DISTRIBUTED DATA GATHERING AND RECOMMENDATION IN PHYTOTHERAPY

Prediction of the physiological effect of phytotherapeutic products and the recommendation of phytotherapeutic products on the basis of physiological effects are provided. A first product profile includes a plurality of compound identifiers and a plurality of concentrations. Observational data is read regarding a plurality of subjects who have consumed a first product substantially conforming with the first product profile. From the observational data a first set of physiological effects is determined associated with the first product profile. In some embodiments, a second set of physiological effects associated with a second product profile is determined based on the first product profile, the second product profile, and the first set of physiological effects. In other embodiments, a second product profile associated with a second set of physiological effects is determined based on the first product profile, the first set of physiological effects, and the second set of physiological effects.

FIELD

Embodiments of the present invention relate to distributed data gathering and recommendation in phytotherapy, and more specifically, to the prediction of the physiological effect of phytotherapeutic products and the recommendation of phytotherapeutic products on the basis of physiological effects.

BACKGROUND

There is currently a lack of data available with respect to the effects of various plant-based medicines, remedies or health-promoting agents. In fact, even widely used remedies may not have undergone substantial clinical testing. The lack of data is particularly acute with respect to historically controlled substances, such asCannabis.The effects of plant-based products may vary significantly between strains, between preparations, and between delivery routes. Likewise, combination products may have effects on a user that differ from standalone products. Personalized medicine has been closely studied in the pharmaceutical setting, but has received little attention in other fields of medicine. The complicated interplay of strain, combination, preparation, delivery route, and user attributes has not been adequately analyzed.

Although many plant-based products are used, there has not been a mechanism for collecting data regarding their effects or a mechanism for determining appropriate products for use by a user to achieve a desired effect. Thus, there remains a need in the art for systems and methods for distributed data gathering and recommendation in phytotherapy.

BRIEF SUMMARY

According to one aspect of the present invention, methods of and computer program products for distributed data gathering and recommendation in phytotherapy are provided. In certain embodiments, a first product profile is read. The first product profile includes a plurality of compound identifiers and a plurality of concentrations. Each of the plurality of compound identifiers identifies a compound. Each of the plurality of concentrations is associated with one of the plurality of compound identifiers. Observational data is read regarding a plurality of subjects. Each of the plurality of subjects has consumed a first product substantially conforming with the first product profile. Consumption may include any introduction of a product to a subject organism, whether through ingestion, inhalation, transdermal administration, or otherwise. From the observational data a first set of physiological effects is determined associated with the first product profile.

In some such embodiments, a second product profile is read. A second set of physiological effects associated with the second product profile is determined based on the first product profile, the second product profile, and the first set of physiological effects. Determining the second set of physiological effects may include training an artificial neural network using the first product profile and the first set of physiological effects and providing the second product profile to the artificial neural network.

In some such embodiments, a second set of physiological effects is read. A second product profile associated with the second set of physiological effects is determined based on the first product profile, the first set of physiological effects, and the second set of physiological effects. Determining the second product profile may include training an artificial neural network using the first product profile and the first set of physiological effects and providing the second set of physiological effects to the artificial neural network.

In some embodiments, characteristic data regarding the plurality of subjects is read. In some such embodiments, characteristic data regarding a target user is read. The second set of physiological effects associated with the second product profile may be determined based on the first product profile, the second product profile, the first set of physiological effects, and the characteristic data regarding the target user. Alternatively, a second product profile associated with the second set of physiological effects may be determined based on the first product profile, the first set of physiological effects, the second set of physiological effects, and the characteristic data regarding the target user.

In some embodiments, determining the second set of physiological effects comprises training an artificial neural network using the first product profile, the first set of physiological effects, and the characteristic data regarding the plurality of subjects and providing the second product profile and the characteristic data regarding the target user to the artificial neural network. Determining the second product profile may comprise training an artificial neural network using the first product profile, the first set of physiological effects, and the characteristic data regarding the plurality of subjects and providing the second set of physiological effects and the characteristic data regarding the target user to the artificial neural network.

In various embodiments, the first and second product profiles each contain at least one common compound identifier.

In various embodiments, the artificial neural network comprises a feedforward neural network, a radial basis function network, a self-organizing map, learning vector quantization, a recurrent neural network, a Hopfield network, a Boltzmann machine, an echo state network, long short term memory, a bi-directional recurrent neural network, a hierarchical recurrent neural network, a stochastic neural network, a modular neural network, an associative neural network, a deep neural network, a deep belief network, a convolutional neural networks, a convolutional deep belief network, a large memory storage and retrieval neural network, a deep Boltzmann machine, a deep stacking network, a tensor deep stacking network, a spike and slab restricted Boltzmann machine, a compound hierarchical-deep model, a deep coding network, a multilayer kernel machine, or a deep Q-network.

In various embodiments, the artificial neural network comprises a processor, a field-programmable gate array, an adaptive neuromorphic processor, a memory network, a long short-term memory, or a memristor.

In various embodiments, the method further comprises determining at least one association between one of the plurality of compound identifiers and one of the first set of physiological effects.

In various embodiments, the characteristic data comprise a genotypic profile, a metabolic profile, a proteomic profile, a lipomic profile, or a microbiomic profile.

In various embodiments, the characteristic data comprise a disease diagnosis. In some embodiments, the disease is Parkinson's disease, cancer, glaucoma,Cannabiswithdrawal syndrome, multiple sclerosis, or lupus.

In various embodiments, the characteristic data comprise a symptom. In some embodiments, the symptom comprises nausea, seizure, insomnia, lack of appetite, weight loss, depression, or narcolepsy.

In various embodiments, the first product profile further comprises a delivery route. In some embodiments, the delivery route comprises ingestion, inhalation, or transdermal administration.

In various embodiments, at least one of the plurality of compound identifiers identifies a isoprenoid or a terpene. In some embodiments, the isoprenoid is a cannabinoid.

In various embodiments, the method further comprises providing a survey to the plurality of subjects; receiving a plurality of survey responses from the plurality of subjects; and compiling the observational data regarding the plurality of subjects from the plurality of survey responses. In some embodiments, the survey includes a plurality of questions. In some embodiments, one of the plurality of questions is directed to a feeling of relaxation, nausea, alertness, well-being, or intoxication.

In various embodiments, the characteristic data comprise heart rate, tremor strength, or tremor frequency.

In various embodiments, the first product profile corresponds to a plant hybrid.

In various embodiments, at least one of the plurality of compound identifiers corresponds to a plant. In some embodiments, the at least one of the plurality of compound identifiers comprises taxonomic information of the plant, proteomic information of the plant, lipomic information of the plant, or genotypic information of the plant.

In various embodiments, the second product profile corresponds to a plant hybrid. In some embodiments, the plant hybrid is created by breeding or genetic modification.

In various embodiments, the characteristic data regarding the target user comprise a genotypic profile, a metabolic profile, a proteomic profile, a lipomic profile, or a microbiomic profile. In some embodiments, the characteristic data regarding the target user comprise a disease diagnosis. In some embodiments, the disease is Parkinson's disease, cancer, glaucoma,Cannabiswithdrawal syndrome, multiple sclerosis, or lupus.

In various embodiments, the characteristic data regarding the target user comprise a symptom. In some embodiments, the symptom comprises nausea, seizure, insomnia, lack of appetite, weight loss, depression, or narcolepsy.

In various embodiments, the method further comprises receiving the second set of physiological effects from a user via a user interface.

In various embodiments, consumption by each of the plurality of subjects comprises ingestion, inhalation, or transdermal administration.

In some embodiments, the first product profile corresponds to a plant extract. In some embodiments, the first product profile comprises supplemental information regarding the plant, the supplemental information comprising growth conditions, growth procedures, strain conditions, harvesting conditions, harvesting procedures, drying conditions, drying procedures, processing conditions, processing procedures, extraction conditions, extraction procedures, storage conditions, or storage procedures.

In various embodiments, reading observational data comprises reading sensor data from a sensor. In some embodiments, the sensor is a biometric sensor. In some embodiments, the sensor comprises an accelerometer, a camera, a microphone, a blood pressure sensor, an electroencephalograph, or a heart rate sensor. In some embodiments, the sensor is embedded in a wearable device. In some embodiments, the sensor is embedded in a smartphone.

In various embodiments, the method further comprises providing a cognitive test to the plurality of subjects; receiving a plurality of cognitive data from the plurality of subjects; and compiling the observational data regarding the plurality of subjects from the plurality of cognitive data.

In various embodiments, the method further comprises providing a game to the plurality of subjects; receiving a plurality of performance data from the plurality of subjects; and compiling the observational data regarding the plurality of subjects from the plurality of performance data.

DETAILED DESCRIPTION

According to various embodiments of the present disclosure, systems, methods and computer program products are provided for distributed data gathering and recommendation in phytotherapy. By collecting data from users of various products and analyzing that data in light of the product profiles, recommendations are provided to users as to the most appropriate product for a desired effect.

With reference now toFIG. 1, a system100according to embodiments of the present disclosure is depicted. A data store101contains a plurality of product profiles102. In some embodiments, each product profile102includes at least one compound identifier103and a concentration104. In various embodiments, compound identifier103may be a chemical name (e.g., tetrahydrocannabinol), chemical formula (e.g., C21H30O2), common name of a plant (e.g.,Cannabis), plant species (e.g.,Cannabis indica), or other identifying information of a plant strain (e.g., Purple Kush). In various embodiments, concentration104is a measure of mass concentration, molar concentration, or volume concentration of the compound identified by compound identifier103within a product conforming to product profile102. In some embodiments, the product profile further includes a delivery route. The delivery route may include ingestion or inhalation. In some embodiments, product profile102further includes additional information regarding some or all of the included compounds. For example, where a compound identifier corresponds to a plant variety, supplemental information regarding its cultivation or handling may be included. Such information may include growth conditions and procedures (including, e.g., strain conditions), harvesting conditions and procedures, drying conditions and procedures, processing conditions and procedures, extraction conditions and procedures, or storage conditions and procedures.

In some embodiments, each product profile102is further linked to at least one effect105. In some embodiments, effect105is associated with occurrence data106. In this way, the effects associated with a product conforming to product profile102are tracked. In some embodiments, effect105is determined from observational data regarding subjects that have consumed a product conforming with product profile102. In some embodiments, occurrence data106includes a percentage of users observing the associated effect. In some embodiments, occurrence data106includes characteristic data of subjects.

As set forth below, a product profile may serve both a descriptive and a prescriptive function. In particular, a product profile may describe a preexisting product being analyzed. A product profile may also be generated by the systems and methods provided for herein and prescribe a product to be made. In some cases, one or more constituents of a product profile may themselves correspond to a product profile. In this manner, the systems and methods of the present disclosure are suitable for analysis of products at various levels of granularity. Adopting the above example, a product profile corresponding to a plant strain might specify the percent by mass of tetrahydrocannabinol. A product profile corresponding to a consumable product containing that plant strain might specify the percent by mass of the plant strain. A product profile corresponding to an extract of that plant strain might specify the percent by mass of the plant strain in the extract.

In some embodiments, artificial neural network107is operatively connected to data store101. As set forth further below, product profile data, effect data, and data regarding subjects is fed into neural network107both to train neural network107and to provide recommendations and predictions regarding products. It will be appreciated that artificial neural network107may be resident on the same or different computing node as data store101. In addition, it will be appreciated that various types of artificial neural network are suitable for use according to embodiments of the present disclosure.

Suitable artificial neural networks include but are not limited to a feedforward neural network, a radial basis function network, a self-organizing map, learning vector quantization, a recurrent neural network, a Hopfield network, a Boltzmann machine, an echo state network, long short term memory, a bi-directional recurrent neural network, a hierarchical recurrent neural network, a stochastic neural network, a modular neural network, an associative neural network, a deep neural network, a deep belief network, a convolutional neural networks, a convolutional deep belief network, a large memory storage and retrieval neural network, a deep Boltzmann machine, a deep stacking network, a tensor deep stacking network, a spike and slab restricted Boltzmann machine, a compound hierarchical-deep model, a deep coding network, a multilayer kernel machine, or a deep Q-network.

In some embodiments, artificial neural network107is implemented in software. In some embodiments, artificial neural network107is implemented with custom hardware such as a processor, a field-programmable gate array, an adaptive neuromorphic processor, a memory network, a long short-term memory, or a memristor.

In some embodiments, user interface108is provided. User interface108may be a web-based user interface or a native user interface operating on a mobile device or a desktop device in various embodiments. In general, any computing node equipped with a display is suitable for user interface108according to the present disclosure. As set forth further below, user interface108is adapted to provide a survey to a user in order to collect both characteristic information regarding that user, and observational data regarding that user's experiences. In some embodiments, a user is queried through the user interface for effects experienced after consuming a product conforming to a product profile. Once effect data is collected, it is stored in data store101. In some embodiments, the raw data is stored in data store101. In some embodiments, aggregate data is stored pertaining to each product profile102.

User interface108may include a variety of tools for collection of user data. As noted above, in some embodiments, user interface108includes a survey. In some embodiments, user interface108includes a testing component, for example a cognitive test or challenge for a user to complete. For example, typing speed may be measured while a user completes a survey through user interface108. Additional exemplary tests include measuring the relative time to input a password, whether already existing in an external application, or established by the user for this purpose. Such password tests may collect, for example, relative rate on input, and number of failures. In yet other embodiments, user interface108includes a game, a user's performance of which is suitable for determining cognitive ability or dexterity.

As noted above, user interface108may be displayed by any computing node having a display. In some embodiments, the computing node displaying user interface108communicates through a network interface109over a network110. In some embodiments, network110is the Internet, however, network110may include a cellular network, a radio network, a LAN, a WAN, or any number of other data networks known in the art. It will be appreciated that user interface108, artificial neural network107and data store101may be spread across one or many computing nodes, which may be in communication with each other through network110.

In embodiments with user interface108, it is further adapted for a user to indicate a set of preferred effects and to receive from data store101or artificial neural network107a recommendation based on that set of preferred effects.

In some embodiments, one or more sensor111is provided. Sensor111may include an accelerometer, a camera, a microphone, a blood pressure sensor, a heart rate sensor, or other sensor known in the art. Such sensor111is configured to collect data from the user, either on an ongoing basis or solely at the time of user interaction with user interface108. In some embodiments, sensor111is embedded in a wearable device, such as an existing fitness tracker, smartphone, sensor equipped cane, augmented reality glasses, or other devices known in the art. In some embodiments, sensor111is coupled to the same computing node as hosts under interface108. In other embodiments, sensor111separately transmits data via network interface109. In exemplary embodiments, a camera is used to measure pupil dilation. In other exemplary embodiments, an accelerometer is used to measure shake or balance of a user. In yet other exemplary embodiments, a microphone is used to record speech, which is then analyzed for speed, slurring, or changes in speech pattern.

Referring now toFIG. 2, a method of training a neural network according to embodiments of the present disclosure is illustrated. An artificial neural network is instantiated201. In some embodiments, the artificial neural network comprises a input layer, one or more hidden layers, and an output layer. However, it will be appreciated that various artificial neural network configurations are suitable for use according to the present disclosure. Training data are provided202to the artificial neural network. In some embodiments, training data include a product profile. A product profile corresponds to a product that may be consumed by a user. The product profile includes compound identifiers and corresponding concentrations. In this way, a product identifier provides the information necessary to characterize a product. As a simple example, a product identifier for bread might indicate 28% water by volume, 71% flour by volume, and 1% yeast by volume. In some embodiments, training data also include a set of physiological effects associated with the product profile.

In some embodiments, the physiological effects associated with the product profile are determined from observational data regarding a plurality of subjects. The observational data may include a user's assessment of relaxation, nausea, alertness, well-being, or intoxication. In some embodiments, the user's assessment is collected through a user interface. For example, a user may be queried as to a numeric rating for one or more of relaxation, nausea, alertness, well-being, or intoxication.

Based on the training data, the neural network is updated203. It will be appreciated that a variety of methods are suitable for each of the artificial neural networks according to the present disclosure. For example, back propagation may be used to train the neural network. In certain such embodiments, a product identifier is provided at the input layer of the neural network, and a result is determined from the output layer of the neural network. The physiological effects associated with the product profile are compared with the output of the neural network, and corrections are propagated back up through the hidden layers of the neural network to update it. However, in other embodiments, the input and outputs are reversed. Thus, the physiological effects are provided to the input layer as part of the training process while the product identifier is determined from the output layer. It will be appreciated that certain artificial neural networks are bidirectional, and thus input may be provided to the output layer with results being determined at the input layer.

In some embodiments, training data further include characteristic data for a user. In various embodiments, such characteristic data include a genotypic profile, a metabolic profile, a proteomic profile, a lipomic profile, or a microbiomic profile. In some embodiments, the characteristic data include a disease profile. In some such embodiments, the disease profile includes a diagnosis of a disease, which may include one or more of Parkinson's disease, cancer, glaucoma,Cannabiswithdrawal syndrome, multiple sclerosis, or lupus.

In some embodiments, training data include a delivery route. The delivery route may include ingestion, inhalation, or transdermal administration.

Referring now toFIGS. 3A-B, inputs and outputs of an artificial neural network are illustrated according to embodiments of the present disclosure. InFIG. 3A, a product profile301and optional user characteristics302are provided to trained artificial neural network303. Physiological effects304are obtained as output. The arrangement ofFIG. 3Ais suitable for determining a set of physiological effects304based on a product profile and optional user characteristics. For example, the effects of a previously unknown product can be predicted. InFIG. 3B, physiological effects304and optional user characteristics302are provided to trained artificial neural network303. Product profile301is obtained as output. The arrangement ofFIG. 3Bis suitable for determining a product on the basis of desired physiological effects. For example, a previously unknown product can be generated that is likely to have the input effects. As discussed above, the training process entails applying known training data to neural network303with the same arrangement of inputs and outputs.

Referring now toFIG. 4, a method of predicting the effects of a product according to embodiments of the present disclosure is illustrated. A first product profile is read401. As described above with reference toFIG. 1, the product profile includes compound identifiers and associated concentrations. Observational data regarding subjects is read402. The observational data pertain to subjects that have consumed a product conforming with the first product profile. From the observational data, first physiological effects associated with the first product profile are determined403. In some embodiments, determining the physiological effects includes analyzing user responses to a survey. For example, users may be queried through a user interface to rate the occurrence of a plurality of effects after consuming a product conforming with the first product profile. In some embodiments, only the most frequently occurring effects are considered. In other embodiments, all reported effects are considered. In yet other embodiments, a determination is made as to statistical significance, and only statistically significant effects are considered.

A second product profile is read404. A second set of physiological effects is determined405. The second set of physiological effects is associated with the second product profile, and is determined based on the first product profile, the second product profile, and the first set of physiological effects. In some embodiments, this determination is made using an artificial neural network as described above. For example, this determination may be made by training an artificial neural network using the first product profile and the first set of physiological effects before providing the second profile to the artificial neural network to obtain the second set of physiological effects.

Referring now toFIG. 5, a method of recommending a product according to embodiments of the present disclosure is illustrated. A first product profile is read501. As described above with reference toFIG. 1, the product profile includes compound identifiers and associated concentrations. Observational data regarding subjects is read502. The observational data pertain to subjects that have consumed a product conforming with the first product profile. From the observational data, first physiological effects associated with the first product profile are determined503. In some embodiments, determining the physiological effects includes analyzing user responses to a survey. For example, users may be queried through a user interface to rate the occurrence of a plurality of effects after consuming a product conforming with the first product profile. In some embodiments, only the most frequently occurring effects are considered. In other embodiments, all reported effects are considered. In yet other embodiments, a determination is made as to statistical significance, and only statistically significant effects are considered.

A second set of physiological effects is read504. A second product profile is determined505. The second product profile is associated with the second set of physiological effects, and is determined based on the first product profile, the first set of physiological effects, and the second set of physiological effects. In some embodiments, this determination is made using an artificial neural network as described above. In certain such embodiments, this determination is made by training an artificial neural network using the first product profile and the first set of physiological effects before providing the second set of physiological effects to the artificial neural network to obtain the second product profile.

In some embodiments, a product is selected506from a set of predetermined products based on the determined product profile. In some embodiments, a product is selected that substantially conforms with the product profile. However, in some embodiments, where a substantially conforming product is not available, the closest available product is selected.

In some embodiments, the product profiles correspond to plant varieties. For example, a product profile might indicate 94%Cannabis indicaand 6%Cannabis sativa,corresponding to a particular hybrid. In certain such embodiments, the second product profile is indicative of a hybrid that is predicted to have the input physiological effects. Such a hybrid may then be created. Creation of a hybrid may be through breeding or through genetic modification. It will be appreciated once an indicated hybrid has been consumed, the observed effects may be used to further refine artificial neural networks according to the present disclosure.

Bus18represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. Computer system/server12typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server12, and it includes both volatile and non-volatile media, removable and non-removable media.

The embodiments disclosed herein may be implemented as a system, a method, and/or a computer program product. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present invention.