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
Applicant claims the benefit of US Provisional Application No. 63222077, filed July 15, 2021. Claims 1-17, 19-21, and 23 have been afforded the benefit of this filing date.
Applicant claims the benefit of US Provisional Application No. 63266070, filed December 28, 2021. Claims 1-17, 19-21, and 23 have been afforded the benefit of this filing date.
Receipt is acknowledged that application is a National Stage application of PCT PCT/CA2022/051096. Priority to 63222077 and 63266070 with a priority date of 7/14/2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 1/12/2024 has been considered and placed in the application file.
Claim Objections
Claim(s) 17 is objected to because of the following informalities:
Claim 17, line(s) 14, should be “plain language ” without the quotations
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17, 19-21, and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, and 16, Further claim 1 recites A system comprising:
a memory accessible to one or more microprocessors storing a database comprising a plurality of recommendations;
another memory accessible to the one or more microprocessors storing another database comprising user data relating to a set of users;
a further memory accessible to the one or more microprocessors storing computer executable instructions which when executed by the one or more microprocessors configure the one or more microprocessors to execute a process comprising: Ingest a recommendation;
process the ingested recommendation with at least a plurality of artificial intelligence (Al) processes and a plurality of machine learning (ML) processes;
generate a narrative from the processed ingested recommendation;
generate an expert commentary targeted to medical professionals;
generate at least one of a plain language recommendation and a plain language actionable message statement targeted to the medical professionals;
generate at least one of another plain language recommendation and another plain language actionable message statement targeted to non-medical professional users;
generate at least one of a local relevance question and a local truth question to present to each user accessing the ingested recommendation;
generate metadata for storage within one or more web servers for use in generating and rendering to a user the ingested recommendation upon an electronic device associated with the user.
The limitation of “process…” “generating…” , “generating…” , “generating…” , “generating…” , “generating…” , and “generating…” , as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person receiving a recommendation where he gives a narrative behind the recommendation. Additionally producing a fact based upon the recommendation for a doctor. Further producing it in plain language for the medical professional to understand as well as a non-medical professional. Further based on the recommendation generating question whether that be questioning the fact behind it or the relatability of the recommendation. Finally associating a meta data for it which could be a title, date/time, or a demographic.
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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements that are computer components “microprocessors” (paragraph 56) , “memory” (paragraphs 56), “another memory” (paragraph 56 ), “further memory ” (paragraph 297), “AI processes” (paragraph 47 ), and “ML processes” (paragraph 48) recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further “web server” would be intended use, post-solution activity by showing the result. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
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 integration of the abstract idea into a practical application, the additional element of using the computer components 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. The claims are not patent eligible.
Claims 2 additionally recite the system according to claim 1, wherein the narrative is a narrative of a plurality of narratives; and each narrative is generated in dependence upon a demographic set of a plurality of demographic sets. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the person having a plurality of narratives which could be for professionals or non-professionals or even based on demographics. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 3 additionally recites the system according to claim 1, wherein the narrative is a narrative of a plurality of narratives; each narrative is generated in dependence upon a demographic set of a plurality of demographic sets; and the plurality of demographic sets are defined by a classification of the ingested recommendation wherein at least one of an AI process of the plurality of AI processes and a ML process of the plurality of ML processes is a classification process. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the person having a plurality of narratives which could be for professionals or non-professionals or even based on demographics. Further classifying the recommendation based on demographics. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 4 additionally recites the system according to claim 1, wherein the narrative is a narrative of a plurality of narratives; each narrative is generated in dependence upon a demographic set of a plurality of demographic sets; and the demographic set of the plurality of demographic sets is established in dependence upon acquired location data relating to the user who is either a non-medical professional or a medical professional users[[; and]]. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the person having a plurality of narratives which could be for professionals or non-professionals or even based on demographics of the professional, non-professional. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 5 additionally recites The system according to claim 1, wherein the narrative is a narrative of a plurality of narratives; each narrative is generated in dependence upon a demographic set of a plurality of demographic sets; the demographic set of the plurality of demographic sets is established in dependence upon acquired location data relating to the user who is either a non-medical professional or a medical professional users; the user can view content relating to other users within their demographic set of the plurality of demographics sets; and the content comprises at least one of: recommendations viewed by the other users within their demographic set of the plurality of demographics sets; responses to local relevance questions presented to the other users within their demographic set of the plurality of demographics sets; and responses to local truth questions presented to the other users within their demographic set of the plurality of demographics sets. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the person having a plurality of narratives which could be for professionals or non-professionals or even based on demographics of the professional, non-professional. Additionally, where the person can view previous or other people’s recommendation based upon demographics. Which could be questions, facts, etc. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 6 additionally recites The system according to claim 1, wherein the narrative is a narrative of a plurality of narratives; each narrative is generated in dependence upon a demographic set of a plurality of demographic sets; the demographic set of the plurality of demographic sets is established in dependence upon acquired location data relating to the user who is either a non-medical professional or a medical professional users; and the computer executable instructions further configure the one or more microprocessors to establish at least one of: a social media network for the demographic set of the plurality of demographic sets; and a virtual environment accessible to the user and other users within the demographic set of the plurality of demographic sets; and a virtual environment accessible to the user and other users. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the person having a plurality of narratives which could be for professionals or non-professionals or even based on demographics of the professional, non-professional. Additionally, where the person can view previous or other people’s recommendation based upon demographics. Which could be questions, facts, etc. Thus, these claims are directed towards a mental process. In particular, the claim only recites additional elements that is “microprocessors” (Paragraph 56), “social media network” (paragraph 30), and “virtual environment” (paragraph 30), where they are general purpose. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claims 7 additionally recites the system according to claim 1, wherein the expert commentary is an expert commentary of a plurality of expert commentaries; and each expert commentary is generated in dependence upon a demographic set of a plurality of demographic sets of medical professionals. However, these limitations encompass a person giving an expert commentary to a medical professional in their demographic area based upon a recommendation he received. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 8 additionally recites The system according to claim 1, wherein the expert commentary is an expert commentary of a plurality of expert commentaries; each expert commentary is generated in dependence upon a demographic set of a plurality of demographic sets of medical professionals; and the plurality of demographic sets are defined by a classification of the ingested recommendation wherein at least one of an AI process of the plurality of AI processes and a ML process of the plurality of ML processes is a classification process. However, these limitations encompass a person giving an expert commentary to a medical professional in their demographic area based upon a recommendation he received. Further classifying the demographics. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 9 additionally recite the system according to claim 1, wherein the at least one of a plain language recommendation and a plain language actionable message statement is one of a plurality of plain language statements; and each plain language statement is generated in dependence upon a demographic set of a plurality of demographic sets of the medical professionals. However, these limitations encompass a person receiving a recommendation and generating a plurality of plain languages to a medical professional based upon there demographics. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 10 additionally recites The system according to claim 1, wherein the at least one of a plain language recommendation and a plain language actionable message statement is one of a plurality of plain language statements; and each plain language statement is generated in dependence upon a demographic set of a plurality of demographic sets of the medical professionals; and the plurality of demographic sets are defined by a classification of the ingested recommendation wherein at least one of an AI process of the plurality of AI processes and a ML process of the plurality of ML processes is a classification process. However, these limitations encompass a person receiving a recommendation and generating a plurality of plain languages to a medical professional based upon there demographics. Further classifying the recommendation based on demographics. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 11 additionally recites the system according to claim 1, wherein the at least one of the other plain language recommendations and the other plain language actionable message statement is one of a plurality of other plain language statements; and each other plain language statement is generated in dependence upon a demographic set of a plurality of demographic sets of the other users. However, these limitations encompass a person receiving a recommendation and based from the recommendation generations a plain language actionable message like giving a scheduled time to take medicine. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 12 additionally recites The system according to claim 1, wherein the at least one of the other plain language recommendation and the other plain language actionable message statement is one of a plurality of other plain language statements; and each other plain language statement is generated in dependence upon a demographic set of a plurality of demographic sets of the other users; and the plurality of demographic sets are defined by a classification of the ingested recommendation wherein at least one of an AI process of the plurality of AI processes and a ML process of the plurality of ML processes is a classification process. However, these limitations encompass a person receiving a recommendation and based from the recommendation generations a plain language actionable message like giving a scheduled time to take medicine based upon there demographics, further classifying them. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 13 additionally recites the system according to claim 1, wherein the at least one of a local relevance question and a local truth question is a query of a plurality of queries; and each query is generated in dependence upon a demographic set of a plurality of demographic sets of users. However, these limitations encompass a person receiving a recommendation and generating question based upon a demographic. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 14 additionally recites The system according to claim 1, wherein the at least one of the local relevance question and the local truth question is a query of a plurality of queries; and each query is generated in dependence upon a demographic set of a plurality of demographic sets of users; and the plurality of demographic sets are defined by a classification of the ingested recommendation wherein at least one of an AI process of the plurality of AI processes and a ML process of the plurality of ML processes is a classification process. However, these limitations encompass a person receiving a recommendation and generating question based upon a demographic further classifying them. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 15 additionally recites the system according to claim 1, wherein the metadata associated with an ingested recommendation comprises one or more elements selected from the group comprising: a mnemonic for the recommendation; a title of the recommendation; the source of the recommendation; a date and/or time of the recommendation being issued; an expiry of the recommendation; and demographic data associated with the recommendation. However, these limitations encompass a person receiving a recommendation and labeling it with a given title or associating a date with it. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 17 additionally recites [[A]]The method according to claim 16, further comprising: providing a user with credits, each credit associated with the user accessing and engaging within a software application with an ingested [[a]] recommendation; wherein the credits are fed back from the software application to at least one of a professional association associated with the user and an electronic medical record of the user; [[and]] each credit is established in dependence upon a response of the user with respect to at least one of the local relevance question and the local truth question associated with the recommendation; when the credits are fed back to the professional association the credits are employed either in establishing an accreditation of the user with the professional association or in completing a requirement for advancement of the user with the professional association; and when the credits are fed back to the electronic medical record of the user the credits are subsequently employed by a medical professional to establish a history that defines the user's background knowledge or experience with "plain language" recommendations. However, these limitations encompass a person receiving a recommendation and giving a response based upon the recommendation to receive accreditation from an association. Further keeping record of said credits. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 19 additionally recites [[A]]The method according to claim 16, further comprising: providing a user with credits, each credit associated with the user accessing and engaging within a software application a recommendation; wherein the credits are linked from the software application to another software application; and the user can establish at least one of: at least one of a financial reward, a financial discount, and a financial credit to employ in respect of purchasing at least one of a service and a product; and an option with respect of at least one of a service and a product which is only available to users exceeding a specific credit threshold. However, these limitations encompass a person receiving a recommendation and giving a response based upon the recommendation to receive accreditation from an association. Further keeping record of said credits. Finally, being able to accesses and redeem said credits for a reward. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 20, Further claim 20 recites A system comprising:
a memory accessible to one or more microprocessors storing a database comprising a plurality of recommendations;
another memory accessible to the one or more microprocessors storing another database comprising user data relating to a set of users;
a further memory accessible to the one or more microprocessors storing computer executable instructions which when executed by the one or more microprocessors configure the one or more microprocessors to execute a process comprising: managing documentation for each cluster of a plurality of clusters;
managing communications between users for each cluster of the plurality of users; and
establish one or more knowledge-based communities for each cluster of the plurality of clusters; wherein each cluster of the plurality of clusters is established in dependence upon at least one of:
geographic locations of each user of a plurality of users accessing the system; and
profiles of each user of the plurality of users accessing the systems.
The limitation of “managing …” “establish …” , “geographic …” , and “profiles …” , as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person receiving data. From this data the person organizes it into a plurality of groups and even associating groups with the users. He is also able to maintain communication between them and create a community based upon there demographics.
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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements that are computer components “microprocessors” (paragraph 56) , “memory” (paragraphs 56), “another memory” (paragraph 56 ), and “further memory ” (paragraph 297 ), “recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
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 integration of the abstract idea into a practical application, the additional element of using the computer components 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. The claims are not patent eligible.
Claim 21 additionally recites the system according to claim 20, wherein at least one of: each cluster of the plurality of clusters has different at least one of inferences, influences and knowledge; and each knowledge-based communities for each cluster of the plurality of clusters is established in dependence upon the profiles of each user within a subset of the plurality of users where the subset of the plurality of users are those users within the cluster of the plurality of clusters. However, these limitations encompass a person receiving data. From this data the person organizes it into a plurality of groups and even associating groups with the users. He is also able to maintain communication between them and create a community based upon there demographics. Where these communities have a certain knowledge base based upon there users. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 23 additionally The method according to claim 16[[22]], wherein the credits are fed back from the software application to at least one of a professional association associated with the user and an electronic medical record of the user; the credit is modified by a scaling; the scaling is one of a positive integer, a negative integer, a positive non-integer, a negative non- integer, and zero; and the scaling is established in dependence upon one or more factors selected from the group comprising: does the user answer the at least one of the local relevance question and the local truth question associated with the recommendation a question correctly or not; a degree of accuracy of the user's answer to the at least one of the local relevance question and the local truth question associated with the recommendation; a degree of accuracy of the user's answer to the at least one of the local relevance question and the local truth question associated with the recommendation where an answer comprises multiple elements; how many of at least one of the local relevance question and the local truth question associated with a plurality of recommendations the user answers; how quickly the user answers the at least one of the local relevance question and the local truth question associated with the recommendation; a subsequent repetition of the at least one of the local relevance question and the local truth question associated with the recommendation and adjusting the scaling based upon a degree of improvement or degradation in the accuracy of the responses from the user; and does the user skip the at least one of the local relevance question and the local truth question associated with the recommendation by default. However, these limitations encompass a person receiving a recommendation and questions. Based upon engaging the recommendation receiving credit. Where these credits can be scaled based upon how fast the user can answer the questions. From this data the person organizes it into a plurality of groups and even associating groups with the users. He is also able to maintain communication between them and create a community based upon there demographics. Where these communities have a certain knowledge base based upon there users. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-16, and 18 is rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US Patent US 20200321119 A1, (Neumann; Kenneth.).
Claim 1 and 16
Regarding Claim 1 and 16, Neumann teach
1. (Original) A system comprising: a memory accessible to one or more microprocessors storing a database comprising a plurality of recommendations;
(Fig 1 shows a system as a whole element 100
Paragraph 30 " Systems and methods are provided for machine-learning processing of heterogenous prognostic, ameliorative, and linguistic datasets. In an embodiment, a prognostic label learner may classify biological extraction data to prognostic labels. An ameliorative process label learner may classify prognostic labels to ameliorative processes. Prognostic label leaner and/or ameliorative process label learner are used to generate a diagnostic output using at least a biological extraction. An advisor module generates an advisory output using the diagnostic output; advisor module may receive additional inputs, which may trigger additional processing."
paragraph 31 "Referring now to the drawings, FIG. 1 illustrates an exemplary embodiment of an artificial intelligence advisory support system 100 for vibrant constitutional guidance. Artificial intelligence advisory system includes at least a server 104. At least a server 104 may include any computing device as described below in reference to FIG. 21, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described below in reference to FIG. 20. At least a server 104 may be housed with, may be incorporated in, or may incorporate one or more sensors of at least a sensor. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. At least a server 104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. At least a server 104 with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting a at least a server 104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. At least a server 104 may include but is not limited to, for example, a at least a server 104 or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. At least a server 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. At least a server 104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. At least a server 104 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of system 100 and/or computing device."
Paragraph 62 " Referring again to FIG. 3, diagnostic engine 108 and/or another device in diagnostic engine 108 may populate one or more fields in biological extraction database 300 using expert information, which may be extracted or retrieved from an expert knowledge database 304. An expert knowledge database 304 may include any data structure and/or data store suitable for use as a biological extraction database 300 as described above. Expert knowledge database 304 may include data entries reflecting one or more expert submissions of data such as may have been submitted according to any process described above in reference to FIG. 2, including without limitation by using first graphical user interface 212 and/or second graphical user interface 232. Expert knowledge database may include one or more fields generated by language processing module 216, such as without limitation fields extracted from one or more documents as described above. For instance, and without limitation, one or more categories of physiological data and/or related prognostic labels and/or categories of prognostic labels associated with an element of physiological state data as described above may be stored in generalized from in an expert knowledge database 304 and linked to, entered in, or associated with entries in a biological extraction database 300. Documents may be stored and/or retrieved by diagnostic engine 108 and/or language processing module 216 in and/or from a document database 308; document database 308 may include any data structure and/or data store suitable for use as biological extraction database 300 as described above. Documents in document database 308 may be linked to and/or retrieved using document identifiers such as URI and/or URL data, citation data, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which documents may be indexed and retrieved according to citation, subject matter, author, date, or the like as consistent with this disclosure.")
another memory accessible to the one or more microprocessors storing another database comprising user data relating to a set of users;
(Fig 16 shows a user database
Paragraph 134 "With continued reference to FIG. 16, diagnostic output may be utilized to generate an advisory output and/or specialized instruction set 1604. Diagnostic output may be utilized to generate at least an advisory output. For example, diagnostic output that contains depression may be utilized to generate at least an advisory output identifies a spiritual informed advisor to create a custom plan that includes meditation and deep breathing exercise. In yet another non-limiting example, a diagnostic output that includes obesity may be utilized to generate an advisory output that identifies a fitness professional informed advisor. Specialized instruction set 1604 may contain particular instructions for an informed advisor as described in more detail above in reference to FIG. 16. Specialized instruction set may be generated from diagnostic output and/or user database 1036. For example, a diagnostic output such as stage 4 brain cancer may contain a specialized instruction set containing information pertaining to such a diagnosis. In such an instance, specialized instruction set may contain biological extraction data such as a complete blood count (CBC) indicating abnormally high white blood cells. Specialized instruction set may contain medical records such as previous CT scans and/or MRIs performed that confirmed such a diagnosis. In yet another non-limiting example, a diagnostic output such as diabetes may result in a meeting with a nutritional informed advisor. In such an instance, nutritional professional may be provided with a specialized instruction set containing biological extraction data such as previous fasting blood sugar readings and/or medical records containing information pertinent to user's diabetes treatment such as current medications, previously tried medications, and/or supplements user may be currently taking.")
a further memory accessible to the one or more microprocessors storing computer executable instructions which when executed by the one or more microprocessors configure the one or more microprocessors to execute a process comprising:
(Fig 20 shows the execution process.
Paragraph 155 "FIG. 2100 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 2100 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 2100 includes a processor 2104 and a memory 2108 that communicate with each other, and with other components, via a bus 2112. Bus 2112 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures."
Paragraph 136 "With continued reference to FIG. 16, advisory output and/or specialized instruction set may be generated as a function of information contained within prognostic support network database 1608. In an embodiment, advisory output containing a treatment for strep throat for a user may not contain a recommendation of penicillin if information contained within prognostic support network database 1608 indicates that user has a severe allergic reaction to penicillin. In yet another embodiment, information contained within prognostic support network database 1608 may be utilized to guide transmission of specialized instruction set. For example, a user who resides in Denver, Colorado may have a preference for informed advisors within Denver, Aurora, and Lakewood. In such an instance, prognostic support network database 1608 may not transmit specialized instruction set to an informed advisor located in Boulder or Colorado Springs." )
ingest a recommendation; process the ingested recommendation with at least a plurality of artificial intelligence (Al) processes and a plurality of machine learning (ML) processes;
(Paragraph 5 “In another aspect, a method for an artificial intelligence support network for informed advisor guidance. The method includes receiving, by a diagnostic engine operating on a computing device, a biological extraction related to a user, said biological extraction comprising a self-assessment of the user. The method includes generating, by the diagnostic engine operating on the computer device, a diagnostic output as a function of the self-assessment of the user. The generating includes identifying, by a machine learning module operating on the computing device, a current condition of the user as a function of the self-assessment of the user and a first training set, said first training set including a plurality of data entries, each first data entry of the plurality of data entries including an element of physiological state data and a correlated first prognostic label. The generating also includes identifying, by the machine learning module operating on the computing device, an ameliorative output related to the current condition of the user as a function of the identified current condition of the user and a second training set, said second training set including a plurality of second data entries, each second data entry including a second prognostic label and a correlated ameliorative process label. The method includes selecting, by an advisor module operating on the computing device, an informed advisor as a function of the diagnostic output. The method includes generating, by an advisor module operating on the computing device, an advisory output as a function of the diagnostic output, said advisory output identifying the current condition of the user. The method includes transmitting, by the advisor module operating on the computing device, the advisory output to a client device associated with the selected informed advisor.”
Paragraph 53 " Still referring to FIG. 2, diagnostic engine 108 is designed and configured to receive a second training set 220 including a plurality of second data entries. Each second data entry of the second training set 220 includes at least a second prognostic label 224; at least a second prognostic label 224 may include any label suitable for use as at least a first prognostic label 208 as described above. Each second data entry of the second training set 220 includes at least an ameliorative process label 228 correlated with the at least a second prognostic label 224, where correlation may include any correlation suitable for correlation of at least a first prognostic label 208 to at least an element of physiological data as described above. As used herein, an ameliorative process label 228 is an identifier, which may include any form of identifier suitable for use as a prognostic label as described above, identifying a process that tends to improve a physical condition of a user, where a physical condition of a user may include, without limitation, any physical condition identifiable using a prognostic label. Ameliorative processes may include, without limitation, exercise programs, including amount, intensity, and/or types of exercise recommended. Ameliorative processes may include, without limitation, dietary or nutritional recommendations based on data including nutritional content, digestibility, or the like. Ameliorative processes may include one or more medical procedures. Ameliorative processes may include one or more physical, psychological, or other therapies. Ameliorative processes may include one or more medications. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various processes that may be used as ameliorative processes consistently with this disclosure."
Paragraph 4 “In an aspect, a system for an artificial intelligence support network for informed advisor guidance includes a computing device. The system includes a diagnostic engine. The diagnostic engine is configured to receive a biological extraction related to a user, said biological extraction comprising a self-assessment of the user, and to generate a diagnostic output as a function of the self-assessment of the user. The generating includes identifying, by a machine learning module operating on the computing device, a current condition of the user as a function of the self-assessment of the user and a first training set, said first training set including a plurality of data entries, each first data entry of the plurality of data entries including an element of physiological state data and a correlated first prognostic label. The generating also includes identifying, by the machine learning module operating on the computing device, an ameliorative output related to the current condition of the user as a function of the identified current condition of the user and a second training set, said second training set including a plurality of second data entries, each second data entry including a second prognostic label and a correlated ameliorative process label. The system includes an advisor module. The advisor module is configured to select an informed advisor as a function of the diagnostic output, generate an advisory output as a function of the diagnostic output, said advisory output identifying the current condition of the user, and transmit the advisory output to a client device associated with the selected informed advisor.”
paragraph 118 " With continued reference to FIG. 1, artificial advisory system 100 includes at least an advisor module 128 executing on the at least a server. At least an advisor module 128 may include any suitable hardware or software module. In an embodiment, at least an advisor module 128 is designed and configured to receive at least a request for an advisory input, generate at least an advisory output using the at least an advisory input and at least a diagnostic output, select at least an informed advisor as a function of the at least a request for an advisory input, and transmit the at least an advisory output to the at least a selected informed advisor. Informed advisors may together create a prognostic support network that functions to assist a user in achieving and maintaining a vibrant constitution as described in more detail below. Informed advisors may function to assist a user in achieving and maintaining a vibrant constitution by providing encouragement, support, mentorship, guidance, and/or services to a user. Prognostic support network may be composed of different categories of informed advisors based on a category of function and support that an informed advisor may be providing to a user. Informed advisors may include artificial intelligence informed advisors that may exchange messaging services and/or protocols with a user as described in more detail below. Informed advisors may include spiritual professionals such as a pastor, rabbi, Buddhist monk and the like. Informed advisors may include nutrition professionals such as a nutritionist, dietician, and chefs. Informed advisors may include fitness professionals such as personal trainers, sports coaches, group exercise instructors and the like. Informed advisors may include functional medicine professionals such as medical doctors, nurses, naturopathic doctors, nurse practitioners, and the like. Informed advisors may include a community of a user's family and friends. Informed advisors may include electronic behavior coaches and other miscellaneous informed advisors as described in more detail below. Diagnostic output may be linked to at least a selected informed advisor by at least an advisor module. Linking may include selecting at least an informed advisor as a function of the at least a diagnostic output. Linking may be learned using a machine learning process as described in more detail below. In an embodiment, linking may include at least a diagnostic output that is presented with at least an informed advisor. For example, at least a diagnostic output such as Celiac disease may be linked to at least an informed advisor consisting of a dietician who can provide inputs and advice as described in more detail below as to how best implement a gluten free diet. At least an advisor module may select at least an informed advisor by matching at least a request for an advisory input to a selected informed advisor as described in more detail below."
Paragraph 138 “With continued reference to FIG. 16, advisory support module 128 includes artificial intelligence module 1612. Artificial intelligence module 1612 may provide output to user client device 124 and/or advisor client device 132. Artificial intelligence module 1612 may receive inputs from user client device 124 and/or advisor client device 132. Inputs and/or outputs may be exchanged using messaging services and/or protocols, including without limitation any instant messaging protocols. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of a multiplicity of co