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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgement is made of applicant’s claim for foreign priority to 30 June 2021 under 35 U.S.C. 119(a)-(d).
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06 March 2026 has been entered.
Response to Amendment
Claims 1-9 were previously pending in this application. The amendment filed 06 March 2026 has been entered and the following has occurred: Claims 1 & 7-8 have been amended. No claims have been cancelled or added.
Claims 1-9 remain pending in the application
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claims recite subject matter within a statutory category as a process (claim 8), machine (claims 1-6 & 7), and manufacture (claim 9) (Subject Matter Eligibility (SME) Test Step 1: Yes) which recite steps of:
detect a frequency with which the user views a health management application installed on the portable terminal based on a viewing history of the health management application;
obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history;
rearrange the dish list based on the frequency calculated based on the viewing history;
display the rearranged dish list and receive a selection of an eaten dish from the user;
generate food consumption information according to the selection;
transmit the frequency and the food consumption information to the server;
obtain a plurality of first stages of behavior change by evaluating a stage of behavior change of each of a plurality of first users, wherein the stage of behavior change is determined based on the frequency and the food consumption information;
classify the plurality of first users into a plurality of groups according to the plurality of first stages of behavior change, grouping users evaluated to be in similar stages of behavior change together;
obtain one or more first behaviors of each of one or more second users belonging to a first group among the plurality of groups, and extracts, from among the one or more first behaviors obtained, a second behavior that affects transition to a higher stage of behavior change; and
notify one or more third users currently belonging to the first group among the one or more second users with behavior information indicating the second behavior, wherein the second behavior is determined as a frequent checking behavior of the health management application;
receive the behavior information;
present the second behavior based on the behavior information received.
These steps of detecting a frequency with which the user views a health management application installed on the portable terminal, obtaining a plurality of first stages of behavior change by evaluating a stage of behavior change for a plurality of first users, classifying the first users into a plurality of groups according to the behavior change, obtaining one or more first behaviors associated with from a plurality of second users in the one or more groups that affects transition to a higher stage of behavior change, and notifying one or more third users currently in the third group with behavior information indicating the second behavior, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the detecting a frequency with which the user views a health management application installed on the portable terminal or receiving various behaviors or behavior changes in one or more people/groups language, receiving various data and/or checking behaviors or behavior changes in the context of this claim encompasses a mental process of the user merely observing or recording various behaviors observed between one or more people or groups of people. Similarly, the limitation of determining one or more behaviors that affects a transition to a higher stage of behavior, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as a user determining that certain behaviors or tasks encourage transition from one behavior state to an improved or different behavior state. For example, but for the notify one or more users of the behavior information language, notifying one or more users of the behavior information in the context of this claim encompasses a mental process of the user relaying the information to one or more users regarding how to improve their behavior or change their behavior by performing one or more tasks or being in a particular behavior state for one or more periods of time. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps of detecting a frequency with which the user views a health management application installed on the portable terminal based on a viewing history, obtaining a dish list, rearranging the dish list based on the frequency calculated based on the viewing history, displaying the rearranged dish list and receiving a selection of an eaten dish, generating food consumption information according to the selection, obtaining a plurality of first stages of behavior change by evaluating a stage of behavior change for a plurality of first users, classifying the first users into a plurality of groups according to the behavior change, obtaining one or more first behaviors associated with from a plurality of second users in the one or more groups that affects transition to a higher stage of behavior change, and notifying one or more third users currently in the third group with behavior information indicating the second behavior, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity (MOHA). MPEP 2106.04(a)(2)(II) describes various MOHA, including concepts relating to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The steps identified above are substantially rooted in the concept of user behaviors, either affecting or mitigating certain user behaviors of those users, and recommending one or more tasks or behavior changes for achieving said affect or mitigation of user behaviors. Therefore, these steps heavily relate to managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) in order to mitigate or change user behaviors in one or more plurality of users. Therefore, under broadest reasonable interpretation, the steps recited fall within the MOHA grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-6 & 9, reciting particular aspects of how behavior observation, behavior recommendation, and/or determination of behavior patterns may be performed in the mind but for recitation of generic computer components) (SME Test Step 2A, Prong 1: Yes).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a portable terminal, a server, one or more memories, one or more processors, a non-transitory computer-readable recording medium, and a computer amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s spec [0109]-[0110] for a portable terminal; [0069] for a server; [0116] for one or more memories; [0206] for one or more processors; [0008] & [0064] for a non-transitory computer-readable recording medium; [0064] for a computer; see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of detecting a frequency with which the user views a health management application based on a viewing history of the application, obtaining a dish list including a plurality of dishes, receiving a selection of an eaten dish, obtaining a plurality of first stages of behavior change, obtaining/extracting one or more first behaviors of one or more second users from the plurality of groups that affects transition to a higher stage of behavior change, notifying one or more third users with the behavior information indicating the behavior affecting transition, receiving the behavior information amounts to mere data gathering; recitation of rearranging the dish list based on the frequency calculated based on the viewing history, generating food consumption information according to the received selection, evaluating a stage of behavior change to obtain stages of behavior change, the stage of behavior determined based on the detected frequency, and the second behavior is determined s a frequency checking behavior, i.e. based on the detected frequency, which amounts to selecting a particular data source or type of data to be manipulated; recitation of classifying the plurality of users into groups according to stages of behavior change, presenting the second behavior based on behavior information received amounts to insignificant application, see MPEP 2106.05(g); displaying an input screen for inputting food consumption history and displaying the rearranged dish list amounts to gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, see MPEP 2106.05(II));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of lifestyle improvement or dietary/nutritional recommendations related to lifestyle, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-6 & 9, which recite limitations relating to one or more computerized system and/or non-transitory computer-readable recording medium, additional limitations which amount to invoking computers as a tool to perform the abstract idea; claims 2-6 & 9, which recite limitations relating to receiving one or more behaviors of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 2-6 & 9, which recite limitations relating to electing or selecting one or more behaviors to be observed or extracted for one or more users/groups, electing or selecting one or more lifestyle improvements, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 2-6 & 9, which recite limitations relating to lifestyle improvement or support for lifestyle improvement, and as suggested by Applicant’s Specification, dietary/nutritional recommendations, additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application (SME Step 2A, Prong 2: No).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such detecting a frequency with which the user views a health management application based on a viewing history of the application, obtaining a dish list including a plurality of dishes, receiving a selection of an eaten dish, obtaining a plurality of first stages of behavior change, obtaining/extracting one or more first behaviors of one or more second users from the plurality of groups that affects transition to a higher stage of behavior change, notifying one or more third users with the behavior information indicating the behavior affecting transition, receiving the behavior information, receiving the behavior information amounts to mere data gathering, recitation of evaluating a stage of behavior change to obtain stages of behavior change, e.g. receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); rearranging the dish list based on the frequency calculated based on the viewing history, generating food consumption information according to the received selection, classifying the plurality of users into groups according to stages of behavior change, the stage of behavior determined based on the detected frequency, and the second behavior is determined as a frequency checking behavior, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more classifications of groups for classifying the behavior change of the first users into one or more groups, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing obtained behavior change data, storing one or more behavior data, storing one or more classifications of groups for classifying behavior changes, storing computerized instructions for performance of the steps recited on a computer system, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); obtaining/extracting behavior changes and/or behaviors of one or more users, which under BRI, includes extraction or scanning of a document for said obtained information, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v); displaying an input screen for inputting food consumption history, displaying the rearranged dish list, presenting second behavior based on the behavior information received, e.g., gathering and analyzing information using conventional techniques and displaying, i.e. presenting, the result, TLI Communications, MPEP 2106.05(a)(II) and/or presenting and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv); displaying an input screen for inputting food consumption history and receiving a selection of an eaten dish from the user, which under BRI, includes one or more button and/or selectable indicia functionality, e.g., a web browser' s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(ii)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-6 & 9, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2-6 & 9, which recite limitations relating to receiving one or more behaviors of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 2-6 & 9, which recite limitations relating to receiving one or more behaviors of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, which generally recite limitations relating to determination and/or classification of one or more behaviors or users into behavior groups, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 2-6 & 9, which recite limitations relating to receiving and maintaining one or more classifications of groups for classifying the behavior or one or more behaviors changes of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2-6 & 9, which recite limitations relating to receiving and storing one or more behaviors of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, storing computerized instructions, such as in a non-transitory computer-readable recording medium, for performance of the steps recited, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2-6 & 9, which recite limitations relating to receiving, e.g. extracting, one or more behaviors of one or more third users, behaviors that influenced one or more fourth users belonging to a second group, one or more stages of behavior change, which under BRI, includes extraction or scanning of a document for said obtained information, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation (SME Test Step 2B: No).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable by Sasaki et al. (U.S. Patent Publication No. 2020/0279633), hereinafter “Sasaki”, in view of Starson et al. (U.S. Patent Publication No. 2021/0365687), hereinafter “Starson”, further in view of Wexler et al. (U.S. Patent Publication No. 2021/0383925), hereinafter “Wexler”.
Claim 1 –
Regarding Claim 1, Sasaki discloses a lifestyle improvement system comprising:
a server (See Sasaki Par [0033] & [0064]-[0066] which discloses an example embodiment in the form of server provided to one or more providers and/or target users); and
a portable terminal of each of a plurality of first users (See Sasaki Par [0064]-[0065] which discloses the use of one or more terminals for one or more target users, such as an administrator terminal and/or a client terminal) comprising:
one or more first memories (See Sasaki Par [0027] which discloses the use of one or more memories); and
at least one first processor each coupled to at least one of the one or more first memories (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.) and configured to:
detect a frequency with which the user views a health management application installed on the portable terminal, based on a viewing history of the health management application (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s); See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior),
transmit the frequency to the server (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s));
the server comprising:
one or more second memories (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.); and
at least one second processor each coupled to at least one of the one or more second memories and configured to:
obtain a plurality of first stages of behavior change by evaluating a stage of behavior change of (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.) each of the plurality of first users (While Sasaki does not necessarily disclose an “obtainer”, “classifier”, “extractor”, “notifier”, “receiver”, and/or “presenter” per se, these are understood by Examiner to simply constitute various program modules and/or processing units for performing the actions recited, therefore see Sasaki Par [0030] which discloses one or more processors and/or processing devices for performing the embodiments described throughout Sasaki and is applied hereinafter for each recitation of “various program modules” to read on these components; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of a plurality of target persons), wherein
the stage of behavior change is determined based on the frequency and the food consumption information (See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, i.e. the data defined in Sasaki Par [0066], including various data obtained by measuring behaviors of a plurality of target persons, such as PC operation logs, which would therefore include a user using/ said health management application, i.e. the one or more personal computers to be used by the target person);
classify the plurality of first users into a plurality of groups according to the plurality of first stages of behavior change, grouping users evaluated to be in similar stages of behavior change together (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets, i.e. first users, belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups);
obtain one or more first behaviors of each of one or more second users belonging to a first group among the plurality of groups, and extracts, from among the one or more first behaviors obtained, a second behavior that affects transition to a higher stage of behavior change (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups; See Sasaki Par [0040]-[0041] which discloses identifying a stage gap, i.e. a gap between a characteristic of a lower/initial stage and a characteristic of a higher/different stage, and an associated gap reason/shift measure for the stage gap from relationship information for each stage pair, i.e. from one stage to another stage, such that a shift measure can be identified that indicates a measure of causing a target person belonging to a lower or initial stage to make a behavior change to shift to a higher or different stage; Sasaki Par [0052]-[0053] further specifically identifies shift measures and/or factors that are either inhibitory, i.e. does not encourage transition, or stimulatory, i.e. does encourage transition, to behavior change of one or more users); wherein
the second behavior is determined as a frequent checking behavior of the health management application (While Sasaki does not explicitly disclose a “frequent” checking behavior per se, it is understood by Examiner that Sasaki Par [0039] & [0066] do disclose defining a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of target persons, such as PC operation logs, which would therefore include a user using/checking said health management application, i.e. the one or more personal computers to be used by the target person for mitigating and/or learning new behavior, and therefore while Sasaki does not explicitly recite/anticipate a “frequent” checking behavior of the health management application, Sasaki Par [0039] & [0066] generally discloses and renders obvious a checking behavior of the health management application, via PC operation logs of a user using/checking a health management application, and as such, because it is understood that the checking behavior of the health management application defined in Sasaki would naturally occur at some frequency (i.e., without the “frequency” or “frequent checking” behavior being further defined in the instant set of claims, Sasaki generally renders said embodiment obvious);
notify one or more third users currently belonging to the first group among the one or more second users with behavior information indicating the second behavior on the portable terminal (See Sasaki Par [0100] & [0103]-[0104] which discloses a display section that performs display of the processing efforts related to a gap reason/shift measure identified for each stage pair, such that a user can visualize their behaviors and various gap reasons/shift measures to achieve behavioral change; See Sasaki Par [0047]-[0048] which discloses the number of target person groups that are classified into a stage may be larger than one or more people; See Sasaki Par [0122]-[0124] which discloses that each of one or more target persons, i.e. identified subset of “second users” that becomes the “third users”, from among a plurality of target users, i.e. second users, is assigned a shift measure applicable to the subset or “third users” from among shift measures identified by the reason/measure identification section with respect to a stage pair including a stage to which the target person(s) belongs and a next higher stage that the target person(s) are likely to transition given a certain quality or behavior of that target person(s)).
While Sasaki generally discloses receiving user inputs and/or logs regarding operational behavior for one or more devices, Sasaki is relatively silent on:
obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history;
rearrange the dish list based on the frequency calculated based on the viewing history;
display the arranged dish list and receive a selection of an eaten dish from the user;
generate food consumption information according to the selection;
transmit the frequency and the food consumption information to the server;
the stage of behavior change being based on food consumption information.
However, Starson discloses obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history (See Starson Par [0003]-[0004] & [0075]-[0076] which describes applications and tools utilizing manual entry of food and selecting from a list of suggestions, and further discloses while viewing the screen, the user can see useful data displayed next to or over recognized food items, and log their entire meal with a simple swipe or gesture, thereby constituting an input screen for inputting food consumption history); rearrange the dish list based on the frequency calculated based on the viewing history (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities); display the arranged dish list and receive a selection of an eaten dish from the user (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities; See Starson Par [0121] which discloses displaying said information for the user, such as on the touchscreen of the mobile device; See Starson Par [0004] & [0075]-[0076] which discloses the user logs identified foods consumed with a simple gesture, thereby constituting selection of an eaten dish); generate food consumption information according to the selection (See Starson Par [0120] which discloses the number of elements storing said each food identity are counted and then ranked to determine one or more most-viewed food identifies; See Starson Par [0121] which discloses that the one or more most-viewed food identities are outputted, such that the one or more most-viewed food identities, i.e. food consumption information, are added to a running, i.e. generated, list for display to a user based on selections made by the user in Starson Par [0004] & [0075]-[0076]); transmit the frequency and the food consumption information to the server (See Starson Par [0121] which discloses displaying information for the user, such as on the touchscreen of the mobile device; See Starson Par [0008] & [0152]-[0153] which discloses transmitting the images and selections, i.e. frequency thereof, and food consumption information to an external server that processes said information, and transmits the results back to the mobile device). The disclosure of Starson is directly applicable to the disclosure of Sasaki, because both disclosures share limitations and capabilities, such as being directed towards computerized logging of one or more user tendencies/behaviors, e.g. computer activity, food consumption, etc. over time.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Sasaki, which already discloses receiving user inputs and/or logs/logging regarding operational behavior for one or more devices, to further include obtaining a dish list, arranging said dish list according to viewing history, displaying the arranged dish list, generating food consumption information according to the selection, and transmitting these aspects to the server, as disclosed by Starson, because this simplifies the process of logging meals by making it faster, more engaging, and more reliable for users by eliminating from-scratch search and selection of meals and replacing said search and selection with an arranged list of most-viewed meals/foods for selection by the user based on the user’s tendencies (See Starson Par [0003]-[0004]).
While Sasaki and Starson effectively disclose the stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, Sasaki and Starson do not necessarily disclose:
the stage of behavior change being based on food consumption information.
However, Wexler discloses the stage of behavior change is determined based on the food consumption information (See Wexler Par [0092]-[0093] which discloses an adaptive support model based on performance or non-performance of a user’s behavior, estimating a new state of the user, an action taken by the software application for the user, or other factors, such that corrective actions can be issued for said user based on said new state that has the highest effectiveness in assisting the behavioral adjustments; See Wexler Par [0020] & [0061] which discloses that the behaviors can be any habit, routine choice, and can include increasing (or decreasing) consumption of a particular kind of food, meal, or nutrition data (and/or including input data relating to number of meals, timing of meals, nutritional information associated therewith, etc.), i.e. the behavioral state and changes determined in Wexler Par [0092]-[0093] are effectively based on the increasing (or decreasing) consumption of a particular kind or number of food, meals, or nutrition data, as disclosed in Wexler Par [0020] & [0061]). The disclosure of Wexler is directly applicable to the combined disclosure of Sasaki and Starson, because the disclosures share limitations and capabilities, such as being directed towards computerized logging of one or more user tendencies/behaviors, e.g. computer activity, food consumption, etc. over time.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Sasaki and Starson, which already discloses he stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, to further include the stage of behavior change is determined based on the food consumption information, as disclosed by Wexler, because food consumption data is relevant to a patient’s health state, and should therefore be considered/processed when determining a user’s health state/behaviors and adjustments/transitions thereof (See Wexler Par [0020] & [0092]-[0093]).
Claim 2 –
Regarding Claim 2, Sasaki, Starson, and Wexler disclose the lifestyle improvement system according to claim 1 in its entirety. Sasaki further discloses a system, wherein:
the one or more behaviors are behaviors of the one or more third users (See Sasaki Par [0100] & [0103]-[0104] which discloses a display section that performs display of the processing efforts related to a gap reason/shift measure identified for each stage pair, such that a user can visualize their behaviors and various gap reasons/shift measures to achieve behavioral change; See Sasaki Par [0047]-[0048] which discloses the number of target person groups that are classified into a stage may be larger than one or more people; See Sasaki Par [0122]-[0124] which discloses that each of one or more target persons, i.e. identified subset of “second users” that becomes the “third users”, from among a plurality of target users, i.e. second users, is assigned a shift measure applicable to the subset or “third users” from among shift measures identified by the reason/measure identification section with respect to a stage pair including a stage to which the target person(s) belongs and a next higher stage that the target person(s) are likely to transition given a certain quality or behavior of that target person(s)).
Claim 3 –
Regarding Claim 3, Sasaki, Starson, and Wexler disclose the lifestyle improvement system according to claim 1 in its entirety. Sasaki further discloses a system, wherein:
the one or more first behaviors are behaviors that influenced one or more fourth users currently belonging to a second group evaluated at a higher stage of behavior change relative to the first group to transition from the first group to the second group (See Sasaki Par [0046] which discloses one or more target person groups (clusters), e.g. five target person groups, which would therefore include a first and second group up to a fifth group; See Sasaki Par [0059]-[0061] which discloses identifying a cognitive bias for each of the stages by using a characteristic of each stage/target group as a key, such that inhibitory/stimulatory factors for each stage is also based on the other existing stages/target person groups in order to properly classify/tier the groups/stages sequentially; therefore, as understood by Examiner, in order to properly tier the stages/groups from a first stage/group to a fifth stage/group, each of the groups has to be considered and compared to one another to determine the proper order of transition when moving from group 1 to group 2 to group 3, and so on, and therefore, the one or more behaviors that influenced one or more users in a later or higher group are related and/or based on behaviors that influence one or more users in an earlier or lower group).
Claim 4 –
Regarding Claim 4, Sasaki, Starson, and Wexler disclose the lifestyle improvement system according to claim 1 in its entirety. Sasaki further discloses a system, wherein:
the plurality of first users include a plurality of supported users who receive support for lifestyle improvement and a plurality of supporting users who support the lifestyle improvement of the plurality of supported users (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups; See Sasaki Par [0067] which discloses one or more administrators that manage, i.e. support, the one or more target persons, and would therefore constitute “support users” under BRI).
Claim 5 –
Regarding Claim 5, Sasaki, Starson, and Wexler disclose the lifestyle improvement system according to claim 4 in its entirety. Sasaki further discloses a system, wherein:
the first stage of behavior change of each of the plurality of supported users is determined at predetermined intervals according to at least one of:
a record of daily activity of the supported user; a viewing history of the health management application of the supported user; biometric information of the supported user; or an amount of exercise by the supported user (See Sasaki Par [0066] which discloses behavior data of one or more users including activity data, profile data, survey data, etc., and are further specified as human body measures, sensor data, mechanical data, PC operation logs, communicational information such as e-mails and SNS (Social Networking Service), image information (still images or moving images) shot by a camera such as a security camera, history information such as a purchase histories and work histories, management information created for work management, work-time schedule information, audio data, locating information (for example, information indicating positions located by using infrared, Wi-Fi®, UWB (Ultra Wide Band)®, or the like), a data set indicating a profile (for example, age, gender, and the like) of the target person, a data set indicating a result of a survey (for example, a plurality of behaviors) (for example, respective track records of the plurality of behaviors), such as a data set indicating a result of an questionnaire (a plurality of questions) (respective answers to the plurality of questions) or a data set indicating a result of an interview (a plurality of questions) (respective answers to the plurality of questions)), and
the first stage of behavior change of each of the plurality of supporting users is determined at predetermined intervals according to a viewing history of the health management application of the supporting user, and a supporting skill of the supporting user (See Sasaki Par [0067] which discloses one or more administrators that manage, i.e. support, the one or more target persons, and would therefore constitute “support users” under BRI; See Sasaki Par [0040]-[0041] which discloses identifying a stage gap, i.e. a gap between a characteristic of a lower/initial stage and a characteristic of a higher/different stage, and an associated gap reason/shift measure for the stage gap from relationship information for each stage pair, i.e. from one stage to another stage, such that a shift measure can be identified that indicates a measure of causing a target person belonging to a lower or initial stage to make a behavior change to shift to a higher or different stage; Sasaki Par [0052]-[0053] further specifically identifies shift measures and/or factors that are either inhibitory, i.e. does not encourage transition, or stimulatory, i.e. does encourage transition, to behavior change of one or more users; See Sasaki Par [0103], [0105] & [0110] which disclose one or more GUI’s or touch points specified by the administrator to visualize the plurality of stages and/or target users associated with one or more behavior stages and associated information with said plurality of stages/target users; See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s); See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior).
Claim 6 –
Regarding Claim 6, Sasaki, Starson, and Wexler disclose the lifestyle improvement system according to claim 1 in its entirety. Sasaki further discloses a system, wherein:
the stage of behavior change is an indicator of a degree of willingness to change daily behavior to transition from a first state to a second state improved relative to the first state (See Sasaki Par [0040]-[0041] which discloses identifying a stage gap, i.e. a gap between a characteristic of a lower/initial stage and a characteristic of a higher/different stage, and an associated gap reason/shift measure for the stage gap from relationship information for each stage pair, i.e. from one stage to another stage, such that a shift measure can be identified that indicates a measure of causing a target person belonging to a lower or initial stage to make a behavior change to shift to a higher or different stage; Sasaki Par [0052]-[0053] further specifically identifies shift measures and/or factors that are either inhibitory, i.e. does not encourage transition, or stimulatory, i.e. does encourage transition, to behavior change of one or more users, and since the limitation above is not further specified to be a “willingness of a user to change” per se, Sasaki’s description of “inhibitory” and “stimulatory” shift measures can be interpreted to read on a behavior or habit’s “willingness to change”).
Claim 7 –
Regarding Claim 7, Sasaki discloses a portable terminal of each of a plurality of first users connected to a server over a network (See Sasaki Par [0064] which discloses one or more servers and/or terminals connected to describe the computerized environment where the steps/embodiments are being implemented), comprising:
one or more first memories (See Sasaki Par [0027] which discloses the use of one or more memories); and
at least one first processor each coupled to at least one of the one or more first memories (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.) and configured to:
detect a frequency with which the user views a health management application installed on the portable terminal, based on a viewing history of the health management application (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s); See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior),
transmit the frequency to the server (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s)), and
the server comprising (See Sasaki Par [0064] which discloses one or more servers and/or terminals connected to describe the computerized environment where the steps/embodiments are being implemented):
one or more second memories (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.); and
at least one second processor each coupled to at least one of the one or more second memories (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.) and configured to:
obtain a plurality of first stages of behavior change by evaluating a stage of behavior change of each of a plurality of first users (While Sasaki does not necessarily disclose an “obtainer”, “classifier”, “extractor”, “notifier”, “receiver”, and/or “presenter” per se, these are understood by Examiner to simply constitute various program modules and/or processing units for performing the actions recited, therefore see Sasaki Par [0030] which discloses one or more processors and/or processing devices for performing the embodiments described throughout Sasaki and is applied hereinafter for each recitation of “various program modules” to read on these components; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of a plurality of target persons), wherein
the stage of behavior change is determined based on the frequency (See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, i.e. the data defined in Sasaki Par [0066], including various data obtained by measuring behaviors of a plurality of target persons, such as PC operation logs, which would therefore include a user using/ said health management application, i.e. the one or more personal computers to be used by the target person);
classify the plurality of first users into a plurality of groups according to the plurality of first stages of behavior change, grouping users evaluated to be in similar stages of behavior change together (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets, i.e. first users, belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups);
obtain one or more first behaviors of each of one or more second users belonging to a first group among the plurality of groups, and extracts, from among the one or more first behaviors obtained, a second behavior that affects transition to a higher stage of behavior change (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups; See Sasaki Par [0040]-[0041] which discloses identifying a stage gap, i.e. a gap between a characteristic of a lower/initial stage and a characteristic of a higher/different stage, and an associated gap reason/shift measure for the stage gap from relationship information for each stage pair, i.e. from one stage to another stage, such that a shift measure can be identified that indicates a measure of causing a target person belonging to a lower or initial stage to make a behavior change to shift to a higher or different stage; Sasaki Par [0052]-[0053] further specifically identifies shift measures and/or factors that are either inhibitory, i.e. does not encourage transition, or stimulatory, i.e. does encourage transition, to behavior change of one or more users); wherein
the second behavior is determined as a frequent checking behavior of the health management application (While Sasaki does not explicitly disclose a “frequent” checking behavior per se, it is understood by Examiner that Sasaki Par [0039] & [0066] do disclose defining a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of target persons, such as PC operation logs, which would therefore include a user using/checking said health management application, i.e. the one or more personal computers to be used by the target person for mitigating and/or learning new behavior, and therefore while Sasaki does not explicitly recite/anticipate a “frequent” checking behavior of the health management application, Sasaki Par [0039] & [0066] generally discloses and renders obvious a checking behavior of the health management application, via PC operation logs of a user using/checking a health management application, and as such, because it is understood that the checking behavior of the health management application defined in Sasaki would naturally occur at some frequency (i.e., without the “frequency” or “frequent checking” behavior being further defined in the instant set of claims, Sasaki generally renders said embodiment obvious);
notify one or more third users currently belonging to the first group among the one or more second users with behavior information indicating the second behavior on the portable terminal (See Sasaki Par [0100] & [0103]-[0104] which discloses a display section that performs display of the processing efforts related to a gap reason/shift measure identified for each stage pair, such that a user can visualize their behaviors and various gap reasons/shift measures to achieve behavioral change; See Sasaki Par [0047]-[0048] which discloses the number of target person groups that are classified into a stage may be larger than one or more people; See Sasaki Par [0122]-[0124] which discloses that each of one or more target persons, i.e. identified subset of “second users” that becomes the “third users”, from among a plurality of target users, i.e. second users, is assigned a shift measure applicable to the subset or “third users” from among shift measures identified by the reason/measure identification section with respect to a stage pair including a stage to which the target person(s) belongs and a next higher stage that the target person(s) are likely to transition given a certain quality or behavior of that target person(s)).
While Sasaki generally discloses receiving user inputs and/or logs regarding operational behavior for one or more devices, Sasaki is relatively silent on:
obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history;
rearrange the dish list based on the frequency calculated based on the viewing history;
display the arranged dish list and receive a selection of an eaten dish from the user;
generate food consumption information according to the selection;
transmit the frequency and the food consumption information to the server;
the stage of behavior change being based on food consumption information.
However, Starson discloses obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history (See Starson Par [0003]-[0004] & [0075]-[0076] which describes applications and tools utilizing manual entry of food and selecting from a list of suggestions, and further discloses while viewing the screen, the user can see useful data displayed next to or over recognized food items, and log their entire meal with a simple swipe or gesture, thereby constituting an input screen for inputting food consumption history); rearrange the dish list based on the frequency calculated based on the viewing history (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities); display the arranged dish list and receive a selection of an eaten dish from the user (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities; See Starson Par [0121] which discloses displaying said information for the user, such as on the touchscreen of the mobile device; See Starson Par [0004] & [0075]-[0076] which discloses the user logs identified foods consumed with a simple gesture, thereby constituting selection of an eaten dish); generate food consumption information according to the selection (See Starson Par [0120] which discloses the number of elements storing said each food identity are counted and then ranked to determine one or more most-viewed food identifies; See Starson Par [0121] which discloses that the one or more most-viewed food identities are outputted, such that the one or more most-viewed food identities, i.e. food consumption information, are added to a running, i.e. generated, list for display to a user based on selections made by the user in Starson Par [0004] & [0075]-[0076]); transmit the frequency and the food consumption information to the server (See Starson Par [0121] which discloses displaying information for the user, such as on the touchscreen of the mobile device; See Starson Par [0008] & [0152]-[0153] which discloses transmitting the images and selections, i.e. frequency thereof, and food consumption information to an external server that processes said information, and transmits the results back to the mobile device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Sasaki, which already discloses receiving user inputs and/or logs/logging regarding operational behavior for one or more devices, to further include obtaining a dish list, arranging said dish list according to viewing history, displaying the arranged dish list, generating food consumption information according to the selection, and transmitting these aspects to the server, as disclosed by Starson, because this simplifies the process of logging meals by making it faster, more engaging, and more reliable for users by eliminating from-scratch search and selection of meals and replacing said search and selection with an arranged list of most-viewed meals/foods for selection by the user based on the user’s tendencies (See Starson Par [0003]-[0004]).
While Sasaki and Starson effectively disclose the stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, Sasaki and Starson do not necessarily disclose:
the stage of behavior change being based on food consumption information.
However, Wexler discloses the stage of behavior change is determined based on the food consumption information (See Wexler Par [0092]-[0093] which discloses an adaptive support model based on performance or non-performance of a user’s behavior, estimating a new state of the user, an action taken by the software application for the user, or other factors, such that corrective actions can be issued for said user based on said new state that has the highest effectiveness in assisting the behavioral adjustments; See Wexler Par [0020] & [0061] which discloses that the behaviors can be any habit, routine choice, and can include increasing (or decreasing) consumption of a particular kind of food, meal, or nutrition data (and/or including input data relating to number of meals, timing of meals, nutritional information associated therewith, etc.), i.e. the behavioral state and changes determined in Wexler Par [0092]-[0093] are effectively based on the increasing (or decreasing) consumption of a particular kind or number of food, meals, or nutrition data, as disclosed in Wexler Par [0020] & [0061]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Sasaki and Starson, which already discloses he stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, to further include the stage of behavior change is determined based on the food consumption information, as disclosed by Wexler, because food consumption data is relevant to a patient’s health state and changes frequently, and should therefore be considered/processed when determining a user’s health state/behaviors and adjustments/transitions thereof and so a model can learn how these different factors affect the individual's response to the actions (See Wexler Par [0020], [0077], & [0092]-[0093]).
Claim 8 –
Regarding Claim 8, Sasaki discloses a control method of a lifestyle improvement system including one or more first processors and one or more second processors of a server, the control method comprising,(While Sasaki does not necessarily disclose an “obtainer”, “classifier”, “extractor”, “notifier”, “receiver”, and/or “presenter” per se, these are understood by Examiner to simply constitute various program modules and/or processing units for performing the actions recited, therefore see Sasaki Par [0030] which discloses one or more processors and/or processing devices for performing the embodiments described throughout Sasaki and is applied hereinafter for each recitation of “various program modules” to read on these components; See Sasaki Par [0032] for a non-transitory computer-readable medium having recorded thereon a program for causing a computer to execute steps):
detecting, by the one or more first processors, a frequency with which the user views a health management application installed on the portable terminal based on a viewing history of the health management application (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s); See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior);
transmitting, by the one or more processors, the frequency to the server (See Sasaki Par [0064]-[0065] which discloses an assistance server, including one or more personal computers to be used by a target person for managing at least a part of the user’s behavior, i.e. health management application, such that the one or more client systems includes transmission of at least one type of data obtained by measurement about said target person(s));
obtaining, by the one or more second processors, a plurality of first stages of behavior change by evaluating a stage of behavior change of each of a plurality of first users (See Sasaki Par [0030] which discloses the use of one or more processor and/or microprocessor devices, including a central processing unit, graphical processing unit, etc.; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of a plurality of target persons), wherein
the stage of behavior change is determined based on the frequency (See Sasaki Par [0066] which disclose behavior data of the user including various activities measured by one or more sensors or computers, including PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by a target person for managing at least a part of the user’s behavior; See Sasaki Par [0039] which discloses a stage definition section that defines a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, i.e. the data defined in Sasaki Par [0066], including various data obtained by measuring behaviors of a plurality of target persons, such as PC operation logs, which would therefore include a user using said health management application, i.e. the one or more personal computers to be used by the target person);
obtaining, by the one or more second processors, one or more first behaviors of each of one or more second users belonging to a first group among a plurality of groups into which the plurality of first users have been classified, the plurality of first users being classified according to the plurality of first stages of behavior change, grouping users evaluated to be in similar stages of behavior change together (See Sasaki Par [0046] which discloses the stage definition section classifying a plurality of quantitative data sets belonging to one or more types of the quantitative data into a plurality of target person groups (clusters), i.e. second users, each of which is a group of target persons, such that the stage definition section identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores, and further defines each of the plurality of stages that follow the stage indicator and into which the plurality of target person groups are classified, based on a characteristic of each of the plurality of target person groups; See Sasaki Par [0040]-[0041] which discloses identifying a stage gap, i.e. a gap between a characteristic of a lower/initial stage and a characteristic of a higher/different stage, and an associated gap reason/shift measure for the stage gap from relationship information for each stage pair, i.e. from one stage to another stage, such that a shift measure can be identified that indicates a measure of causing a target person belonging to a lower or initial stage to make a behavior change to shift to a higher or different stage; Sasaki Par [0052]-[0053] further specifically identifies shift measures and/or factors that are either inhibitory, i.e. does not encourage transition, or stimulatory, i.e. does encourage transition, to behavior change of one or more users); and
notifying, by the one or more second processors, one or more third users currently belonging to the first group among the one or more second users with behavior information indicating a second behavior that affects transition to a higher stage of behavior change from among the one or more first behaviors obtained (See Sasaki Par [0100] & [0103]-[0104] which discloses a display section that performs display of the processing efforts related to a gap reason/shift measure identified for each stage pair, such that a user can visualize their behaviors and various gap reasons/shift measures to achieve behavioral change; See Sasaki Par [0047]-[0048] which discloses the number of target person groups that are classified into a stage may be larger than one or more people; See Sasaki Par [0122]-[0124] which discloses that each of one or more target persons, i.e. identified subset of “second users” that becomes the “third users”, from among a plurality of target users, i.e. second users, is assigned a shifty measure applicable to the subset or “third users” from among shift measures identified by the reason/measure identification section with respect to a stage pair including a stage to which the target person(s) belongs and a next higher stage that the target person(s) are likely to transition given a certain quality or behavior of that target person(s)), wherein
the second behavior is determined as a frequent checking behavior of the health management application (While Sasaki does not explicitly disclose a “frequent” checking behavior per se, it is understood by Examiner that Sasaki Par [0039] & [0066] do disclose defining a stage indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following a stage indicator, based on behavior data, including various data obtained by measuring behaviors of target persons, such as PC operation logs, which would therefore include a user using/checking said health management application, i.e. the one or more personal computers to be used by the target person for mitigating and/or learning new behavior, and therefore while Sasaki does not explicitly recite/anticipate a “frequent” checking behavior of the health management application, Sasaki Par [0039] & [0066] generally discloses and renders obvious a checking behavior of the health management application, via PC operation logs of a user using/checking a health management application, and as such, because it is understood that the checking behavior of the health management application defined in Sasaki would naturally occur at some frequency (i.e., without the “frequency” or “frequent checking” behavior being further defined in the instant set of claims, Sasaki generally renders said embodiment obvious).
While Sasaki generally discloses receiving user inputs and/or logs regarding operational behavior for one or more devices, Sasaki is relatively silent on:
obtaining a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history;
rearranging the dish list based on the frequency calculated based on the viewing history;
displaying the arranged dish list and receive a selection of an eaten dish from the user;
generating food consumption information according to the selection;
transmitting the frequency and the food consumption information to the server;
the stage of behavior change being based on food consumption information.
However, Starson discloses obtain a dish list including a plurality of dishes, when displaying an input screen for inputting food consumption history (See Starson Par [0003]-[0004] & [0075]-[0076] which describes applications and tools utilizing manual entry of food and selecting from a list of suggestions, and further discloses while viewing the screen, the user can see useful data displayed next to or over recognized food items, and log their entire meal with a simple swipe or gesture, thereby constituting an input screen for inputting food consumption history); rearrange the dish list based on the frequency calculated based on the viewing history (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities); display the arranged dish list and receive a selection of an eaten dish from the user (See Starson Par [0100]-[0118] which discloses arranging a most-viewed queue, the number of elements storing said each food identity are counted, such that these counts may then be ranked to determine one or more most-viewed food identities; See Starson Par [0121] which discloses displaying said information for the user, such as on the touchscreen of the mobile device; See Starson Par [0004] & [0075]-[0076] which discloses the user logs identified foods consumed with a simple gesture, thereby constituting selection of an eaten dish); generate food consumption information according to the selection (See Starson Par [0120] which discloses the number of elements storing said each food identity are counted and then ranked to determine one or more most-viewed food identifies; See Starson Par [0121] which discloses that the one or more most-viewed food identities are outputted, such that the one or more most-viewed food identities, i.e. food consumption information, are added to a running, i.e. generated, list for display to a user based on selections made by the user in Starson Par [0004] & [0075]-[0076]); transmit the frequency and the food consumption information to the server (See Starson Par [0121] which discloses displaying information for the user, such as on the touchscreen of the mobile device; See Starson Par [0008] & [0152]-[0153] which discloses transmitting the images and selections, i.e. frequency thereof, and food consumption information to an external server that processes said information, and transmits the results back to the mobile device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Sasaki, which already discloses receiving user inputs and/or logs/logging regarding operational behavior for one or more devices, to further include obtaining a dish list, arranging said dish list according to viewing history, displaying the arranged dish list, generating food consumption information according to the selection, and transmitting these aspects to the server, as disclosed by Starson, because this simplifies the process of logging meals by making it faster, more engaging, and more reliable for users by eliminating from-scratch search and selection of meals and replacing said search and selection with an arranged list of most-viewed meals/foods for selection by the user based on the user’s tendencies (See Starson Par [0003]-[0004]).
While Sasaki and Starson effectively disclose the stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, Sasaki and Starson do not necessarily disclose:
the stage of behavior change being based on food consumption information.
However, Wexler discloses the stage of behavior change is determined based on the food consumption information (See Wexler Par [0092]-[0093] which discloses an adaptive support model based on performance or non-performance of a user’s behavior, estimating a new state of the user, an action taken by the software application for the user, or other factors, such that corrective actions can be issued for said user based on said new state that has the highest effectiveness in assisting the behavioral adjustments; See Wexler Par [0077] which discloses the state vector being extended to include other information that changes frequency, including self-reported information, such as food consumed; See Wexler Par [0020] & [0061] which discloses that the behaviors can be any habit, routine choice, and can include increasing (or decreasing) consumption of a particular kind of food, meal, or nutrition data (and/or including input data relating to number of meals, timing of meals, nutritional information associated therewith, etc.), i.e. the behavioral state and changes determined in Wexler Par [0092]-[0093] are effectively based on the increasing (or decreasing) consumption of a particular kind or number of food, meals, or nutrition data, as disclosed in Wexler Par [0020], [0061], & [0077]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Sasaki and Starson, which already discloses he stage of behavior change being based on the frequency and logging various food consumption information and presenting food/dishes according to said food consumption information, to further include the stage of behavior change is determined based on the food consumption information, as disclosed by Wexler, because food consumption data is relevant to a patient’s health state and changes frequently, and should therefore be considered/processed when determining a user’s health state/behaviors and adjustments/transitions thereof and so a model can learn how these different factors affect the individual's response to the actions (See Wexler Par [0020], [0077], & [0092]-[0093]).
Claim 9 –
Regarding Claim 9, Sasaki, Starson, and Wexler disclose a non-transitory computer-readable medium having recorded thereon a program for causing a computer to execute the control method according to claim 8 (See prior art analysis of claim 8 above which is disclosed in its entirety by Sasaki, Starson, and Wexler; See Sasaki Par [0032] for a non-transitory computer-readable medium having recorded thereon a program for causing a computer to execute steps).
Response to Arguments
Applicant's arguments filed 06 March 2026 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 101 rejections of claims 1-9, Applicant argues on p. 7-8 of Arguments/Remarks that under step 2A, Prong 1, the claims do not recite an abstract idea. More specifically, Applicant points to claim 2 of Example 37 of the 2019 PEG, stating that the rearranging of icons on a GUI did not constitute an abstract idea, and therefore the independent claims also do not constitute an abstract idea. Examiner respectfully disagrees with Applicant’s arguments. The fact patterns between claim 2 of Example 37 and the instantly pending claims differ substantially. That is, claim 2 of Example 37 recited “determining the amount of each icon using a processor that tracks how much memory has been allocated to each application associated with each icon over a predetermined period of time” which fell outside the realm of being reasonably performed in the human mind. However, the instant claims do not recite limitations relating to determining an amount of memory that has been allocated to each application associated with each icon over a predetermined period of time. That is, each of the steps recited in the instant claims were determined to represent limitations that can be reasonably performed in the human mind, and/or additional elements that represented insignificant, extra-solution activity and/or well-understood, routine, conventional activity found in prior art systems. While Applicant argues against “the dynamic rearrangement of a dish list within a specific health management application interface based on calculated application usage metrics is a specific computer function that has no mental analog”, Applicant argues language that is not found in the instant claims. That is, the usage metrics are merely based on how often a user views a health management application, which is wholly dissimilar from Example 37 which constructed its usage metrics from how much memory had been allocated to each application associated with each icon over a predetermined period of time. That is, merely determining how often a user views or interacts with a system does not represent activity that falls outside of the human mind, whereas determining how much memory had been allocated to each application associated with each icon over a predetermined period of time does fall outside of a human analogue. Furthermore, the inventive concept of Example 37 was directed towards providing an improved graphical user interface. Via Applicant’s own disclosure and arguments, the instant application is directed towards providing “lifestyle improvement” and/or dietary guidance support to the subject according to their phase of behavior change. That is, the instant application’s inventive concept is not directed towards user interfaces and improvements thereof. Instead, the instant application merely utilizes the presentation of the information/list of foods to the user in a rearranged format to further the already-characterized abstraction, by presenting potential foods for a user according to the user’s needs. These efforts still represent activity that can be reasonably performed in the mind of a human and/or at the very least represent efforts to organize human activity at least by outputting recommendations based on said user data and food preferences/interaction. As such, claims 1-9 are not substantially similar to claim 2 of Example 37. Therefore, claims 1-9 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1-9, Applicant argues on p. 8-9 of Arguments/Remarks that the claims overcome previous 35 U.S.C. 101 rejections. More specifically, Applicant argues that the claims represent a practical application in view of Claim 1 of Example 37 from the 2019 Subject Matter Eligibility Examples, at least by “improv[ing] a portable terminal” by displaying a rearranged dish list and receiving a selection of an eaten dish from the user. Examiner respectfully disagrees with Applicant’s arguments. The operations recited in the independent claims represented insignificant, extra-solution activity and/or well-understood, routine, and conventional activity found in prior art systems. Furthermore, the aspects specifically argued by Applicant regarding “displaying a rearranged dish list and receiving a selection of an eaten dish from the user” represent aspects that merely further limit the already-characterized abstraction by simply outputting data and/or receiving inputs a ta user interface. Therefore, while Applicant argues in view Claim 1 of Example 37, the fact patterns between these scenarios wholly differ. That is, the background of Example 37 specifically provided evidence towards shortcomings in prior art systems regarding dynamic/non-traditional arrangements of icons on a display in the Specification of Example 37, and the solution for said shortcomings was clearly presented in claim 1 of Example 37. This substantially differs from the scenario for the instant application, because the instant application’s inventive concept is not directed towards user interfaces and improvements thereof. Instead, the instant application merely utilizes the presentation of the information/list of foods to the user in a rearranged format to further the already-characterized abstraction, by presenting potential foods for a user according to the user’s needs. That is, Applicant does not provide evidence regarding shortcomings in prior art systems of how potential food selections for a user is arranged in a user interface. Rather, the efforts described of displaying a rearranged dish list and receiving a selection of an eaten dish from the user merely further limit the abstraction at hand and/or represent aspects to simply accomplish the already-characterized abstraction of providing “lifestyle improvement” and/or dietary guidance support to the subject according to their phase of behavior change. As such, claims 1-9 are not substantially similar to claim 1 of Example 37. Therefore, claims 1-9 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 103 rejections of claims 1-9, Applicant argues on p. 9-10 of Arguments/Remarks that Sasaki does not disclose the newly amended limitations found in independent claims 1, 7, & 8. More specifically, Applicant argues that Sasaki does not explicitly disclose detecting a frequency with which the user views a health management application, because the process including the recited detecting operation in claim 1 is not a general notion, but rather includes tracking viewing events, detecting the frequency, transmitting the frequency, and determining the stage of behavior change based on the frequency. Examiner respectfully disagrees with Applicant’s Arguments. Sasaki does not explicitly recite/anticipate a “frequent” checking behavior of the health management application, but Sasaki Par [0039] & [0066] generally discloses and renders obvious a checking behavior of the health management application, via PC operation logs of a user using/checking a health management application. As such, because it is understood that the checking behavior of the health management application defined in Sasaki would naturally occur at some frequency, without the “frequency” or “frequent checking” behavior being further defined in the instant set of claims, Sasaki generally renders said embodiment obvious under 35 U.S.C. 103. While Applicant generally argues on that the PC operation log is not the frequency with which the user views a health management application, the disclosure of Sasaki Par [0039] & [0066] suggests otherwise. That is, PC operation logs are maintained for one or more users that are using and/or checking the associated health management application. As such, it is understood that these operation logs effectively track each user and the duration/frequencies of the user using said system/application, but Sasaki does not explicitly recite a behavior relating to “frequent” performance of said usage. Furthermore, while Applicant argues that the process in claim 1 is not a general notion, but rather includes tracking viewing events, detecting the frequency, transmitting the frequency, and determining the stage of behavior change based on the frequency, Examiner asserts that PC operation logs do allow for tracking viewing events, detecting the frequency, and transmitting the frequency at least by recording various operation logs, storing various operation logs and transmitting or utilizing operation logs by the system. Furthermore, the determined stage of behavior of Sasaki is determined based on measuring behavior of a plurality of target persons, including said PC operation logs, which as mentioned above, allows for tracking viewing events, detecting the frequency, and transmitting the frequency at least by recording various operation logs, storing various operation logs and transmitting or utilizing operation logs by the system. However, Examiner concedes that Sasaki does not entirely disclose the newly amended limitations regarding obtaining a dish list, displaying an input screen for inputting food consumption history, rearranging the dish list based on the frequency calculated based on the viewing history, displaying the rearranged dish list and receiving a selection of an eaten dish from the user, and generating food consumption information according to the received selection. Therefore, the previous 35 U.S.C. 103 rejections for claims 1-9 made in view of Sasaki are withdraw. However, upon further consideration, a new ground of rejection has been made under 35 U.S.C. 103 over Sasaki in view of Starson, further in view of Wexler. Newly cited Starson discloses the newly amended limitations regarding obtaining a dish list, displaying an input screen for inputting food consumption history, rearranging the dish list based on the frequency calculated based on the viewing history, displaying the rearranged dish list and receiving a selection of an eaten dish from the user, and generating food consumption information according to the received selection in their entirety. Newly cited Wexler is relied upon for disclosing the stage of behavior change being based on food consumption information. As such, independent claims 1, 7, & 8 and claims dependent therefrom (claims 2-6 & 9) are effectively rendered obvious by Sasaki in view of Starson, further in view of Wexler and therefore remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of claims 1-9, Applicant argues on p. 9-11 of Arguments/Remarks that MPEP 2141(III) states that “The fact that a certain result or characteristic may occur or be present in the prior art is not sufficient to establish the inherency of that result or characteristic” and that Examiner and the previous Office Action lacked a reasoned rationale to modify the prior art. Applicant further argues that therefore the prior art rejections under 35 U.S.C. 103 should be withdrawn. Examiner respectfully disagrees with Applicant’s arguments. Examiner never states that a certain result or characteristics may occur, but that said result or characteristic inherently occurs. That is, Sasaki Par [0039] & [0066] generally discloses and renders obvious a checking behavior of the health management application, via PC operation logs of a user using/checking a health management application, and as such, because it is understood that the checking behavior of the health management application defined in Sasaki would naturally, i.e. inherently, occur at some frequency (i.e., without the “frequency” or “frequent checking” behavior being further defined in the instant set of claims) Sasaki generally renders said embodiment obvious. That is, because Sasaki generates PC operation logs of a user that checks their health management application, the checking behavior and frequency thereof would therefore be effectively logged by the PC. As such, this is in direct contradiction to Applicant’s arguments which states that Examiner’s reasoning relied on something that may occur, because the reasoning provided stemmed from something that does happen, not something that has a possibility of happening. Furthermore, regarding the purported lack of a reasoned rationale to modify the prior art, Examiner contends that other rationales to support a conclusion of obviousness may be relied upon by Office personnel and there is no explicit list of rationales to support a conclusion of obviousness. In the instance of Sasaki, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recognize that by recording PC operation logs of a user using/checking a health management application, and as such, the using/checking behavior of the health management application defined in Sasaki would naturally, i.e. inherently, occur at some frequency. That is, each instance or log of said activity in the entirety of the operation logs would inherently show the frequency that the user is using or checking said health management application. Furthermore, Examiner points to MPEP 2141(IV) that “inherency may support a missing claim limitation in obviousness analysis”. As such, Examiner still maintains the opinion that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recognize that by recording PC operation logs of a user using/checking a health management application, and as such, the using/checking behavior of the health management application defined in Sasaki would naturally, i.e. inherently, occur at some frequency. As such, independent claims 1, 7, & 8 and claims dependent therefrom (claims 2-6 & 9) are effectively rendered obvious by Sasaki in view of Starson, further in view of Wexler and therefore remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of claims 1-9, Applicant argues on p. 11 of Arguments/Remarks that dependent claims 2-6 & 9 depend from amended independent claims 1, 7, & 8 and would therefore also be allowable over the prior art by virtue of dependency. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, a new ground of rejection under 35 U.S.C. 103 over Sasaki in view of Starson, further in view of Wexler, has been issued for claims 1-9, and it has been determined that independent claims 1, 7, & 8 and claims dependent therefrom are effectively rendered obvious by Sasaki in view of Starson, further in view of Wexler and therefore remain rejected under 35 U.S.C. 103. As such, Applicant’s arguments regarding independent claims 1, 7, & 8 being allowable are rendered moot, because these claims are not allowable over the prior art. As such, independent claims 1, 7, & 8 and claims dependent therefrom (claims 2-6 & 9) are effectively rendered obvious by Sasaki in view of Starson, further in view of Wexler and therefore remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
McNair et al. (U.S. Patent No. 10,420,486) discloses a system for promoting positive activity patterns for users and facilitate long-term adherence to the activity patterns;
Petrov et al. (U.S. Patent Publication No. 2020/0279500) discloses a system for facilitating lifestyle changes by providing support, motivation, progress/tracking, information, analysis;
Abujbara et al. (U.S. Patent Publication No. 2012/0233002) discloses a system for assist users in the selection of the most affordable and beneficial subset of food items to order at food serving establishments and/or grocery stores by composing and presenting to each guest an individualized subset of menu items.
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/H.R./Examiner, Art Unit 3684 /KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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