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 .
Examiner Comments
The examiner notes that claims 9 and 12 recite contingent limitations. The contingent limitations are storing, using a storage unit of the information processing device, a trained model in which, when the target person information and the exercise evaluation information are input, evaluation of exercise information of the target person is estimated to be improved, and instruction content based on the age information is output; in claim 9 and when instruction content based on target person information including a plurality of parameters related to a target person and exercise evaluation information about evaluation of an exercise of the target person, and the exercise evaluation information after the instruction are input, an effective parameter among the plurality of parameters of the target person information is analyzed in claim 12. Contingent limitations are not required to be met as they are conditional to a prior condition (MPEP 2111.04). The examiner recommends the applicant amends the independent claims to fix the contingency. For the purposes of compact prosecution, the contingent limitations are interpreted as having their prior conditions met.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a target person information acquisition unit configured to acquire… in claim 1.
an exercise evaluation information acquisition unit configured to acquire… in claim 1.
a storage unit configured to store… in claim 1.
a report output unit configured to input… in claim 1.
the storage unit stores… in claims 2-4.
an exercise information acquisition unit configured to acquire… in claim 6.
acquiring target person information…using a target person information acquisition unit… in claim 9.
acquiring exercise evaluation information…using an exercise evaluation information acquisition unit… in claim 9.
storing, using a storage unit… in claim 9.
and outputting an instruction report…using a report output unit… in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112: Indefiniteness
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1-4 and 9, the claims recite the limitation evaluation of exercise information of the target person is estimated to be improved. The claims are indefinite as it is unclear the amount of improvement required to consider “to be improved”. For the purposes of examination, the “to be improved” is interpreted as improvement in an exercise.
Regarding claim 5, the claim recites the term ideal exercise information. The term “ideal exercise information” is a relative term which renders the claim indefinite. The term “ideal exercise information” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding claims 6 and 7, the claims are rejected for at least their dependence to claim 1.
Regarding claims 8 and 12, the claims recite the limitation the exercise evaluation information and the exercise evaluation information after the instruction are input. There is insufficient antecedent basis for this limitation in the claim because the term “the instruction” lacks antecedent basis. For the purposes of examination, “the instruction” is interpreted as recommendations or suggestions provided to a user.
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 1-9 and 12 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An information processing device. The claim recites a machine. A machine is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
when the target person information and the exercise evaluation information are input, evaluation of exercise information of the target person is estimated to be improved, and instruction content based on the age information is output; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining whether a person is performing an exercise correctly and giving suggestions to improve, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a target person information acquisition unit configured to acquire target person information including age information indicating an age of a target person; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
an exercise evaluation information acquisition unit configured to acquire exercise evaluation information about evaluation of an exercise of the target person; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
a storage unit configured to store a trained model in which, (i.e., the broadest reasonable interpretation of storing a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
and a report output unit configured to input the target person information and the exercise evaluation information to the trained model and output an instruction report including the instruction content output from the trained model. (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation(s) (II-V), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein the target person information includes physique information about a physique of the target person, and the storage unit stores the trained model in which,. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 2 also recites when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the physique information is output. Under the broadest reasonable interpretation, the limitations recite determining whether a person is performing an exercise correctly and giving suggestions to improve which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein the target person information includes personality information about a personality of the target person, and the storage unit stores the trained model in which,. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 3 also recites when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the personality information is output. Under the broadest reasonable interpretation, the limitations recite determining whether a person is performing an exercise correctly and giving suggestions to improve which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the target person information includes environmental information about a place at which the target person lives and a family structure of the target person, and the storage unit stores the trained model in which,. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 4 also recites when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the environment information is output. Under the broadest reasonable interpretation, the limitations recite determining whether a person is performing an exercise correctly and giving suggestions to improve which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein the exercise evaluation information is information obtained by comparing ideal exercise information for the age of the target person with exercise information about the exercise of the target person. Under the broadest reasonable interpretation, the limitations recite comparing a person’s performance against other people’s performances which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 5 does not solve the deficiencies of claim 1.
Regarding claim 6, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites further comprising an exercise information acquisition unit configured to acquire the exercise information about the exercise of the target person based on information from a first detection device attached to the target person. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 6 does not solve the deficiencies of claim 1.
Regarding claim 7, it is dependent upon claim 6 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites wherein the first detection device detects information about at least one of acceleration, position information, and angular velocity. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 7 does not solve the deficiencies of claim 6.
Regarding claim 8, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 8 recites wherein the trained model is a learning model configured to…. Under the broadest reasonable interpretation, the limitations merely recite steps that apply input data to a generic machine learning model for a prediction, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Claim 8 also recites analyze an effective parameter among a plurality of parameters of the target person information during learning when the instruction content based on the target person information including the plurality of parameters related to the target person and the exercise evaluation information and the exercise evaluation information after the instruction are input. Under the broadest reasonable interpretation, the limitations recite analyzing a person’s performance when given suggestions/instructions which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 8 does not solve the deficiencies of claim 1.
Regarding claim 9, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An information processing method. The claim recites a method. A method is one of the four statutory categories of invention. For steps 2A/2B of the 101 analysis, claim 9 is similar to claim 1 and is rejected under the same rationales.
Regarding claim 12, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A model being trained. A model being trained is interpreted as computer program. Therefore, the claimed invention in claim 12 is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a computer program is interpreted as “software per se” which is not one of the four statutory categories (MPEP 2106.03). Applicant is encouraged to amend the claim into one of the four statutory categories. For the purposes of compact prosecution, the additional steps of the 101 analysis will be performed below.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
wherein, when instruction content based on target person information including a plurality of parameters related to a target person and exercise evaluation information about evaluation of an exercise of the target person, and the exercise evaluation information after the instruction are input, an effective parameter among the plurality of parameters of the target person information is analyzed. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like analyzing a person’s performance when given suggestions/instructions, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A model being trained… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (II), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-9, and 12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Asikainen, et al., US Pre-Grant Publication US20210008413A1 (“Asikainen”).
Regarding claim 1, Asikainen discloses:
An information processing device comprising: (Asikainen, ⁋47, “FIG. 2 is a block diagram illustrating one embodiment of a computing device 200 including a personal training application 110. The computing device 200 may also include a processor 235, a memory 237, a display device 239, a communication unit 241, an optional capture device 245, an input/output device(s) 247, optional sensor(s) 249, and a data storage 243, according to some examples [An information processing device comprising:].”).
a target person information acquisition unit configured to acquire target person information including age information indicating an age of a target person; (Asikainen, ⁋64, “In some implementations, the personal training engine 202 receives a user profile from a user's social network account with permission from the user…The user profile received from the third-party social network server 140 may include one or more of the user's age, gender, interests, location, and other demographic information [a target person information acquisition unit configured to acquire target person information including age information indicating an age of a target person;].”).
an exercise evaluation information acquisition unit configured to acquire exercise evaluation information about evaluation of an exercise of the target person; (Asikainen, ⁋46, “FIG. 1B is a diagram illustrating an example configuration for tracking physical activity of a user performing exercise movements and providing feedback and recommendations relating to performing the exercise movements [an exercise evaluation information acquisition unit configured to acquire exercise evaluation information about evaluation of an exercise of the target person;].”).
a storage unit configured to store a trained model in which, (Asikainen, ⁋58, “The data storage 243 may store, among other data, user profiles 222, training datasets 224, machine learning models 226, and workout programs 228 [a storage unit configured to store a trained model in which,].”).
when the target person information and the exercise evaluation information are input, evaluation of exercise information of the target person is estimated to be improved, and instruction content based on the age information is output; (Asikainen, ⁋90, “The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, [and the exercise evaluation information are input,] the vital signs and health status signals, performance data, object detection data, user profile, and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group […based on the age information…], gender, weight, height, fitness preference, similar performance and workout patterns [when the target person information].” and Asikainen, ⁋92, “The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user [evaluation of exercise information of the target person is estimated to be improved,]. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low [and instruction content… is output;].”).
and a report output unit configured to input the target person information and the exercise evaluation information to the trained model and output an instruction report including the instruction content output from the trained model. (Asikainen, ⁋92, “The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low [and output an instruction report including the instruction content output from the trained model.].” and Asikainen, ⁋91, “The recommendation engine 210 uses the tagged sequences from the aggregate user dataset to train a machine learning model (e.g., CNN) to identify or predict a level of fatigue in the exercise movement [and a report output unit configured to input the target person information and the exercise evaluation information to the trained model].”).
Regarding claim 2, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses:
wherein the target person information includes physique information about a physique of the target person, (Asikainen, ⁋65, “In some implementations, the user profile 222 may include additional information about the user including name, age, gender, height, weight, profile photo, 3D body scan [wherein the target person information includes physique information about a physique of the target person,]”).
and the storage unit stores the trained model in which, (Asikainen, ⁋58, “The data storage 243 may store, among other data, user profiles 222, training datasets 224, machine learning models 226, and workout programs 228 [and the storage unit stores the trained model in which,].”).
when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the physique information is output. (Asikainen, ⁋90, “The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, [and the exercise evaluation information are input,] the vital signs and health status signals, performance data, object detection data, user profile [and the physique information…], and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group […on the age information], gender, weight, height, fitness preference, similar performance and workout patterns [when the target person information].” and Asikainen, ⁋92, “The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user [evaluation of exercise information of the target person is estimated to be improved,]. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low [and instruction content…is output.]”).
Regarding claim 3, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses:
wherein the target person information includes personality information about a personality of the target person, (Asikainen, ⁋65, “In some implementations, the user profile 222 may include additional information about the user including name, age, gender, height, weight, profile photo, 3D body scan, training preferences (e.g. HIIT, Yoga, barbell powerlifting, etc.), fitness goals (e.g., gain muscle, lose fat, get lean, etc.), fitness level (e.g., beginner, novice, advanced, etc.), fitness trajectory (e.g., losing 0.5% body fat monthly, increasing bicep size by 0.2 centimeters monthly, etc.), workout history (e.g., frequency of exercise, intensity of exercise, total rest time, average time spent in recovery, average time spent in active exercise, average heart rate, total exercise volume, total weight volume, total time under tension, one-repetition maximum, etc.), activities (e.g. personal training sessions, workout program subscriptions, indications of approval, multi-user communication sessions, purchase history, synced wearable devices, synced third-party applications, followers, following, etc.), video and audio of performing exercises, and profile rating and badges (e.g., strength rating, achievement badges, etc.) [wherein the target person information includes personality information about a personality of the target person,].”).
and the storage unit stores the trained model in which, (Asikainen, ⁋58, “The data storage 243 may store, among other data, user profiles 222, training datasets 224, machine learning models 226, and workout programs 228 [and the storage unit stores the trained model in which,].”).
when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the personality information is output. (Asikainen, ⁋90, “The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, [and the exercise evaluation information are input,] the vital signs and health status signals, performance data, object detection data, user profile [and the personality information…], and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group […on the age information], gender, weight, height, fitness preference, similar performance and workout patterns [when the target person information].” and Asikainen, ⁋92, “The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user [evaluation of exercise information of the target person is estimated to be improved,]. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low [and instruction content…is output.]”).
Regarding claim 5, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses wherein the exercise evaluation information is information obtained by comparing ideal exercise information for the age of the target person with exercise information about the exercise of the target person. (Asikainen, ⁋84, “the movement adherence monitor 310 uses a machine learning model, such as a convolutional neural network trained on a large set of ideal or correct repetitions of an exercise movement to determine a score or a quality of the exercise movement performed by the user based at least on the estimated 3D pose data and the consecutive repetitions of the exercise movement [wherein the exercise evaluation information is information obtained by comparing ideal exercise information…with exercise information about the exercise of the target person.].” and Asikainen, ⁋90, “The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, the vital signs and health status signals, performance data, object detection data, user profile, and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group [comparing ideal exercise information for the age of the target person]”).
Regarding claim 6, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses further comprising an exercise information acquisition unit configured to acquire the exercise information about the exercise of the target person based on information from a first detection device attached to the target person. (Asikainen, ⁋46, “Concurrently, the IMU sensor 132 on the equipment 134 a in motion and the wearable device 130 on the person of the user [based on information from a first detection device attached to the target person.] are communicatively coupled with the interactive personal training device 108 to transmit recorded IMU sensor data and recorded vital signs and health status information (e.g., heart rate, blood pressure, etc.) during the performance of the exercise movement to the interactive personal training device 108. For example, the IMU sensor 132 records the velocity and acceleration, 3D positioning, and orientation of the equipment 134 a during exercise movement [further comprising an exercise information acquisition unit configured to acquire the exercise information about the exercise of the target person].”).
Regarding claim 7, Asikainen discloses the information processing device according to claim 6. Asikainen further discloses wherein the first detection device detects information about at least one of acceleration, position information, and angular velocity. (Asikainen, ⁋46, “Concurrently, the IMU sensor 132 on the equipment 134 a in motion and the wearable device 130 on the person of the user [wherein the first detection device] are communicatively coupled with the interactive personal training device 108 to transmit recorded IMU sensor data and recorded vital signs and health status information (e.g., heart rate, blood pressure, etc.) during the performance of the exercise movement to the interactive personal training device 108. For example, the IMU sensor 132 records the velocity and acceleration, 3D positioning, and orientation of the equipment 134 a during exercise movement [detects information about at least one of acceleration, position information, and angular velocity.].”).
Regarding claim 8, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses wherein the trained model is a learning model configured to analyze an effective parameter among a plurality of parameters of the target person information during learning when the instruction content based on the target person information including the plurality of parameters related to the target person and the exercise evaluation information and the exercise evaluation information after the instruction are input. (Asikainen, ⁋72, “In another example, the machine learning engine 206 may train a machine learning model 226 to classify an adherence of an exercise movement performed by a user to predefined conditions for correctly performing the exercise movement [wherein the trained model is a learning model configured to analyze an effective parameter among a plurality of parameters of the target person information during learning].” and ⁋84, “The movement adherence monitor 310 receives data including the estimated 3D pose data from the pose estimator 302, the object data from the object detector 304, the exercise classification result from the action recognizer 306, and the consecutive repetitions of the exercise movement from the repetition counter 308 for determining whether the user performance of one or more repetitions of the exercise movement adhere to predefined conditions or thresholds for correctly performing the exercise movement [when the instruction content based on the target person information including the plurality of parameters related to the target person and the exercise evaluation information and the exercise evaluation information after the instruction are input.].”).
Regarding claim 9, the claim is similar to claim 1 and is rejected under the same rationales.
Regarding claim 12, Asikainen discloses A model being trained, wherein, when instruction content based on target person information including a plurality of parameters related to a target person and exercise evaluation information about evaluation of an exercise of the target person, and the exercise evaluation information after the instruction are input, an effective parameter among the plurality of parameters of the target person information is analyzed. (Asikainen, ⁋72, “In another example, the machine learning engine 206 may train a machine learning model 226 [A model being trained, wherein,] to classify an adherence of an exercise movement performed by a user to predefined conditions for correctly performing the exercise movement [an effective parameter among the plurality of parameters of the target person information is analyzed.].” and ⁋84, “The movement adherence monitor 310 receives data including the estimated 3D pose data from the pose estimator 302, the object data from the object detector 304, the exercise classification result from the action recognizer 306, and the consecutive repetitions of the exercise movement from the repetition counter 308 for determining whether the user performance of one or more repetitions of the exercise movement adhere to predefined conditions or thresholds for correctly performing the exercise movement [when instruction content based on target person information including a plurality of parameters related to a target person and exercise evaluation information about evaluation of an exercise of the target person, and the exercise evaluation information after the instruction are input,].”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Asikainen, et al., US Pre-Grant Publication US20210008413A1 (“Asikainen”) in view of Pauley, et al., US Pre-Grant Publication US20210104173A1 (“Pauley”).
Regarding claim 4, Asikainen discloses the information processing device according to claim 1. Asikainen further discloses:
wherein the target person information includes environmental information about a place at which the target person lives…, (Asikainen, ⁋64, “For example, the personal training engine 202 may access an API 136 of a third-party social network server 140 to request a basic user profile to serve as a starter profile. The user profile received from the third-party social network server 140 may include one or more of the user's age, gender, interests, location, and other demographic information [wherein the target person information includes environmental information about a place at which the target person lives…,].”).
and the storage unit stores the trained model in which, (Asikainen, ⁋58, “The data storage 243 may store, among other data, user profiles 222, training datasets 224, machine learning models 226, and workout programs 228 [and the storage unit stores the trained model in which,].”).
when the target person information and the exercise evaluation information are input, the evaluation of the exercise information of the target person is estimated to be improved, and instruction content based on the age information and the environment information is output. (Asikainen, ⁋90, “The recommendation engine 210 receives one or more of the 3D pose data, the exercise movements performed, the quality of exercise movements, the repetitions of the exercise movements, [and the exercise evaluation information are input,] the vital signs and health status signals, performance data, object detection data, user profile [and the environment information…], and other analyzed user data from the feedback engine 208 and the data processing engine 204 to compare a pattern of the user's workout with an aggregate user dataset (collected from multiple users) to identify a community of users with common characteristics. For example, the common characteristics may include an age group […on the age information], gender, weight, height, fitness preference, similar performance and workout patterns [when the target person information].” and Asikainen, ⁋92, “The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user [evaluation of exercise information of the target person is estimated to be improved,]. For example, the recommendation engine 210 may recommend to the user to push for As Many Repetitions As Possible (AMRAP) in the last set of an exercise movement if the level of fatigue of the user is low [and instruction content…is output.]”).
While Asikainen teaches a system for recommending exercise improvements for individuals, Asikainen does not explicitly teach …and a family structure of the target person….
Pauley teaches …and a family structure of the target person… (Pauley, ⁋222, “Family and medical history may be used to encourage, monitor, and calculate the health risks associated with a user's family and medical history. For example, a user's family records may show that members of the user's immediate family developed type 2 diabetes. Family history of type 2 diabetes is one factor that increases risk of an individual developing type 2 diabetes. The system may utilize this information to calculate a health score based on that family history and transmit that health score to the user […and a family structure of the target person…].”).
Asikainen and Pauley are both in the same field of endeavor (i.e. personalized health improvement). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Asikainen and Pauley to teach the above limitation(s). The motivation for doing so is that knowing family health history aids in a system’s understanding of the user’s health (cf. Pauley, ⁋222, “Family and medical history may be used to encourage, monitor, and calculate the health risks associated with a user's family and medical history.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Togawa, US20250132001A1 discloses a machine learning system that uses action states to control which exercise actions that improves a person’s health.
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/N.S.W./Examiner, Art Unit 2148
/SHERROD L KEATON/Primary Examiner, Art Unit 2148
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