independent claim 1 of the instant application does not recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the computer-implemented method.
Our analysis is based on the Recentive Analytics wherein the Machine Learning is used at a high level. That is, the claims do not delineate steps through which the machine learning technology achieves an improvement.
In the instant claims, the newly added claim features do not improve the functionality of a computer or any technology.
Accordingly, Claim 1 does not integrate the abstract ideas into a practical application and is not patent-eligible under Step 2A, Prong Two.
Step 2B
Argument:
Applicant appears to assert that Claim 1 as amended mirrors the reasoning in BASCOM because, even if the individual hardware elements (e.g., a processor and memory) are viewed as generic, the ordered combination of the recited machine-learning and inferencing steps amounts to "significantly more" by virtue of a non-conventional, non-generic arrangement. As amended, claim 1 does not merely recite "using a computer" to perform mental steps; rather, it specifies a particular configuration in which the processor (i) receives a user profile including activity metrics, an endpoint element, and an image component; (ii) pre-processes that profile via feature extraction to reduce the dimensionality of the visual representation of user data; (iii) trains an aptitude
machine-learning model and determines an aptitude measurement; and then (iv) uses a strategic machine-learning model comprising an inferencing system that is configured with strategic training data mapped to an input vector (plurality of aptitude measurements correlated to endpoint elements) and an output vector (plurality of examples of data structures), generates rules with linguistic variables representing data structures, determines degrees of membership of the aptitude measurement in output linguistic variables, selects, based on those membership values, a data structure whose first parameter changes comprise instructions to adjust at least one activity metric to change the aptitude measurement from a negative category to a positive category within a predetermined duration of time, and defuzzifies the inferencing system's
output to generate the data structure comprising the first parameter changes. In combination, these limitations define a specific, non-generic ML and fuzzy-inference architecture, including particular data representations (vectors, linguistic variables, membership values) and a structured selection/defuzzification sequence, that, like the filtering architecture in BASCOM, provides a technical improvement in how the system processes and transforms data and therefore amounts to "significantly more" than any alleged abstract idea under Step 2B.
As such, Applicant submits that claim 1 as amended is allowable under 35 U.S.C. §101 at least for the reasons stated above.
Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Applicant
submits that the rejection to claim 11 has been overcome for the same reasons as to claim 1.
Applicant respectfully requests reconsideration and withdrawal of the rejection. Claims 2-3, 6-10, 12, 13 and 16-20 depend, directly or indirectly, on claims I or 11 and thus recite all of the same elements as claim I and claim 11. Applicant, therefore, submits that claims 2-3, 6-10, 12, 13 and 16-20 overcome these rejections for at least the same reasons as discussed above with reference to claims I and 11.
Examiner’s response:
Examiner respectfully disagrees. The claim as a whole does not integrate the mental process into a practical application. It is important to note that in order for a claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally cannot be said to improve computer technology. MPEP 2106.05(a). As noted in this action, to show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method.
Claim 1 does 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 elements
to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
In BASCOM, the court determined that the claimed combination of limitations did not simply recite an instruction to apply the abstract idea of filtering content on the Internet. BASCOM Global Internet Servs. v. AT&T Mobility, LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016). Instead, the claim recited a "technology based solution" of filtering content on the Internet that overcome the disadvantages of prior art filtering systems. 827 F.3d at 1350-51, 119 USPQ2d at 1243. Finally, in Thales Visionix, the particular configuration of inertial sensors and the particular method of using the raw data from the sensors was more than simply applying a law of nature. Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017). The court found that the claims provided a system and method that "eliminate[d] many ‘complications’ inherent in previous solutions for determining position and orientation of an object on a moving platform." In other words, the claim recited a technological solution to a technological problem.
As such, Examiner submits that claim 1 as amended is not allowable under 35 U.S.C. §101, at least for the reasons stated above and does not mirror the reasoning in BASCOM.
Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Examiner submits that the rejection to claim 11 has not been overcome for the same reasons as to claim 1.
Claims 3, 6-10, 12-13, and 16-20 are dependent from one of the not patent eligible independent claims (i.e., claims 1 and 11) discussed above, and are therefore believed to be not patent eligible for at least the same reasons.
Therefore, claims 3, 6-10, 12-13, and 16-20 are not patent eligible due to their nature of dependence upon their respective independent claims and the rejection under 35 U.S.C. §101 is respectfully maintained.
Rejections under 35 U.S.C. §103
Applicant’s arguments are moot in view of new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/ABABACAR SECK/Examiner, Art Unit 2122
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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