Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
2. Claims 1-26 are currently pending in this case and have been examined and addressed below.
Claim Objections
The claim is objected to because of the following informalities:
Claim 19 recite “the moderator engine is connected to provide feedback” – the limitation should read “the moderator engine is configured to provide feedback” as the claims do not recite the moderator engine being connected to anything.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a data acquisition engine (claims 1-26) configured to receive multiple input data streams
a moderator engine (claims 18-20) configured to moderate the artificial intelligence model with regard to automatic generation of the recommendation for the patient
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The disclosure provides in [0024] and [0053] that the data acquisition engine 103 includes a network interface card (NIC) to provide for de-packetization and extraction of data incoming to the PGS 100…the data acquisition engine 103 implements a data filtering system…implements machine learning to analyze big data that is collected from a population of patients that may have characteristics similar to those of the patient 101. Therefore, the claim limitations will be interpreted as a hardware component and a system comprised of machine learning. Additionally, the specification discloses in [0040-0041] that the moderator engine 111 is configured to operate in either an autonomous mode, a manual mode, or a hybrid mode. In the autonomous mode, the moderator engine 111 operates to automatically ensure that the output generated by the deep learning engine 105 is in compliance with a profile and/or preferences specified by the patient…in the manual mode, the moderator engine 111 provides the output generated by the deep learning engine 105 to a moderator portal (e.g., implemented as a graphical user interface)… in the hybrid mode, the moderator engine 111 applies a probabilistic confidence assessment to determine a confidence level that the output generated by the deep learning engine 105 is appropriate for conveyance to the patient 101. If the determined confidence level for a given output generated by the deep learning engine 105 meets or exceeds a specified confidence level threshold value, the PGS 100 operates to automatically convey the given output generated by the deep learning engine 105 to the patient 101. However, if the determined confidence level for a given output generated by the deep learning engine 105 is less than the specified confidence level threshold value, the PGS 100 operates to quarantine the given output generated by the deep learning engine 105 for manual moderation, such as through the above-mentioned moderator portal. Therefore, the claim limitations will be interpreted a portal comprised of a graphical user interface and program that assesses the probabilistic confidence.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 – 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-26 are drawn to a system. As such, claims 1-26 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: A patient guidance system, comprising:
a data acquisition engine configured to receive multiple input data streams, wherein the multiple input data streams include a stream of current medical data for a patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient;
an artificial intelligence model configured to automatically generate a recommendation for the patient in real-time based on the multiple input data streams;
and an output processor configured to convey the recommendation to the patient.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, receiving data, generating a recommendation, conveying the recommendation, providing feedback, displaying the recommendation, identifying a condition, identifying an action, and identifying information are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to the current health condition.
Claims 3 is directed to the current medical data which includes current body temp, current heart rate, current respiration rate, current blood pressure, fetal heart rate, blood oxygen saturation level, or an electrocardiogram.
Claim 4 is directed to current medical data which includes a current bodyweight and current body measurements.
Claim 5 is directed to current medical data which includes a current medical diagnosis.
Claim 6 is directed to current medical data which includes a current image of body parts.
Claim 7 is directed to the stream of current situational data which includes a current location.
Claim 8 is directed to the stream of current situational data which includes a current listing of calendared events.
Claim 9 is directed to the stream of current situational data which includes a current daily schedule.
Claim 10 is directed to the stream of current situational data which includes an activity being performed.
Claim 11 is directed to the stream of current environmental characterization data which includes an outdoor temperature value, a humidity value, a barometric pressure value, an air quality index value, a value for particulate matter sized at less than or equal to about 2.5 micrometers, a heat index value, a wind speed value, a wind direction, a visibility distance value, or an insect/animal vector distribution.
Claim 12 is directed to the stream of current environmental characterization data which includes air quality measurements within a current vicinity.
Claim 13 is directed to the stream of current environmental characterization data which includes air quality measurements along an anticipated travel route.
Claim 14 is directed to case data being used for training.
Claim 15 is directed to support bi-directional communication.
Claim 16 is directed to articulating the recommendation.
Claim 18 is directed to moderate the automatic generation of the recommendation, receive a current profile that specifies preferences, and ensure the recommendation is conveyed.
Claim 19 is directed to provide feedback.
Claim 20 is directed to the preferences which includes budget sensitivity, time restrictions, sleep patterns, dietary preferences, meal times, exercise preferences, entertainment preferences, working hours, work location, travel preferences, travel times, communication preferences, restaurant preferences, grocer preferences, or wellness provider preferences.
Claim 21 is directed to connecting data from the multiple data streams.
Claim 22 is directed to display the recommendation.
Claim 23 is directed to provide bi-directional communication.
Claim 24 is directed to automatically identify a condition or situation that will have an adverse impact when left unmitigated and suggest a recommendation that will mitigate the condition or situation.
Claim 25 is directed to automatically identify an action that will have a beneficial impact when performed and generate a recommendation encourage the action.
Claim 26 is directed to automatically identify information for conveyance and generate a recommendation to convey the information.
Each of these steps of the preceding dependent claims 2-26 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Independent claim 1 recites the use of a data acquisition engine, in this case to receive multiple input data streams. The claim also recites the use of an output processor, in this case to convey the recommendation. The data acquisition engine and the output processor are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)). Independent claim 1 further recites the use of an artificial intelligence model, in this case to automatically generate a recommendation, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 14 recites the use of an artificial intelligence model, in this case to be trained based on case data, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 15 recites the use of a natural language processor, in this case to support bi-directional communication, only recites the natural language processor as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 16 recites the use of a natural language processor, in this case to articulate the recommendation, only recites the natural language processor as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 17 recites the use of a natural language processor and an artificial intelligence model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 18 recites the use of a moderator engine, in this case to moderate in regard to automatic generation of the recommendation and ensure the recommendation is conveyed. The claim also recites the use of a data acquisition engine, in this case to receive a current profile that specifies preferences. Additionally, claim 18 recites the use of an output processor, in this case to convey the recommendation. The moderator engine, data acquisition engine, and the output processor are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)). Dependent Claim 18 further recites the use of an artificial intelligence model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 19 recites the use of a moderator engine, in this case to provide feedback, only recites the moderator engine as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)). The claim further recites the use of an artificial intelligence model, in this case to receive feedback, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 21 recites the use of a data acquisition engine, in this case to data connection, only recites the data acquisition engine as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Dependent Claim 22 recites the use of a graphical user interface and a computing system, in this case to display the recommendation in real-time, only recites the graphical user interface and computing system as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Dependent Claim 24 recites the use of an artificial intelligence model, in this case to automatically identify a condition or situation that will have an adverse impact if left unmitigated, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 25 recites the use of an artificial intelligence model, in this case to automatically identify an action that will have a beneficial impact, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Dependent Claim 26 recites the use of an artificial intelligence model, in this case to automatically identify information for conveyance, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the data acquisition engine, artificial intelligence model, output processor, natural language processor, moderator engine, graphical user interface, and computing system in independent claim 1 and dependent claims 2-26 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
This conclusion is based on a factual determination. Applicant’s own disclosure in paragraph [0053] acknowledges that the “data acquisition engine 103 implements a data filtering system that functions to filter data within the multiple input data streams 151-1 to 151-N to identify specific data relevant to the patient 101…the data acquisition engine 103 implements machine learning to analyze big data that is collected from a population of patients that may have characteristics similar to those of the patient 101”. Paragraph [0024] acknowledges that the “AI model(s) 107 to provide AI-based predictive analysis of cause-and-effect probabilistic correlations that are embedded (and often hidden) within the multiple input data streams 151-1 to 151-N… The AI model(s) 107 are trained by a cumulative pool of input data amassed over time from a large population of patients”. Paragraph [0025] and [0076] acknowledges that “the natural language processor 109 is implemented by an artificial intelligence model”. Furthermore, in paragraph [0052] the disclosure acknowledges that “the output processor 113 is defined to prepare and transmit the recommendations, coaching, and/or information for the patient 101, as generated by the PGS 100, within data packets over the cloud network 190 to the personal data communication device 102 of the patient 101. In these embodiments, the data packets are prepared by the output processor 113 in accordance with any known and available network communication protocol. In some embodiments, the output processor 113 includes a NIC to provide for packetization of outgoing data to be transmitted from the PGS 100. In some embodiments, the output processor is configured to communicate the output of the PGS 100 and the associated input data to a general data pool 116, as indicated by arrow 117. In some embodiments, the general data pool 116 is maintained within one or more computer readable media in a storage server system of the cloud network 190. However, in various embodiments, the general data pool 116 can be maintained within one or more computer readable media anywhere that is accessible by the PGS 100. Also, in some embodiments, the output processor 113 is configured to communicate information from the PGS 100 to any one or more of the data sources associated with the multiple input data streams 151-1 to 151-N, by way of the cloud network 190”. Paragraph [0040] acknowledges that the “moderator engine 111 is configured to operate in either an autonomous mode, a manual mode, or a hybrid mode… the moderator engine 111 provides the output generated by the deep learning engine 105 to a moderator portal (e.g., implemented as a graphical user interface) through which the output generated by the deep learning engine 105 can be reviewed and either approved or rejected by a human moderator before it is conveyed outside of the PGS 100 to the patient 101”. Additionally, paragraph [0036] discloses that the “the PGS 100 interfaces with one or more other data processing/computing systems that have information relative to the patient 101. For example, in some embodiments, the PGS 100 interfaces with one or more of a home security system, a remote monitoring camera system, a home automation system, an automobile, a remote patient monitoring device, a medical device, an in-home air monitoring device, a wearable air monitoring device, an in-home appliance, an environment control system (e.g., thermostat, humidifier, de-humidifier, air filter, etc.), among essentially any other device/system that is associated with the patient 101 and that is capable of data communication with the data acquisition system 103 of the PGS 100”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-26 are not eligible subject matter under 35 USC 101.
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 1-26 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-26 of copending Application No. 18/307,757 in view of Bitran et al. (US 20170039344 A1)[hereinafter Bitran]. The examined application claims are based on the patient guidance system, while the copending application is based on a method for the claimed invention. Therefore, the examined claims are narrower than the copending application claims. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
For reference, the following table matches limitations of method claim 1 of the copending Application No. 18/307,757 with similar limitation of the system claim 1 of the current examined Application No. 18/307,752:
Claim 1 of Copending Application 18/307,757
Claim 1 of Examined Application 18/307,752
A method for automatically providing guidance to a patient in real-time, comprising: receiving multiple input data streams,
A patient guidance system, comprising: a data acquisition engine configured to receive multiple input data streams,
wherein the multiple input data streams include a stream of current medical data for a patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient;
wherein the multiple input data streams include a stream of current medical data for a patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient;
executing an artificial intelligence model to automatically generate a recommendation for the patient in real-time based on the multiple input data streams;
an artificial intelligence model configured to automatically generate a recommendation for the patient in real-time based on the multiple input data streams;
and conveying the recommendation to the patient
and an output processor configured to convey the recommendation to the patient.
Claim 1 of copending Application 18/307,757 recites the limitations above, however claim 1 lacks a data acquisition engine, an artificial intelligence model and an output processor. Bitran discloses in paragraphs [0017] and [0019-0020] and [0022-0023] the personal assistant interpretation engine (referring to the data acquisition engine) receives user, synonymous to patient, data. Bitran further discloses in paragraphs [0035] and [0037] a Bayesian machine-learning algorithm (referring to the artificial intelligence model) determines a health recommendation based on user data. Additionally, in paragraphs [0035] Bitran discloses a health recommender (referring to the output processor) outputs the health recommendation to the computing device of the patient. It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to have modified the method of providing guidance to a patient to include the data acquisition engine, artificial intelligence model, and output processor for the purpose of improving the health and well-being of individuals by providing recommendations on resources.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-14, and 21-26 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Bitran et al. (US 20170039344 A1)[hereinafter Bitran].
As per Claim 1, Bitran discloses a patient guidance system in paragraphs [0003] and [0015] (computing system (referring to the patient guidance system) that includes a health recommender (Examiner notes that the health recommender provides guidance to the patient in regards to treating a health condition)), comprising: a data acquisition engine configured to receive multiple input data streams, wherein the multiple input data streams include a stream of current medical data for a patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient in paragraphs [0017-0020] and [0022-0023] (the personal assistant interpretation engine (referring to the data acquisition engine) receives user, synonymous to patient, data, wherein user data includes medical data which includes the user's electronic medical record that consists of the preexisting medical conditions of the user, geolocation data (referring to the current situational data of the patient), and non-medical data (referring to the current environmental characterization data relevant to the patient), wherein the non-medical data includes weather data, allergen concentration, pathogen concentration, UV index, and air quality); an artificial intelligence model configured to automatically generate a recommendation for the patient in real-time based on the multiple input data streams in paragraphs [0035] and [0037] (a Bayesian machine-learning algorithm (referring to the artificial intelligence model) determines a health recommendation based on user's electronic medical record, identified health condition, time and location-based data, and health insurance information, wherein these all come from the user data); and an output processor configured to convey the recommendation to the patient in paragraphs [0035] and [0055] (a health recommender (referring to the output processor) outputs the health recommendation to a display associated with the computing device of the user).
As per Claim 2, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the current health condition of the patient is one or more of a woman trying to conceive, a woman that is currently pregnant, and a woman that is within two years postpartum in paragraphs [0035-0036] (the health condition of the user, wherein the health condition determines the type of recommendation, may include women that are pregnant (Examiner notes that women who are pregnant meets the one or more limitations of the current health condition)).
As per Claim 3, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the current medical data for the patient includes one or more of a current body temperature, a current heart rate, a current respiration rate, a current blood pressure, a fetal heart rate, a blood oxygen saturation level, and an electrocardiogram in paragraphs [0019] (the medical data may comprise of user's electronic medical record, biometric data, wherein biometric data includes heart rate, blood pressure, and body temperature, and medical device data (Examiner notes that the heart rate, blood pressure, and body temperature meets the one or more limitations of medical data)).
As per Claim 5, Bitran discloses the patient guidance system as recited in claim 3, Bitran also discloses wherein the current medical data for the patient includes a current medical diagnosis in paragraphs [0023] and [0027] and [0034-0037] (the user's electronic medical record includes current medications, allergies, preexisting medical conditions, wherein the medical conditions can describe diseases and syndromes and their associated symptoms and signs, past medical screenings and procedures, past hospitalizations and visits (Examiner notes that the patient's medical history, symptoms, and signs are factors of a current medical diagnosis)).
As per Claim 6, Bitran discloses the patient guidance system as recited in claim 3, Bitran also discloses wherein the current medical data for the patient includes a current image of one or more body parts in paragraphs [0031] (medical data may also include a picture of a skin lesion to be identified at a later time (Examiner notes that the picture skin lesion on the user's body is an example of an image of a body part)).
As per Claim 7, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current location of the patient in paragraphs [0017] (geolocation data includes GPS coordinate data, wherein the coordinate data includes time stamp, latitude, longitude, and altitude, that is obtained by a GPS receiver on a computing device).
As per Claim 8, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current listing of calendared events for the patient in paragraphs [0059] and [0062] (the user's geolocation data is used to provide a recommended health service based on the user's predicted location during an available timeslot in the future according to the user's calendar (Examiner notes that the predicted location during an available time slot shows the scheduled events including time and location, wherein time and location are included in geolocation data, that are in the patient's calendar)).
As per Claim 9, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current daily schedule for the patient in paragraphs [0046] and [0059] and [0062] (based on the user's geolocation data, the health recommender notes that the user has a busy schedule which is contributing to a lack of sleep and then recommends a schedule change (Examiner notes that a busy schedule shows that the user had many activities or events planned, wherein the planned events/activities include time stamps and specific locations which are included in geolocation data, throughout the day, week, or for an accumulated amount of time)).
As per Claim 10, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes an activity currently being performed by the patient in paragraphs [0015-0016] and [0053] (geolocation data received from the computing device, wherein the computing device may be a smart phone, tablet computing device, a wearable computing device, a personal computer or a computerized medical device, includes the geographic location and the velocity of the user (Examiner notes that the velocity of the user describes if the user is actively moving or not and the user's speed and direction)).
As per Claim 11, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more of an outdoor temperature value, a humidity value, a barometric pressure value, an air quality index value, a value for particulate matter sized at less than or equal to about 2.5 micrometers, a heat index value, a wind speed value, a wind direction, a visibility distance value, and an insect/animal vector distribution in paragraphs [0018] (the non-medical data may comprise of weather data (Examiner notes that National Oceanic and Atmospheric Administration, NOAA, considers weather data to include temperature, humidity, wind speed and direction, and atmospheric pressure. Also, the weather data meets the one or more limitation of current environmental characterization data)).
As per Claim 12, Bitran discloses the patient guidance system as recited in claim 2, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more air quality measurements within a current vicinity of the patient in paragraphs [0018] and [0038] (the non-medical data may comprise of air quality measurements, wherein air quality includes air pollen and pollutant concentrations, in the vicinity of the user).
As per Claim 13, Bitran discloses the patient guidance system as recited in claim 12, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more air quality measurements along an anticipated travel route of the patient in paragraphs [0018] and [0038] (the non-medical data may comprise of air quality measurements, wherein air quality includes air pollen and pollutant concentrations which may be displayed on an interactive map showing the temporal and geographic distribution (Examiner notes that temporal and geographic distribution shows how the air pollen and pollutant concentration change over time in a geographical area, wherein the geographical area includes travel routes)).
As per Claim 14, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is trained based on case data for a population of patients in paragraphs [0021] and [0025-0026] and [0035-0037] (the machine learning algorithm is informed and modified over time based on context information (referring to the case data), wherein the information is the combined time and location-based data, wherein the combined data is a global aggregated time and location-based history that includes the time and location-based history correlated in the first and second correlator, wherein the correlators correlate a plurality of medical and non-medical data from an user population), wherein the case data for a given patient within the population of patients includes actions taken and corresponding outcomes as a function of time in paragraphs [0033-0034] (the combined time and location-based data for a user includes past medical history, medications, past hospitalizations, family history, social history, occupational history, and environmental history (Examiner notes that the user's personal and medical history corresponds to the actions taken and corresponding outcomes as a function of time. For example, the reference discloses that if a patient reports shortness of breath, the patient's combined time and location-based data may be evaluated to correlate the shortness of breath with the patient's asthma, which was affected due to the recent environmental history. In the evaluation, the shortness of breath can also be associated with the time point when the patient started a medication (referring to the action taken in response to having asthma) and an inference of an adverse effect (referring to the corresponding outcome as a function of time due to the patient having a shortness of breath after taking the medication for a period of time))), the case data for the given patient also including one or more of the multiple input data streams for the given patient as a function of time during periods of time relevant to the actions taken and corresponding outcomes present in the case data for the given patient in paragraphs [0021] and [0025-0026] and [0033-0037] (the combined time and location-based data for a user includes a plurality of medical and non-medical data, wherein the data is associated with time-stamped geolocation data, that is relevant to the user's past medical history, medications, past hospitalizations, family history, social history, occupational history, and environmental history).
As per Claim 21, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the data acquisition engine is configured for data connection with one or more applications executing on a computing device of the patient, wherein the one or more applications provide one or more of the multiple input data streams to the data acquisition engine in paragraphs [0018] and [0020] (the personal assistant interpretation engine receives user data from a search application, and an electronic personal assistant application program executed on the user computing device, wherein the applications provide user data including medical, non-medical, and geolocation data to the personal assistant interpretation engine).
As per Claim 22, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses further comprising: a graphical user interface configured for display on a computing system of the patient, the graphical user interface including a region for displaying the recommendation for the patient in real-time in paragraphs [0046] and [0055] and [0074] (a graphical user interface displays on the computing device of the user, an area for making health recommendations).
As per Claim 23, Bitran discloses the patient guidance system as recited in claim 22, Bitran also discloses wherein the region provides for bi-directional communication between the patient guidance system and the patient in paragraphs [0015-0016] and [0030] (user feedback is solicited in regards to the effectiveness of the recommendation, which the feedback is then transmitted to the electronic personal assistant application server, wherein the server is a part of the computer system's server system, which decides the type of recommendation that is sent to the computing device of the user (Examiner notes that the computing system's program soliciting user feedback to the recommendation is an example of bi-directional communication)).
As per Claim 24, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured to automatically identify a condition or a situation that will adversely impact the patient when left unmitigated, wherein the recommendation for the patient is generated to suggest an action by the patient that will mitigate the condition or the situation in paragraphs [0035] and [0037-0038] (the machine learning algorithm located in the health recommender is configured to identify the worsening asthma (referring to a condition that will adversely impact the patient when left unmitigated) of the user and output a recommendation to stay indoors, use supplemental oxygen, use indoor air filter, and increase nebulizer use (Examiner notes that the recommended treatments will mitigate the worsening asthma)).
As per Claim 25, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured to automatically identify an action that will beneficially impact the patient when performed, wherein the recommendation for the patient is generated to encourage performance of the action by the patient in paragraphs [0037] and [0039] (the machine learning algorithm located in the health recommender may advise a patient with a mild, self-limiting headache to try an NSAID medication at home (Examiner notes that based on the symptoms and signs or user data received, wherein the symptom and sign was the mild, self-limiting headache, the algorithm identified an action that would beneficially impact the patient which would treat the headache)).
As per Claim 26, Bitran discloses the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured to automatically identify information for conveyance to the patient, wherein the recommendation for the patient is generated to convey the identified information in paragraphs [0036-0037] (the machine learning algorithm located in the health recommender is configured to identify a health condition, differential diagnoses, individuals with symptoms related to the flu epidemic and will output this information alongside the recommendation to the user).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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