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 .
Claims 1-20 are pending and have been examined.
Priority
Applicant has filed the instant application as a continuation. Applicant has made amendments to paragraphs [0004] and [0005], which essentially are supported in paragraph [0026]. The amended sections in the paragraphs are interpreted as paragraph [0026].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12127839. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are broader than the patented claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system or method, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 11 as the claim that represents the claimed invention for analysis and is similar to system Claim 1.
Claim 11 recites the limitations of:
A method for generating a stress disorder ration program, the method comprising: obtaining, by a computing device, a stress representation;
ascertaining, by the computing device, an equanimity signature, wherein ascertaining the equanimity signature further comprises:
retrieving an acclimation element;
determining a relative vector as a function of the acclimation element; and
ascertaining the equanimity signature as a function of the relative vector and the stress representation using a stress machine-learning model, and wherein the stress machine-learning model inputs the relative vector and the stress representation and outputs the equanimity signature;
identifying, by the computing device, a physiological influence as a function of the equanimity signature;
determining, by the computing device, an edible as a function of the physiological influence; and
generating, by the computing device, a ration program as a function of the edible.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, in bold above, which covers performance of the limitation as managing personal behavior. Ascertaining stress (equanimity signature), retrieving situations (acclimation element) associated with stressful situations, determining measures/values (relative vector) as a function of stressful situations, ascertaining stress (equanimity signature) as a function of situations (relative vector), identifying physical response (physiological influence) based on stress (equanimity signature), determining an edible (food) as a function of physical response (physiological influence), and generating a food to be consumed over time (ration), is managing personal behavior by teaching and following rules/instructions. See para. [0003] where the steps are performed for an individual. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as a person in their mind or with pen and paper can ascertain stress (equanimity signature), retrieve situations (acclimation element) associated with stressful situations, determine measures/values (relative vector) as a function of stressful situations, ascertain stress (equanimity signature) as a function of situations (relative vector), identify physical response (physiological influence) based on stress (equanimity signature), determine an edible (food) as a function of physical response (physiological influence), and generate a food to be consumed over time (ration), the claims are also abstract under Mental Processes grouping of abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: computing device machine (Claim 1); computing device, machine (Claim 11). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The machine is a generic machine recited at a high level of generality. The stress machine learning model is being used, is a generic model, and is claimed at a high level of generality. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 11 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as retrieving are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1 and 11 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-10 and 12-20 further define the abstract idea that is present in their respective independent claims 1 and 11 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims themselves are abstract or further limit abstract ideas. Claims 6, 8, 16, and 18 recite using machine learning models, which are also generic models recited at a high level of generality. Therefore, the claims 2-10 and 12-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2021/0202067 to Williams et al. in view of Pub. No. US 20130031107 to Pan.
Regarding claim 1 and 11
(claim 11) A method for generating a stress disorder ration program, the method comprising: obtaining, by a computing device, a stress representation;
ascertaining, by the computing device, an equanimity signature, wherein ascertaining the equanimity signature further comprises:
{
From Applicant's specification on "equanimity signature" ...
"Still referring to FIG. 1, computing device 104 ascertains an equanimity
signature 112. As used in this disclosure an "equanimity signature" is a
representation and/or profile of the magnitude of stress of an individual.
For example, and without limitation, equanimity signature 112 may denote that a
magnitude of stress of an individual is exhibiting exceeds a predicted and/or
expected magnitude of stress that should be experienced. As a further nonlimiting
example, equanimity signature 112 may denote that a magnitude of
stress an individual is exhibiting is abnormally low compared to a predicted
and/or expected magnitude of stress that should be experienced ... " [0015]
Therefore "equanimity signature" is a profile for an individual of the
magnitude of stress an individual has or is expected to have.
}
Williams et al. teaches:
A server (computer)…
“FIG. 1 also discloses an example Addiction Server/Cloud/Internet of Things/Client Application(s) and Processing Networks 103 (server 103a) that can be a key hub for communications with a variety of people 102, resources, assets, applications, and data sources that may have relevance to the addict. As shown, the data sources may include a database 103b of support network data (e.g., location, availability/schedule, specialties, privacy requirements or regulations, etc.) and a database 103c of third party app data and interfaces (e.g., social medial, local search, navigation, etc.) and affinity programs. The data sources may also include data sources accessible over a network 118 (e.g., local network, public network, private network, internet, IOT, etc.) such as a database 127 of addict data (e.g., medical, professional, public records, media, etc.), a database 128 of local addiction data (e.g., police reports, trends, etc.) and a database 129 of local data feeds (e.g., events, traffic, news, weather, camera feeds, etc.). Additional data sources may include addict data sources including addict data and analytics 104, including predictive analytics data, etc. The addict data and analytics 104 may include privacy, security, rewards, motivational database(s) and engine(s) 105, action/response engine, interface coordination database(s) and engine(s) 106, risk/relapse assessment/prediction, learning database(s) and engine(s) 107, (trending) context and behavior inference database(s) and engine(s) 108, addict profile, support network, schedule/calendar, devices/vehicles 109, addict usage triggers, hobbies, media posts, behavioral data 110, location/context profiles, historical location/context data 111, high risk locations, places of interests (POIs) suppliers, enablers 112, addict medical, personal data 113, and administration, security, and verification 114.” [0066]
Example of triggers (ascertaining) such as stress (equanimity signature)…
“In various embodiments of the present disclosure, addicts can be helped to detect and deal with the triggers that initiate or enhance the craving to indulge in their addiction. There generally are many such triggers, including but not limited to: Anger, Anxiety, Boredom, Change, Conflict, Depression, Disorder, Envy (desire to) Escape, Excitement, Extreme Emotions, Fear, Frustration, Guilt, Health problems, Holidays, Hunger, Insomnia, Job issues, Kids/Children, Loneliness, Media (TV, Radio, the Internet) marketing, Mid-Life Crisis, Money problems, Music, Noise, Overconfidence, Peer Pressure, Power, Powerlessness, Proximity (to an addictive substance), (fear of) Quitting, Relationships, Relatives, Reminders, Sex, (change of) Seasons, Smell, Social Situations, Stress, Taste, Times of Day, (being) Tired, (feeling) Not Fun or Unhappy, (feeling) Victimized, Weather, Yelling, and Zeal (high energy). Many addicts are especially vulnerable to relapsing when faced with one or more of these triggers. Some of these triggers have a location dimension to them, most notably proximity to an addiction substance or activity, and many more have a location element in the actions or solutions for dealing with those triggers (and/or high-risk for relapse situations) without relapse. For example, a response to the detection of the Boredom trigger may require the addict to go to a certain place to do a certain activity. Loneliness would involve visiting with or visit by a member of the addict's support network. Noise could require going to a quiet place to meditate, such as a church or library, or to retreat to a serene program on a virtual reality device. This kind of information could be captured in a data profile (e.g., an addict profile including actions to take in relation to the addict, etc.) stored in a database or similar data store.” [0045]
retrieving an acclimation element;
{
From Applicant's specification on "acclimation element" ...
"As used in this disclosure an "acclimation element" is an element of datum
denoting an acclimation and/or experience with a stressful situation and/or a
stressor. As used in this disclosure a "stressful situation" is an event and/or
experience that causes an individual to feel and/or experience stress, wherein
stress is defined above in detail. For example, and without limitation,
acclimation element 116 may denote that an individual is used to hearing
firearm discharges and/or loud noises as a function of growing up and/or
frequenting a firearm training facility, wherein that individual may have a
reduced stress when hearing a loud noise and/or firearm discharge. As a
further non-limiting example, acclimation element 116 may denote that an
individual is used to interactions with law enforcement officers as a function of
volunteering as a first responder, wherein an interaction with a law enforcement
officer may result in reduced stress on the individual's body when interacting with
a law enforcement officer. As a further non-limiting example, acclimation element
may denote that an individual has no previous experience and/or history
interacting with animals, wherein that individual may have an elevated effect of
stress due to an incident, such as a bite, with a canine." [0115]
Therefore, "acclimation element" is familiar (familiarity) of an individual
with different stressful environments/situations.
}
Data related to addict’s usage triggers….
“The present disclosure includes various exemplary embodiments of systems and methods that utilize the location and context of an addict and other resources to a) preempt trigger and/or high risk relapse situations, b) prevent relapse in high risk situations, and/or c) respond to, manage, and recovery from a relapse when they do occur. Various embodiments include collecting, aggregating, and analyzing addict- and addiction-related data specific to that addict's condition, vulnerabilities, motivations, and usage triggers. Such data/information can be collected from a wide variety of sensors and other data sources, including but not limited to: personal devices such as smartphones, tablets, computers, PDAs, wearables (data collection devices worn on the person, such as Fitbit, etc.), implants, Google Glass, etc.; nearby sensors or devices such as security/video cameras, smart devices (such as smart home-related sensors, etc.), crowdsourcing data collection applications of nearby users, building/store/office Wi-Fi networks, location-sensitive beacons, etc.; and/or extended data collection mechanisms such as road traffic sensors, public video cameras or billboard displays, weather data collection sensors, law enforcement/security-related devices, etc.” [0046]
Identify (retrieving) contexts of relapse (contexts of familiar, therefore acclimation element) situations…
“Various system and method embodiments according to the present disclosure make use of trigger-based sensor networks and trigger-based support networks that may be tuned or modified so as to collect data potentially related to one or more particular addiction triggers, such as Anger, Frustration, Noise, Social Situations, Stress, Yelling, etc. Such data solely or in combination can identify various high risk (of addiction usage) contexts or relapse situations, circumstances, events, and/or possible mental frame-of-mind/thought processes that often have to be managed to allow the addict the ability to successfully deal with such situations without succumbing to the addiction(s). This managing of such situations may include providing, recommending, and/or injecting actions, activities, resources, recommendations, directions, and/or elements of control into the addict’s life on either an ad-hoc, occasional, periodic, and/or (near) continuous manner to help the addict to refrain from their addiction. Management of such situations can be done via a variety of analysis, assessment, and prediction engines and algorithms that anticipate or predict the impact of certain situations, contexts, circumstances, or events on an addict’s behavior and overall sobriety and devise and quickly put in place a course of action to minimize the addict’s risk of relapse, or failing that, minimizing any resultant harm and damage. Such a course of action may be predominately location-based, meaning using location information as a key part of the course of action, but the present disclosure is not limited to location-based information; key information may well include non-location based elements, particularly the use of sensors that can provide valuable input into understanding the current context of the addict, and actions that may have little or nothing to do with an addict’s location (such as the addict calling a family member to discuss his Frustration, for example).” [0047]
“FIG. 2 discloses one such device(s) 200 that can sense, monitor, and/or control aspects of an addict's context. The device 200 includes an array of capabilities, including sensors for detecting or anticipating addiction trigger conditions (e.g., contexts, situations, circumstances, environments, and/or state of mind(s) that may cause the addict to relapse or use substances or activities related to his/her addiction, etc.); mechanisms for interfacing with the addict including tangible/tactile Interfaces 201 (Display, Lights, Sound, Vibration, Heat, Smell, etc.). For example, a Smell Interface could generate the smell of fresh pine trees or pine tar in response to high risk associated with the Escape trigger being detected (helping remind the addict of good times he has had when hiking in the Rocky Mountains). The wide variety of interfaces is premised that dissemination of information to/from an addict needs to be in the most effective means possible at any given time or context, which can vary from day-to-day or hour-to-hour. In addition to traditional interfaces such as sound or vibration interfaces, some exemplary embodiments of the present disclosure include the ability to project (or interface to a projector) 2 or 3 dimensional images, video, GIFs, or real-time holographic projections that the addict can converse with. It also includes the ability to augment reality (insertion of images not actually present), and even (de) augmenting reality, such as the elimination of addiction triggers/temptations such as liquor stores from the addict's vision.” [0084]
Use of (retrieving) conditions/context (acclimation element) related to stress…
“The use of predictive analytics may be used in the exemplary embodiments disclosed herein. For quarantines, this is important as its not necessarily the current supply of, say, toilet paper, it is when it is likely to run out. Thus, predictive analytics including the use of context (e.g., people in the household, their conditions (e.g., GI problems, etc.), rate of consumption, number of bathrooms, type of toilet paper (e.g. 2 ply versus 1 ply, etc.), etc.) and dynamic monitoring (e.g., not just frequently checking the usage, but recognizing that changing (re: dynamic) conditions/context (e.g. grandma is now sheltering-in-place with us, Janie's increased level of Fear is causing her gastral distress, Tom's PTSD is getting worse as confinement continues, etc.) is very important. For example, this is important when predicting necessary supplies (on an absolute basis, on a relative basis compared to needs and/or certain circumstances or assumptions, and/or given/relative to a specific time period(s)), and particularly when an exemption is needed to preempt someone being tempted to break quarantine (and what and how that is measured, e.g. rising Anxiety as measured by heart palpitations, monitoring of household conversations, types of web sites visited, content of messages sent, etc.).” [0455]
determining a relative vector as a function of the acclimation element; and
{
From Applicant's specification on "relative vector" ...
"Still referring to FIG. 1, computing device 104 determines a relative vector 120
as a function of acclimation element 116. As used in this disclosure a "relative
vector" is a data structure that represents one or more quantitative values
and/or measures of relative stress of an individual for a given stressful
event and/or stressor. For example, and without limitation, relative vector 120
may be a value of 30 for a stressor of a high stress job, wherein the individual
has 20 years of experience working at the high stress job. As a further nonlimiting
example, relative vector 120 may be a value of 80 for a stressor of need
a surgery, wherein the individual has never had experienced a surgical
procedure ... " [0016]
Therefore a "relative vector" has values/measures (is a stress
value/measure) of an individual stress per given event/stressor (a stress
vector per event)
}
Data related to context (acclimation element)…
“…For example, FIG. 1 shows additional examples of smart home connected devices 115b including a TV, refrigerator, and microwave. As more and more devices become smart, the smart device will have the ability to capture data that will help determine a person's location/context through onboard or connected data capture devices such as video, audio, and/or other sensors. Combined with the device's known location (or ability to determine the device's location), and the connectivity associated with communicating to and from these devices (also known as the Internet of Things or IoT), these devices/networks may provide new key sources of personal context information.” [0072]
Example of context and argument hot day (stressful situation, therefore acclimation element)…
“This term “context” as used herein may refer to the situation or circumstances in which a behavior, event, or activity occurs, e.g., the particular setting in which the behavior/event/activity occurs, etc. For example, when attempting to understand behavior, it is important to look at the situation or circumstances present at the time of the behavior. For example, the behavior Anger can be detected through measuring blood pressure, heart rate, skin temperature, and detection of yelling sounds, and for some people be considered a trigger by itself. But for some other people, in some circumstances, it is particularly valuable to know the context. For example, a person might be in the presence of his ex-spouse, and they may be in an argument. Or, for example, they could be having the argument in a public place on a hot day with no air conditioning. Having this understanding of context, in addition to behavior, helps to select the best preemptive or mitigating actions to cool down the Anger before a violation (e.g., a drinking relapse, etc.) occurs, such as getting the parolee physically away from the ex-spouse as soon as possible and to go cool down in an air conditioned, private place.” [0283]
See Vector below.
See Machine Learning and Vector below.
ascertaining the equanimity signature as a function of the relative vector and the stress representation using a stress machine-learning model, and wherein the stress machine-learning model inputs the relative vector and the stress representation and outputs the equanimity signature;
Artificial intelligence (machine learning) with high-risk situation (stress representation)…
“In addition, there are potentially more persons/entities involved in location/context privacy and security than just users. Certainly data can be generated/sourced from a wide variety of mechanisms/methods/sources. In addition, control over this data does not necessarily have to be by the (primary) user or account holder. Indeed, in addiction-related embodiments in particular, different data sets can be controlled by someone other than the key user (e.g. addict, etc.). One embodiment of such in location-based security is the use of an addict monitor or controller. Such an entity can be a human (or even artificial entity) that is responsible for monitoring the addict over a certain time period. The idea is to maximize or at least increase the probability that if a relapse were to happen, there would be a person on-call that would be at least generally aware of what the addict was doing, or at least have no uncertainty that if a high-risk/relapse situation were to occur that they are #1 on the list of support persons to respond. This entity may be a human or an artificial intelligence entity that has the responsibility of being at the top of the addict support hierarchy during a given time period if the addict were to encounter a high-risk situation during that time. The general purpose of such controllers is multi-faceted: to distribute security of addict information across different entities as a general security precaution; make it progressively more difficult for hacker to access the data; and, in the case of addiction-related data provide security control to entities that are (almost literally) more sober than the actual user.” [0190]
Pre-identified behavior and initial risk assessment/detection (ascertaining the equanimity signature) from questionnaire regarding emotional response (stress)…
“FIG. 15 depicts an example embodiment of a method for monitoring for a risk of a pre-identified behavior (e.g., pre-identified addict-related undesirable behavior, etc.). FIG. 15 also includes example triggers, priorities, and initial risk assessment/detection sensors. As shown in FIG. 15, a first step may include providing a questionnaire (e.g., on paper, online, video, etc.). Questions on the question may be designed to obtain not just facts but to also elicit an emotional response from the person (e.g., addict, etc.) answering the questionnaire. The questionnaire may be used to determine the priority/impact/severity of each drinking trigger listed as well as any other ones that might apply. A high ranking for a trigger may indicate that the person often drinks or wants to drink when this trigger occurs, whereas a low ranking for a trigger may indicate that the trigger never causes the person to drink or want to drink.” [0177]
Where drinking causes anger (stress)…
“In exemplary embodiments disclosed herein, the behavior modification rewards programs disclosed herein are not limited to actual “end” or “core” behavior milestones, such as losing 10 pounds or drinking 4 less drinks per day. Instead and/or in addition, the rewards programs reward “upstream” behaviors that underlie the “end” behavior(s) to be modified. For example, if getting Angry causes a person to want to drink (an “Anger” trigger), the rewards program focuses on reducing/modifying situations/contexts that might lead to the person getting Angry. This kind of “preemptive” anger management (in this particular example) is very distinct from what in traditional behavior modification programs would be focused on managing (e.g., calming down the person) once the person is already angry.” [0563]
“The system may be configured to use, in combination, the plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks to determine when a violation trigger is being activated and/or when a potential violation is in progress through a pre-identification process and an iterative machine learning/artificial intelligence process incorporating human input of high risk chance of violation situations, to thereby enable the system to proactively and preemptively detect high-risk violation situations for the person under restriction.” [0349]
Machine learning for updating (ascertaining) pre-identified risk profile (equanimity signature) using machine learning…
“Or, perhaps, detection of such about-to-explode-cabin-fever conditions could cause doctors/authorities to prescribe (without normal seeing-in-person protocols) Anxiety etc.-reducing medications and/or other actions (such as allowing the person out of the house once a day for local-based running under appropriate control conditions). As such, the detection and preemption of risks are an example of the dynamic risk detection capabilities achievable herein, with appropriate machine learning, artificial intelligence, and/or other mechanism-based updating of pre-identified risk profile and employment of new or modification of existing sensors et al., as well as creation of new risk calculations, risk preemption/mitigation actions, resources, and interfaces to prevent/preempt the risk(s). Demographic elements, such as ethnicity could also be incorporated, particularly if there are ethnicity-based risk factors associated with an illness/virus. Such special risk factors, such as ethnicity and age, could feed into considerations of these factors on a macro or micro-basis. Various validation mechanisms could be employed to verify demographic risks linking to databases and also/instead to sensor et. Al. readings for measuring, verifying, and/or validating physical (and mental) demographic risks.” [0446]
See Vector Below.
See Machine Learning and Vector below.
identifying, by the computing device, a physiological influence as a function of the equanimity signature;
{
From Applicant's specification on "physiological influence" ...
"Still referring to FIG. 1, computing device identifies a physiological influence
128 as a function of equanimity signature 112. As used in this disclosure a "physiological influence" is an effect and/or influence that equanimity
signature and/or stress representation has on an individual's body. For
example, and without limitation, physiological influence 128 may include one or more psychological symptoms including, but not limited to, becoming easily agitated, frustration, difficulty relaxing, low energy, headaches, upset stomach, tense muscles, chest pains, rapid heart rate, insomnia, nervousness, tinnitus, dry mouth, difficulty swallowing, memory loss, cardiovascular disease, obesity, sexual dysfunction, acne, psoriasis, eczema, gastrointestinal problems, and the like thereof ... " [0032]
Therefore a "physiological influence" is a physical response related to a
user's stress profile and physical strain caused by stress.
}
Person hot with elevated blood pressure (physiological influence) caused by anxiety/worry (stress, therefore equanimity signature)…
“In addition, physical triggers may also be caused by and/or are interrelated with mental triggers. For example, a person being hot (physical trigger) may be caused by elevated blood pressure (physical), which elevated blood pressure may have been caused by Anxiety/Worry (mental). As another example, a person feeling thirsty (physical trigger) may be caused by the person being hot (a both mental and physical state), which, in turn, may have been caused by Anxiety/Worry (mental state/behavior/trigger). As yet a further example, a person being short-of-breath (physical trigger) may be caused by Anxiety or Excitement (mental). In addition to these examples, another example includes mental and physical triggers that have a more clear/major cause-and-effect connection, such as a person having mental issue/trigger/state caused by a vitamin D deficiency and the household is running out of vitamin D supplements/food.” [0511]
determining, by the computing device, an edible as a function of the physiological influence; and
Eat/drink less (determining an edible) based on weight (physiological influence)…
“In general, rewards programs are focused on a specific, “fixed” behavior (e.g., drinking, eating) or a limited set/combination of behaviors or behavior modifications (e.g., exercise more, eat/drink less, etc.) that will have “fixed” conditions (often with time elements), e.g., lose 10 pounds in the next month, exercise for at least 30 minutes at least twice a week, drink on average 4 less drinks per day over the next month, etc. In turn, these rewards programs focus on one or a (very) limited set of the above rewards types, particularly physical/financial rewards (e.g., free goods and services, coupons/vouchers, monetary credits, etc.). Within a type, the “conditions” by which a reward is awarded is also generally “fixed,” such as a coupon for X in the amount of Y (%), possibly along with various redemption conditions (e.g., redeem by Z date). The nature of these “fixed” requirements, e.g., a fixed reward type, the level, time constraints, etc. for a “fixed” behavior modification (e.g., lose 10 pounds by the end of next month) is generally consistent across all prior art rewards systems (behavior-focused and non-behavior focused, such as traditional airline mile earnings programs) monitoring/modification.” [0594]
generating, by the computing device, a ration program as a function of the edible.
{
From Applicant's specification on "ration program" ...
“Still referring to FIG. 1, computing device 104 generates a ration program 136 as a function of edible 132. As used in this disclosure a "ration program" is a program consisting of one or more edibles that are to be consumed over a given time period, wherein a time period is a temporal measurement such as seconds, minutes, hours, days, weeks, months, years, and the like thereof. As a non-limiting example ration program 136 may consist of recommending matcha powder for 8 days. As a further non-limiting example ration program 136 may recommend swiss chard for a first day, sweet potatoes for a second day, and kimchi for a third day. Ration program 136 may include one or more diet programs such as paleo, keto, vegan, vegetarian, Mediterranean, Dukan, Zone, HCG, and the like thereof. Computing device 104 may develop ration program 136 as a function of an intended functional goal. As used in this disclosure an "intended functional goal" is a goal that an edible may generate according to a predicted and/or purposeful plan. As a non-limiting example, intended functional goal may include a treatment goal…” [0043]
Therefore a “ration program” is a recommendation of food to consume over a time period.
}
Modifying and monitoring (therefore generating) eating to may carbs (ration program) as a function of diet (edible)…
’’’ The actions associated with modifying the behavior, if based on monitoring, are also physical, e.g., monitoring your diet indicates you are eating too many carbs and should eat more fruit next week…” [0531]
Vector
Williams et al. teaches measuring stress and acclimation. They do not teach vector.
Pan et al. also in the business of stress teaches:
A vector as a function of history (acclimation element) ...
"If the emotion type value in the user index or the history behavior
index is set "calm", it (s.sub.4) will score 8 (the highest score) and
the similar emotion types, like "surprised" and "sad", will score 7
(the second highest score) each. The other emotion types follow the
same rule, so the initial score vector of eight emotion types is
presented by S=(5, 6, 7, 8, 7, 6, 5, 4). Another example is indicated in the
following Table 2 ... "[0027]
Table 2 and vector such as "Calm," "Angry," etc. (stress) ...
PNG
media_image1.png
90
332
media_image1.png
Greyscale
Where user model is based on past events and interactions
(therefore acclimation element) ...
"European Patent No. 1647903A 1 disclosed systems and methods that employ user models to personalize queries and/or search results according to information that is relevant to respective user characteristics. The user model may be assembled automatically via an analysis of a user's behavior and other features, such as the user's past events, previous search history, and interactions with the system. Additionally, the user's address or e-mail address can come up with the city where the user is located. For example, when the user looks for "weather," the information about the weather in the city where the user is located can be automatically found." [0005]
It would have been obvious to one of ordinary skill in the art at the time of
filing to include in the method and system of Williams et al. the ability to
use a vector as taught by Pan et al. since the claimed invention is merely a
combination of old elements and in the combination each element merely
would have performed the same function as it did separately, and one of
ordinary skill in the art would have recognized that the results of the
combination were predictable. Further motivation is provided by Pan et al.
who teaches the benefits of using a vector for different stress indicators,
as people have different emotional types.
Machine Learning and Vector
The combined references teach machine learning. They also teach vector, with stress indicators. They do not teach machine learning with vector.
However, Williams et al. also in the business of machine learning teaches:
Al using behavior (equanimity signature) and context (stress
representation, acclimation element) ....
"Although the potential actions may vary, possible actions are preidentified based on the behaviors (and as applicable, contexts), e.g. "triggers", and other actions generated by artificial intelligence (Al), "if/then"-type decision trees and/or other machine-based technologies described above. Example actions include sending out message(s) to trusted friends or family members; automatically calling an Uber driver (while disabling the person's own car from being driven); essentially forcing the person to leave the bar; and/or sending a message to the bar/bartender to cut the person off and stop providing any alcoholic beverages to the person. A particular impactful action might be a pre-recorded video of that person warning oneself that "YOU ARE NOT SOBER-GO HOME!", perhaps even connecting to Bluetooth speakers near the person to add a public self" sham ing" element. Other actions could involve bringing in 3.sup.rd parties to the "stopping" action, such as causing one or both of the parties' mothers to call or be called and put on speaker, creating a Skype/Facetime link, or even transmitting real-time audio/video of the current situation or a virtual reality/augmented reality simulation of the "direction" that the situation/context is trending towards. In such circumstances, the support network and interfaces chosen to facilitate the actions, particularly communication-intensive actions, are a part of the overall action selection/generation process as is the action itself." [0391]
Machine learning to update profile (ascertaining the equanimity signature) based on contexts ...
"Exemplary embodiments may not rely on an unchanging or fixed preidentified profile of risks, etc., but are configured for continually modifying/updating the profile and associated risks through a closed loop, machine-learning intensive, context-based, dynamic methods and mechanisms. There are various ways to perform the updates/modifications to the profile, e.g., including manually through prompting, etc. Exemplary embodiments are configured for dynamically (and both manually but particularly via automation) updating the profile and/or include system and process integration. As disclosed herein, exemplary embodiments are directed to preemption preemption of
risks, preemptions of behaviors that might lead to violations, preemption of other things that could result in an "official" or otherwise punitive warning/punishment. Exemplary embodiments may be configured to help or be on the side of the person, not the authorities-even to the extent of not alerting the authorities until it is absolutely necessary as a last resort without any other choice or option" [0453]
"The system may be configured to dynamically and adaptively determine the reward and/or the disincentive by using one or more of machine learning, a neural network, a quantum network, and/or an artificial intelligence. For example, the system may be configured to dynamically and adaptively determine the reward and/or the disincentive without requiring manual human intervention to create, define, manage, and administer the reward and/or the disincentive by using one or more of machine learning, a neural network, a quantum network, and/or an artificial intelligence." [0617]
It would have been obvious to one of ordinary skill in the art at the time of filing to include in the method and system of the combined references the ability to use machine learning to update user profiles as taught by Williams et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Williams et al. who teaches the benefits of using machine learning for stress situations and updating users profile.
Regarding claim 2 and 12
(claim 12) The method of claim 11, wherein the stress representation includes a psychological analysis.
Williams et al. teaches:
Psychotherapy (therefore psychological analysis)…
“Various types of addiction treatments, programs, and other methods for addressing addiction have been around for many decades. Examples include: 12-Step Programs such as Alcoholics Anonymous (AA), Acupuncture, Aversion Therapy (multiple forms), Behavioral Self-Control Training, Cognitive Therapy, Going Cold Turkey, Community Reinforcement, Diet-based Programs, Drug-based Treatments (multiple forms), Exercise-based programs, Hypnosis, Interventions, Meditation, Motivational programs, Nutrition-based programs, Rehabilitation (Inpatient and Outpatient)/Hospitalization stays, Religious-based programs, Self-Change Manuals/Guides, (Traditional) Psychotherapy (multiple forms), Spiritual Immersion, and Work/Treatment programs to name some of the more commonly-known approaches.” [0034]
“Such data sets and other information may be analyzed on multiple levels using analytics that include but are not limited to a Usage Trigger, Potential Response Analyzer, Risk Prediction Algorithms, and an Action/Coordination Engine (many of these concepts are illustrated in FIG. 1 in the Addict Data and Analytics, and used implicitly or explicitly in many of the Figures). The Usage Trigger and Potential Response Analyzer takes as inputs the addict's usage triggers (defined by techniques such as questionnaires or psycho-therapy), and the addict's personal profile and schedule/calendar, as well as historical information about the addict's movements, actions, behaviors, and hobbies, to develop a risk assessment and prediction algorithms about the addict's vulnerabilities to future potential addiction-related situations and develop a series of potential responses. Risk Prediction Algorithms utilize information about the addict's current situation/location/context, addiction triggers, and historical behavioral data to develop a risk score, rating, or level (score) for the addict. If the risk assessment score reaches or exceeds a threshold, and/or falls within a certain range, the Action Coordination Engine will develop an action or course of action that will then be launched, such as contacting members of the addict's support network, rearranging the addict's navigation (away from high risk locations), or disabling the addict's vehicle and arranging for alternative transportation, as a few examples.” [0105]
Regarding claim 3 and 13
(claim 13) The method of claim 11, wherein obtaining the stress representation further comprises retrieving a behavior pattern and obtaining the stress representation as a function of the behavior pattern.
Williams et al. teaches:
Recognizes (retrieving) when drinks (behavior pattern) when gets aggressive (stress representation)
“…A similar situation from a male perspective would be someone who recognizes that he gets aggressive when he drinks, and is afraid that he may cross a behavioral and/or socially acceptable line when interacting with others (particularly women). In one University of Minnesota study, as much as 70% of certain violent crimes (particularly rape) occur when the (male) criminal has been drinking, thus preempting/preventing unacceptable behavior (which