DETAILED ACTION
Claims 1-12 are presented for examination.
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 .
Claim Rejections - 35 USC § 112
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 1-10 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.
Independent claim 1 recites that the method is “to be executed by a computer” in the preamble. It is not clear if all of the steps recited in the body of the claims are meant to be performed by a computer or only the steps that are explicitly recited as performed by the computer since “to be executed” implies actions that are performed in the future and method claims are defined by positively recited steps. While the steps in the claims are positively recited, the use of the computer to execute each step is not clearly and explicitly presented as performed within the scope of claim 1 and dependent claims 2-10. Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to tracking user activity, evaluating the degree to which goals have been met, and outputting information to the user to improve performance of the activity, without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-10), Apparatus (claims 11, 12)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 11, 12] An activity support method, the method comprising:
obtaining a first goal of a first user in an activity;
obtaining indicator values of the first user, the indicator values indicating an actual result of the activity;
identifying a related indicator from the indicator values, the related indicator changing along with a goal-related parameter that corresponds to the first goal; and
outputting information for the first user to improve the related indicator in performing the activity.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations cited above. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to tracking user activity, evaluating the degree to which goals have been met, and outputting information to the user to improve performance of the activity, which (under its broadest reasonable interpretation) is an example of monitoring activities of people and providing instructions to people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 recites in the preamble that the method is to be executed by a computer. Unless a step is explicitly recited as performed by the computer in the body of the claim, the computer is not necessarily actively used to perform each of the steps of the method. Consequently, claim 1 does not necessarily perform any of the recited steps of the method with the computer and, thus, claim 1 is directed to the abstract ideas per se.
Claim 11 incorporates an activity support device comprising a processor to perform the recited operations.
Claim 12 incorporates an activity support system comprising: a service device that includes a processor; and an electronic device that is communicably connected to the server device over a network; wherein the processor performs the recited operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 6-18).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model (e.g., as recited in claim 7) is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 29-30, 67). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] determines the related indicator, based on a relation between the goal-related parameter and indicator values of second users who set a second goal similar to the first goal and who have a characteristic corresponding to a characteristic of the first user.
[Claim 3] wherein:
the second users include a third user who achieved the second goal and a fourth user who did not achieve the second goal, and
determines an indicator that satisfies following (i) and (ii) to be the related indicator:
(i) in indicator values of the third user, the indicator correlates with the goal-related parameter by a degree greater than a first reference, and
(ii) in indicator values of the fourth user, the indicator has an improvement rate less than a goal achievement rate of the goal-related parameter and less than a second reference.
[Claim 4] wherein the second users having the characteristic corresponding to the characteristic of the first user meet a first requirement that the second users have a physical characteristic within a first reference range from a physical characteristic of the first user.
[Claim 5] wherein the second users having the characteristic corresponding to the characteristic of the first user meet a second requirement that the second users have a behavioral tendency within a second reference range from a behavioral tendency of the first user.
[Claim 6] wherein:
based on a correlation between the goal-related parameter and the related indicator of the second users, obtains an estimated value of the related indicator that changes as the first user performs the activity, and
based on a difference between the estimated value and an actual value of the related indicator obtained from the activity by the first user, outputs the information for improving the related indicator.
[Claim 7] obtains the estimated value of the related indicator by using a model that is defined based on the correlation and that outputs an estimated value of the related indicator to be obtained next in response to input of time-series data of the related indicator obtained multiple times.
[Claim 8] providing a capability of obtaining multiple levels of the first achievement goal, and
determines the related indicator for each of the multiple levels of the first goal.
[Claim 9] wherein the activity includes studying aimed at improving academic ability.
[Claim 10] obtains the indicator values by referring to history information stored.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations cited above. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to tracking user activity, evaluating the degree to which goals have been met, and outputting information to the user to improve performance of the activity, which (under its broadest reasonable interpretation) is an example of monitoring activities of people and providing instructions to people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 1 recites in the preamble that the method is to be executed by a computer. Unless a step is explicitly recited as performed by the computer in the body of the claim, the computer is not necessarily actively used to perform each of the steps of the method. Consequently, claim 1 does not necessarily perform any of the recited steps of the method with the computer and, thus, claim 1 is directed to the abstract ideas per se.
Dependent claim 9 also fails to explicitly recite use of a computer to perform a step of the method; therefore, claim 9 is also directed to the abstract ideas per se.
Claim 2 recites wherein the computer determines the related indicator.
Claim 3 recites that the computer determines an indicator that satisfies following (i) and (ii) to be the related indicator.
Claim 6 recites that the computer obtains an estimated value of the related indicator and the computer outputs the information for improving the related indicator.
Claim 7 recites wherein the computer obtains the estimated value of the related indicator by using a machine learning model that is trained based on the correlation and that outputs an estimated value of the related indicator to be obtained next in response to input of time-series data of the related indicator obtained multiple times.
Claim 8 recites wherein:
the computer is capable of obtaining multiple levels of the first achievement goal, and
the computer determines the related indicator for each of the multiple levels of the first goal.
Claim 10 recites wherein the computer obtains the indicator values by referring to history information stored in a storage or a database device.
Claim 11 incorporates an activity support device comprising a processor to perform the recited operations.
Claim 12 incorporates an activity support system comprising: a service device that includes a processor; and an electronic device that is communicably connected to the server device over a network; wherein the processor performs the recited operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 6-18).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model (e.g., as recited in claim 7) is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 29-30, 67). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are 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 and 9-12 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Bissonnette et al. (US 2022/0047921).
[Claim 1] Bissonnette discloses an activity support method to be executed by a computer (Abstract; Fig. 1; ¶¶ 127, 132-134, 255-260), the method comprising:
obtaining a first goal of a first user in an activity (Fig. 34 – A goal can be selected.; Fig. 41D, ¶ 276 – Goals; Fig. 52, ¶¶ 351-353 – Domains are associated with goals; ¶ 121 – “In one example, a grandparent may desire to play with their grandchildren, and may want to select that physical activity as a goal.”; ¶ 122 – “Accordingly, some embodiments of the present disclosure provide a technical solution for enabling a user to select one or more physical activity goals they desire to achieve and for generating an improved exercise plan that enables the user to achieve the one or more physical activity goals. The system may use an artificial intelligence engine to generate machine learning models that use one or more curated, multi-disciplinary data sources to generate the improved exercise plan. A given data source may include associations between the selected physical activity goal and one or more levels of attainment pertaining to achieving the physical life goal, associations between the one or more levels of attainment and one or more body portions, and associations between the one or more body portions and one or more exercises that target the one or more body portions. Using the data source, the artificial intelligence engine may generate a machine learning model to use the associations to generate improved exercise plans. Further, a machine learning model may be trained to predict a length of time it will take a user, if they follow the improved exercise plan, to achieve their physical activity life goal.”; ¶ 124 – “In some embodiments, numerous enhanced user interfaces may be used to enable the user to create a profile, select physical activity goals, view generated improved exercise plans, perform exercises, view/listen to multimedia regarding the exercises, provider user-reported feedback, view comorbidity information, view evidential trails for the comorbidity information and the exercise plans, control the exercise machine, and the like.”; ¶ 100 – “The target thresholds may be an osteogenesis target threshold, a muscular strength target threshold, and/or a rehabilitation threshold. The osteogenesis target threshold may be determined based on a disease protocol pertaining to the user, an age of the user, a gender of the user, a sex of the user, a height of the user, a weight of the user, a bone density of the user, etc. A disease protocol may refer to any illness, disease, fracture, or ailment experienced by the user and any treatment instructions provided by a caretaker for recovery and/or healing. The disease protocol may also include a condition of health where the goal is avoid a problem.”);
obtaining indicator values of the first user, the indicator values indicating an actual result of the activity (¶ 123 – “The levels of attainment may be objectively monitored and/or measured using various performance measurements from one or more sensors, characteristics of users of the exercise machine, user-reported difficulty levels of exercises, user-reported pain levels, and the like. An onboarding protocol may be used to establish a baseline describing a fitness level of the user, and the fitness level of the user, in the improved exercise plan, may be used to select difficulty levels of exercises. A machine learning model may be trained to perform the onboarding protocol and to determine the fitness level of the user. The improved exercise plan may be dynamically updated based on characteristics of the user, selected physical activity levels, performance measurements, user-reported difficulties of the exercises, user-reported pain levels, and the like. In some embodiments, to comply with the exercise plan, the exercise machine may be controlled using a signal that indicates changing an attribute of an operating parameter of the exercise machine. The control system may change the attribute of the operating parameter in response to receiving the signal.”);
identifying a related indicator from the indicator values, the related indicator changing along with a goal-related parameter that corresponds to the first goal (Fig. 52, ¶¶ 351-353 – Domains are associated with goals. ¶ 353 states, “As depicted, the user may select (e.g., via hand cursor), a particular graphical element 5204 associated with a domain to drill down to view more detailed information pertaining to that domain (e.g., Range of Motion). Accordingly, FIG. 53 illustrates an example user interface 5300 presenting detailed information of a domain selected from FIG. 52. The user interface 5300 is presented by the application 17 on the computing device 12. As depicted, additional information 5302 may include “Range of Motion (ROM) Details”. The details may include “Beginning ROM: X,” “Current ROM: Y,” and “Projected Amount of Time Before Target ROM: 1 week.” Accordingly, the user may be presented with information indicating an amount of progress the user has made with regard to the selected domain. A machine learning model 60 may be trained to analyze the progress the user has made, and based on the analysis, project an amount of time before a target ROM is achieved.”); and
outputting information for the first user to improve the related indicator in performing the activity (¶ 353 -- “As depicted, the user may select (e.g., via hand cursor), a particular graphical element 5204 associated with a domain to drill down to view more detailed information pertaining to that domain (e.g., Range of Motion). Accordingly, FIG. 53 illustrates an example user interface 5300 presenting detailed information of a domain selected from FIG. 52. The user interface 5300 is presented by the application 17 on the computing device 12. As depicted, additional information 5302 may include “Range of Motion (ROM) Details”. The details may include “Beginning ROM: X,” “Current ROM: Y,” and “Projected Amount of Time Before Target ROM: 1 week.” Accordingly, the user may be presented with information indicating an amount of progress the user has made with regard to the selected domain. A machine learning model 60 may be trained to analyze the progress the user has made, and based on the analysis, project an amount of time before a target ROM is achieved.”; Fig. 25 outputs acknowledgement related to progress toward a goal(s).).
[Claim 9] Bissonnette discloses wherein the activity includes studying aimed at improving academic ability (¶ 121 – “A user may lack the proper knowledge, training, and/or education to determine which exercises to perform to target appropriate body portions used, for example, to achieve the appropriate levels of attainment to be able to play with their grandchildren. Further, another problem that users may experience is the ability to determine when the user may be at risk for having or developing various comorbitities in a real-time or near real-time manner. Such knowledge may be useful for a user to prevent the comorbidity from arising and/or to encourage or suggest to a user to consult with a health professional to take preventative care measures.”; ¶ 267 – “The multimedia segment may include video and/or audio of a coaching character providing instructions and guidance on how to perform the first exercise.”; ¶ 268 – “The new multimedia segment may include video and/or audio of a coaching character providing information and guidance to the user pertaining how to perform the second exercise 3602. The second exercise 3602 may be selected as a result of the user indicating the previous exercise was too hard or too easy.”).
[Claim 10] Bissonnette discloses wherein the computer obtains the indicator values by referring to history information stored in a storage or a database device (¶ 100 – “The muscular strength target threshold may be determined based on a historical performance of the user using the exercise machine (e.g., amount of pounds lifted for a particular exercise, amount of force applied associated with each body part, etc.) and/or other exercise machines, a fitness level (e.g., how active the user is) of the user, a diet of the user, a protocol for determining a muscular strength target, etc. The rehabilitation target threshold may be determined based on historical performance of the user using the exercise machine (e.g., amount of force applied associated with each body part, speed of cycling, level of stability, etc.) and/or other exercise machines, a fitness level (e.g., how active the user is, the flexibility of the user, etc.) of the user, a diet of the user, an exercise plan for determining a rehabilitation target, the condition of the user (e.g., type of surgery the user underwent, the type of injury the user sustained), physical characteristics of the user (e.g., an age of the user, a gender of the user, a sex of the user, a height of the user, a weight of the user, a bone density of the user), condition of the user's body part(s) (e.g., the pain level of a user), an exertion level of a user (e.g., how easy/hard the exercise session is for the user), any other suitable characteristic, or combination thereof.”; ¶ 138 – “The cloud-based computing system may include a data source 67 that stores the training data for the training engine 50 and/or the artificial intelligence engine 65 to use to train the one or more machine learning models 60. The data source may include exercises, physical activity goals, levels of attainment, body portions targeted by exercises, weights and/or parameters used to configure a prioritization of certain levels of attainment throughout an exercise schedule, comorbidity information, health-related information, audio segments, video segments, motivational quotations, and so forth. The data source 67 may include various tags and/or keys (e.g., primary, foreign, etc.) to associate items of the data with each other in the data source 67. The data source 67 may be a relational database, a pivot table, or any suitable type of data structure configured to store data used for any of the operations described herein.”).
[Claim 11] Claim 11 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Bissonnette discloses an activity support device comprising a processor, wherein the processor performs the recited operations (Fig. 1; ¶¶ 255-260).
[Claim 12] Claim 12 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Bissonnette discloses an activity support system comprising: a server device that includes a processor; and an electronic device that is communicably connected to the server device over a network, wherein the processor performs the recited operations (Abstract; Fig. 1; ¶¶ 127, 132-134, 255-260).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-8 are rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette et al. (US 2022/0047921), as applied to claim 1 above, in view of Bax et al. (US 2021/0182700).
[Claim 2] Bissonnette compares people to others with similar goals (Bissonnette: Fig. 37; ¶ 269 – “The exercise plan 3702 indicates “People with similar characteristics (user fitness level) as you are able to play with their grandchildren within 6 weeks by following this exercise plan.”). Bissonnette does not explicitly disclose wherein the computer determines the related indicator, based on a relation between the goal-related parameter and indicator values of second users who set a second goal similar to the first goal and who have a characteristic corresponding to a characteristic of the first user.
Bax tracks users and compares them to other users in clusters of users with similar goals, similar ages, similar progress, etc. (Bax: ¶ 70 – “FIG. 6 illustrates a system 600 for content item selection for goal achievement. A content recommendation component may be configured to train a model 616 for selecting content items to provide to users based upon the content items having relatively larger likelihoods of being causation factors for the users to make progress towards their goals. The content recommendation component may be configured to generate clusters, such as a first cluster 602, a second cluster 604, a third cluster 606, and/or other clusters. The generation recommendation component may generate the clusters based upon at least one of user characteristics/information of users (e.g., create a cluster of similar users, such as users with similar ages, locations, occupations, etc.), goals of users (e.g., create a cluster of users with similar goals such as users that have a weight loss goal over winter break), goal progress (e.g., create a cluster of users that made similar progress towards a goal), and/or content items provided to users (e.g., create a cluster of users provided with the same content item in order to inspire/motivate the users to make progress towards a goal). Any number of these factors or combinations thereof may be used to cluster users.”). This information is used to improve content recommendations and increase the likelihood that users will reach their respective goals (Bax: ¶ 47 – “Information about users, goals of each user, what content items have been provided to what users, and goal progress of each users may be tracked and/or used to cluster similar users with similar goals. This information is also used to train a model used by the content recommendation component to select content items to provide to users with the objective of increasing a likelihood that the users will make progress towards their goals based upon the users consuming the content items.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein the computer determines the related indicator, based on a relation between the goal-related parameter and indicator values of second users who set a second goal similar to the first goal and who have a characteristic corresponding to a characteristic of the first user in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47).
[Claim 3] Bissonnette does not explicitly disclose wherein:
the second users include a third user who achieved the second goal and a fourth user who did not achieve the second goal, and
the computer determines an indicator that satisfies following (i) and (ii) to be the related indicator:
(i) in indicator values of the third user, the indicator correlates with the goal-related parameter by a degree greater than a first reference, and
(ii) in indicator values of the fourth user, the indicator has an improvement rate less than a goal achievement rate of the goal-related parameter and less than a second reference.
Bax tracks users and compares them to other users in clusters of users with similar goals, similar ages, similar progress, etc. (Bax: ¶ 70 – “FIG. 6 illustrates a system 600 for content item selection for goal achievement. A content recommendation component may be configured to train a model 616 for selecting content items to provide to users based upon the content items having relatively larger likelihoods of being causation factors for the users to make progress towards their goals. The content recommendation component may be configured to generate clusters, such as a first cluster 602, a second cluster 604, a third cluster 606, and/or other clusters. The generation recommendation component may generate the clusters based upon at least one of user characteristics/information of users (e.g., create a cluster of similar users, such as users with similar ages, locations, occupations, etc.), goals of users (e.g., create a cluster of users with similar goals such as users that have a weight loss goal over winter break), goal progress (e.g., create a cluster of users that made similar progress towards a goal), and/or content items provided to users (e.g., create a cluster of users provided with the same content item in order to inspire/motivate the users to make progress towards a goal). Any number of these factors or combinations thereof may be used to cluster users.”). This information is used to improve content recommendations and increase the likelihood that users will reach their respective goals (Bax: ¶ 47 – “Information about users, goals of each user, what content items have been provided to what users, and goal progress of each users may be tracked and/or used to cluster similar users with similar goals. This information is also used to train a model used by the content recommendation component to select content items to provide to users with the objective of increasing a likelihood that the users will make progress towards their goals based upon the users consuming the content items.”). Users may also set a respective risk tolerance for failing to achieve a set goal (Bax: ¶ 63 – “In an embodiment, a risk tolerance of the user is taken into account when selecting the target content item. For example, the user may have a risk tolerance for an outcome of failing to achieve the goal (e.g., if the goal is to obtain a raise at work, then the user may have a 10% risk tolerance of losing a job from asking for the raise in relation to obtaining a $5 k raise). Accordingly, the risk tolerance is utilized for selecting the target content item.”).
Correlations among content items, goal progress, and clusters of users are evaluated and such analysis includes the identification of causation factors that are above a threshold of predictive strength (Bax: ¶ 71 – “As part of training the model 616, the content recommendation component performs statistical filtering 610 for content items to identify content items that correlate to goal progress for groups of users. For example, the content recommendation component may evaluate the first cluster 602 to determine if users, provided with a particular content item, made progress towards a weight loss goal. If the statistical filter 610 determines that the user generally make progress towards their weight loss goals after being provided with the content item, then the content item and the weight loss goal may be correlated together.”). Statistical inferences may be made when evaluating the correlations (Bax: ¶ 72 – “The A/B testing 612 may be performed to track whether a first set of users provided with the content item made progress towards the weight loss goal and whether a second set of users not provided with the content item made progress towards the weight loss goal. If a proportionally greater number of users within the first set of users made progress towards the weight loss goal than users within the second set of users that made progress towards the weight loss goal, then the content item may be determined to be a causation factor for the users making progress towards the weight loss goal. Otherwise, the content item may not be determined to be the causation factor. In this way, the content recommendation component may create statistical inferences 614 of which content items will cause certain types of users to make progress towards particular goals. The statistical inferences 614 are used by the content recommendation component to train the model 616.”; ¶ 74 – “The content recommendation component 706 may utilize a model (e.g., model 616 of FIG. 6) to evaluate the health goal 708 of gaining 10 lbs of muscle mass over the summer, the user information 710, and a set of content items 712 (e.g., images, videos, links to websites, recommendations, text, etc.) to generate predictions for the content items of how likely each content item will be a causation factor of the user making progress towards the health goal 708 in response to the user being provided with each content item. For example, the model may output an increase in probability that the user will make progress towards the health goal 708 if provided with an Olympic recap video 704 than a probability that the user makes progress towards the health goal 708 without being provided with the Olympic recap video 704. The increase in probability may relate to how strong of a causation factor the Olympic recap video 704 is towards inspiring/motivating the user to make progress towards the health goal 708, which may be utilized as a ranking factor. In response to the Olympic recap video 704 having a predicted likelihood of being the causation factor above a threshold (e.g., a larger predicted likelihood/ranking factor than other content items), the content recommendation component 706 displays the Olympic recap video 704 through a social network feed of the social network profile being accessed by the user.”).
In other words, Bax’s disclosure matches users to multiple clusters of multiple users to find the most similar cluster that would likely be predictive of how each matched user would be most likely to make progress to reach their respective goals, including an evaluation of the best informational content to target to each user. Additionally, both Bissonnette and Bax describe at least one possible common goal as being driven by a time period to achieve the goal (e.g., Bissonnette: Fig. 37; ¶ 269 – “The exercise plan 3702 indicates “People with similar characteristics (user fitness level) as you are able to play with their grandchildren within 6 weeks by following this exercise plan.”; Bax: ¶ 50 – “The user may further define the selected goal (e.g., specify the name of the videogame, a score threshold, a time duration within which the score threshold should be reached such as within a month, and/or other parameters of the goal). The user may break the goal down into multiple steps, such as a first sub-goal step to save money to buy the videogame, a second sub-goal step to install and practice at the videogame, and a third sub-goal step to beat a particular score in the videogame.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein:
the second users include a third user who achieved the second goal and a fourth user who did not achieve the second goal, and
the computer determines an indicator that satisfies following (i) and (ii) to be the related indicator:
(i) in indicator values of the third user, the indicator correlates with the goal-related parameter by a degree greater than a first reference, and
(ii) in indicator values of the fourth user, the indicator has an improvement rate less than a goal achievement rate of the goal-related parameter and less than a second reference
in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47).
[Claim 4] Bissonnette does not explicitly disclose wherein the second users having the characteristic corresponding to the characteristic of the first user meet a first requirement that the second users have a physical characteristic within a first reference range from a physical characteristic of the first user. Bax discloses that “similar ages” (implying a range of ages) may be used to cluster users (Bax: ¶ 70 – “The generation recommendation component may generate the clusters based upon at least one of user characteristics/information of users (e.g., create a cluster of similar users, such as users with similar ages, locations, occupations, etc.), goals of users (e.g., create a cluster of users with similar goals such as users that have a weight loss goal over winter break), goal progress (e.g., create a cluster of users that made similar progress towards a goal), and/or content items provided to users (e.g., create a cluster of users provided with the same content item in order to inspire/motivate the users to make progress towards a goal). Any number of these factors or combinations thereof may be used to cluster users.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein the second users having the characteristic corresponding to the characteristic of the first user meet a first requirement that the second users have a physical characteristic within a first reference range from a physical characteristic of the first user in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47).
[Claim 5] Bissonnette does not explicitly disclose wherein the second users having the characteristic corresponding to the characteristic of the first user meet a second requirement that the second users have a behavioral tendency within a second reference range from a behavioral tendency of the first user. Bax discloses that a user’s personal hobbies may be used to target information to the user (Bax: ¶ 64 – “Subsequent to providing the target content item to the user, a follow-up recommendation of an activity associated with the target content item and/or the goal of the user may be provided (e.g., if the target content item comprised the motivational speech about personal hobbies, then the recommendation may recommend an activity of starting another hobby that was discussed in the motivational speech).”), thereby suggesting that similar users might have similar behavioral tendencies, like having common interests in personal hobbies.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein the second users having the characteristic corresponding to the characteristic of the first user meet a second requirement that the second users have a behavioral tendency within a second reference range from a behavioral tendency of the first user in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47).
[Claim 6] Bissonnette does not explicitly disclose wherein:
based on a correlation between the goal-related parameter and the related indicator of the second users, the computer obtains an estimated value of the related indicator that changes as the first user performs the activity, and
based on a difference between the estimated value and an actual value of the related indicator obtained from the activity by the first user, the computer outputs the information for improving the related indicator.
Bax tracks users and compares them to other users in clusters of users with similar goals, similar ages, similar progress, etc. (Bax: ¶ 70 – “FIG. 6 illustrates a system 600 for content item selection for goal achievement. A content recommendation component may be configured to train a model 616 for selecting content items to provide to users based upon the content items having relatively larger likelihoods of being causation factors for the users to make progress towards their goals. The content recommendation component may be configured to generate clusters, such as a first cluster 602, a second cluster 604, a third cluster 606, and/or other clusters. The generation recommendation component may generate the clusters based upon at least one of user characteristics/information of users (e.g., create a cluster of similar users, such as users with similar ages, locations, occupations, etc.), goals of users (e.g., create a cluster of users with similar goals such as users that have a weight loss goal over winter break), goal progress (e.g., create a cluster of users that made similar progress towards a goal), and/or content items provided to users (e.g., create a cluster of users provided with the same content item in order to inspire/motivate the users to make progress towards a goal). Any number of these factors or combinations thereof may be used to cluster users.”). This information is used to improve content recommendations and increase the likelihood that users will reach their respective goals (Bax: ¶ 47 – “Information about users, goals of each user, what content items have been provided to what users, and goal progress of each users may be tracked and/or used to cluster similar users with similar goals. This information is also used to train a model used by the content recommendation component to select content items to provide to users with the objective of increasing a likelihood that the users will make progress towards their goals based upon the users consuming the content items.”). Users may also set a respective risk tolerance for failing to achieve a set goal (Bax: ¶ 63 – “In an embodiment, a risk tolerance of the user is taken into account when selecting the target content item. For example, the user may have a risk tolerance for an outcome of failing to achieve the goal (e.g., if the goal is to obtain a raise at work, then the user may have a 10% risk tolerance of losing a job from asking for the raise in relation to obtaining a $5 k raise). Accordingly, the risk tolerance is utilized for selecting the target content item.”).
Correlations among content items, goal progress, and clusters of users are evaluated and such analysis includes the identification of causation factors that are above a threshold of predictive strength (Bax: ¶ 71 – “As part of training the model 616, the content recommendation component performs statistical filtering 610 for content items to identify content items that correlate to goal progress for groups of users. For example, the content recommendation component may evaluate the first cluster 602 to determine if users, provided with a particular content item, made progress towards a weight loss goal. If the statistical filter 610 determines that the user generally make progress towards their weight loss goals after being provided with the content item, then the content item and the weight loss goal may be correlated together.”). Statistical inferences may be made when evaluating the correlations (Bax: ¶ 72 – “The A/B testing 612 may be performed to track whether a first set of users provided with the content item made progress towards the weight loss goal and whether a second set of users not provided with the content item made progress towards the weight loss goal. If a proportionally greater number of users within the first set of users made progress towards the weight loss goal than users within the second set of users that made progress towards the weight loss goal, then the content item may be determined to be a causation factor for the users making progress towards the weight loss goal. Otherwise, the content item may not be determined to be the causation factor. In this way, the content recommendation component may create statistical inferences 614 of which content items will cause certain types of users to make progress towards particular goals. The statistical inferences 614 are used by the content recommendation component to train the model 616.”; ¶ 74 – “The content recommendation component 706 may utilize a model (e.g., model 616 of FIG. 6) to evaluate the health goal 708 of gaining 10 lbs of muscle mass over the summer, the user information 710, and a set of content items 712 (e.g., images, videos, links to websites, recommendations, text, etc.) to generate predictions for the content items of how likely each content item will be a causation factor of the user making progress towards the health goal 708 in response to the user being provided with each content item. For example, the model may output an increase in probability that the user will make progress towards the health goal 708 if provided with an Olympic recap video 704 than a probability that the user makes progress towards the health goal 708 without being provided with the Olympic recap video 704. The increase in probability may relate to how strong of a causation factor the Olympic recap video 704 is towards inspiring/motivating the user to make progress towards the health goal 708, which may be utilized as a ranking factor. In response to the Olympic recap video 704 having a predicted likelihood of being the causation factor above a threshold (e.g., a larger predicted likelihood/ranking factor than other content items), the content recommendation component 706 displays the Olympic recap video 704 through a social network feed of the social network profile being accessed by the user.”).
In other words, Bax’s disclosure matches users to multiple clusters of multiple users to find the most similar cluster that would likely be predictive of how each matched user would be most likely to make progress to reach their respective goals, including an evaluation of the best informational content to target to each user. Additionally, both Bissonnette and Bax describe at least one possible common goal as being driven by a time period to achieve the goal (e.g., Bissonnette: Fig. 37; ¶ 269 – “The exercise plan 3702 indicates “People with similar characteristics (user fitness level) as you are able to play with their grandchildren within 6 weeks by following this exercise plan.”; Bax: ¶ 50 – “The user may further define the selected goal (e.g., specify the name of the videogame, a score threshold, a time duration within which the score threshold should be reached such as within a month, and/or other parameters of the goal). The user may break the goal down into multiple steps, such as a first sub-goal step to save money to buy the videogame, a second sub-goal step to install and practice at the videogame, and a third sub-goal step to beat a particular score in the videogame.”). The similarity of a particular user to each of multiple clusters implies some evaluation of predicted behavior being most/least similar and/or least/most different between the particular user and each of the clusters.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein:
based on a correlation between the goal-related parameter and the related indicator of the second users, the computer obtains an estimated value of the related indicator that changes as the first user performs the activity, and
based on a difference between the estimated value and an actual value of the related indicator obtained from the activity by the first user, the computer outputs the information for improving the related indicator
in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47).
[Claim 7] Bissonnette does not explicitly disclose wherein the computer obtains the estimated value of the related indicator by using a machine learning model that is trained based on the correlation and that outputs an estimated value of the related indicator to be obtained next in response to input of time-series data of the related indicator obtained multiple times. Bax allows for a goal to be broken down into a series of multiple steps and/or sub-goals (Bax: ¶ 50 -- The user may break the goal down into multiple steps, such as a first sub-goal step to save money to buy the videogame, a second sub-goal step to install and practice at the videogame, and a third sub-goal step to beat a particular score in the videogame.”), which is suggestive of a time-series of data. Bax trains its models to facilitate goal progress for users (Bax: ¶ 7). Bissonnette explicitly uses machine learning for its models (Bissonnette: Abstract; ¶¶ 122-123).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein the computer obtains the estimated value of the related indicator by using a machine learning model that is trained based on the correlation and that outputs an estimated value of the related indicator to be obtained next in response to input of time-series data of the related indicator obtained multiple times in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47), including by allowing for larger goals to be broken up into a sequence of more manageable sub-goals.
[Claim 8] Bissonnette does not explicitly disclose wherein:
the computer is capable of obtaining multiple levels of the first achievement goal, and
the computer determines the related indicator for each of the multiple levels of the first goal.
Bax identifies multiple levels of progress made toward a common goal (Bax: ¶ 59 – “Accordingly, causality testing, such as A/B testing, is performed to train the model on not just correlations but also on causation. In particular, causality testing may be performed for a content item by tracking whether a first set of users provided with the content item made progress towards the goal and whether a second set of users not provided with the content item made progress towards the goal. If a threshold amount of users within the second set of users made progress towards the goal (e.g., or if certain percentage of users within the second set of users made progress towards the goal in relation to a percentage of users within the first set of users), then the model may be trained to understand that the content item may not be a causation factor for users to make progress towards the goal (e.g., parameters and/or functions used by the model to select the content item may be discounted, such as by having a relative weight of importance decreased to indicate that the parameters and/or functions are relatively less accurate). In this way, the model may be trained to identify content items that cause users to make progress towards certain goals.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Bissonnette wherein:
the computer is capable of obtaining multiple levels of the first achievement goal, and
the computer determines the related indicator for each of the multiple levels of the first goal
in order to improve content recommendations and increase the likelihood that users will reach their respective goals (as suggested in Bax: ¶ 47) as well as to more accurately train the model (as suggested in ¶ 59 of Bax).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ketchell, III et al. (US 11,331,537) – Makes recommendations to optimize athletic performance.
Rauhala et al. (US 2016/0151674) – Recommends workouts for a user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights