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 .
Status of Claims
Claims 38-59 are pending.
This communication is in response to the communication filed November 22, 2023.
Claim Rejections - 35 USC § 112(b)
Claims 38-57 are objected to because of the following informalities: Claim 38 recites “performs the operations of:” in line 6; claim 38 recites “obtaining, in the at least one memory” in line 7, but the only memory recited in the claim is the computer readable non-transitory memory. Appropriate correction or clarification 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 38-59 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite systems, apparatuses, and methods for making recommendations for a condition, which are statutory categories of inventions.
Specifically, the independent claims, taking claim 58 as representative, recite obtaining…a use case package related to the individual, the use case package comprising one or more executable code and data structure compilations, and one or more leading indicator rule sets corresponding to a use case in the use case package; obtaining…sensor data related to the individual from the at least one sensor; generating a set of leading indicators from the sensor data according to the one or more leading indicator rule sets corresponding to the use case in the use case package; predicting…at least one predicted action including a recommendation for a condition based on the set of leading indicators; and causing…to render the at least one predicted action including the recommendation on an output. The claim limitations are directed to collected data, analyzing it, and outputting the results of the collection and analysis, which are grouped within the “certain methods of organizing human activity” grouping of abstract ideas. The claims involve a series of steps for collecting sensor data, analyzing it to make a recommendation based on various given rules, and then provide an output of the recommendation. See MPEP 2106.04. The claims are interpreted to recite concepts relating to tracking or organizing information related to medical data. Accordingly, the claims recite an abstract idea.
The dependent claims further recite limitations of converting lead indicators into a condition state vector, routing notifications, further explaining the action plan, further explaining the use case package being healthcare, physical therapy, or real time injury package. The limitations directed to converting lead indicators into a condition vector may be interpreted as mathematical concepts, because they involve using a set of rules, functions or algorithms to manipulate the data. The combination analysis lead to the conclusion that the recited claim limitations are grouped within the “certain methods of organizing human activity” grouping of abstract ideas, based on the same rationale as above.
The claims additionally recite limitations that are not interpreted as part of the abstract idea: prediction agent, trained machine learning model, chaining agent, memory, computer device, sensor, non-transitory memory, and processor. This judicial exception is not integrated into a practical application. Integration into a practical application requires 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 the claim is more than a drafting effort designed to monopolize the exception.
The claims merely use the additional elements as tools to perform abstract ideas and generally link the use of a judicial exception to a particular technological environment. The use of the additional elements as tools to implement the abstract idea and generally to link the use of the abstract idea to a particular technological environment does not render the claim patent eligible, because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, the prediction agent may include a trained machine learning model or chaining agent and performs the steps of processing data. The memory, non-transitory memory, and processor may be part of a computing device performing data input, processing, and output functions. The computing device can be any practical computing device possibly including a desktop computer, smart watch, patient monitoring system, tablet, medical equipment, robot, vehicle, set top box, appliance, game console, or other device capable of rendering suggested actions (specification par. 26). The sensors as recited function to provide data input. The functions of the invention may be executed with computer programs, which can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment (specification par. 98).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. See Apple v. Ameranth, 842 F.3d 1229, 1240 (Fed. Cir. 2016). The additional elements do not use the exception to affect a particular treatment or prophylaxis for a disease, do not apply the exception using particular machines, and do not effect a transformation or reduction of a particular article to a different state or thing, rather the computer elements are generally stated as to their structure and function and are only used to make recommendations instead of directly providing specific treatment or prophylaxis. Therefore, the additional elements do not impose any meaningful limits on practicing the abstract idea and the additional limitations are not indicative of materializing into a practical application. Accordingly, the claim is directed to an abstract idea.
Generic computer elements recited as performing generic computer functions that are well-understood, routine, or conventional activities amount to no more than implementing the abstract idea with a computerized system (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network and performing repetitive calculations); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); See MPEP 2106.05(d) and July 2015 Update: Section IV). Here, the claim limitations of converting vectors are similar to performing repetitive calculations and the limitations for obtaining data and outputting data are similar to a computer receiving and sending information over a network.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a prediction agent, trained machine learning model, chaining agent, memory, computer device, sensor, non-transitory memory, and processor to perform the steps of obtaining data, generating leading indicators, predicting a recommendations, and outputting the prediction amount to no more than using computer related devices to automate or implement the abstract idea for making recommendations for a condition.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
In conclusion, the claims are directed to the abstract idea of making recommendations for a condition by collecting data, analyzing it, and outputting the results of the collection and analysis. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 38-53 and 55-59 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sobol et al. US2019/0209022.
As per claim 38, Sobol teaches
a personal sensor system comprising: at least one sensor associated with an individual; at least one computer readable non-transitory memory storing software instructions including a prediction agent; and at least one processor coupled with the at least one sensor and the at least one memory that, upon execution of the software instructions, performs the operations of: (Sobol par. 10, 164 teaches a non-transitory computer readable medium and a processor that is configured to perform a predefined set of operations in response to receiving a corresponding instruction selected from a predefined native instruction set, and a set of machine codes selected from the native instruction set and operated upon by the processor and various sensors on or inside a wearable electronic device, where the sensors may act in conjunction with one another—as well as with instructions that are stored on a machine-readable medium such as memory—to aggregate or fuse the acquired data in order to infer certain activities, conditions, or circumstances)
obtaining, in the at least one memory, a use case package related to the individual, the use case package comprising one or more executable code and data structure compilations, and one or more leading indicator rule sets corresponding to a use case in the use case package; (Sobol par. 157, 182, 308 teaches a sensing module, a prediction module, including having or otherwise being cooperative with machine learning-based algorithms and ensuing models, or an alert module, here real-time alerts in a post-event situation may be a use case package for conducting a root cause analysis that in turn may serve as a predictor for future preventable events. Establishing standardized rules for wearable electronic device authentication, data representation, signaling, co-existence, and error detection, which is interpreted as generating leading indicators and obtaining indicator rule sets based on a use case package)
obtaining, in the at least one memory, sensor data related to the individual from the at least one sensor; (Sobol par. 2, 121, 157 teaches processing the real time sensor data, where data may be related to an individual’s location, environmental, activity, and physiological data)
generating a set of leading indicators from the sensor data according to the one or more leading indicator rule sets corresponding to the use case in the use case package; (Sobol par. 182 teaches an embodiment establishing standardized rules for wearable electronic device authentication, data representation, signaling, co-existence, and error detection, which is interpreted as generating leading indicators packaged for specific use cases)
predicting, via the prediction agent, at least one predicted action including a recommendation for a condition based on the set of leading indicators; and (Sobol par. 157, 235, 308, 342 teaches behavior or related parametric information may be compared to real-time or presently-acquired data and may be operated upon by one or more of the machine learning models, here real-time alerts in a post-event situation may be used to conduct a root cause analysis that in turn may serve as a predictor for future preventable events, interpreted as predicting an a recommended action based on condition)
causing a computing device to render the at least one predicted action including the recommendation on an output of the computing device (Sobol fig. 1, 14B-14C and associated paragraphs, par. 40, 308, 342 teaches outputting analyzed data as communicating and displaying alerts).
As per claim 39, Sobol teaches all the limitations of claim 38 and further teach wherein the operations further include converting the set of leading indicators into a condition state vector (Sobol par. 167, 168, 230 teaches using environmental sensors, activity sensors, and physiological sensors to collect temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, smoke, heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells to collect data grouped according to time, date, type of sensor, or the like and may be in feature vector form).
As per claim 40, Sobol teaches all the limitations of claim 39 and further teach wherein the at least one predicted action is based on the condition state vector as input into the prediction agent (Sobol par. 157, 235, 308, 342 teaches behavior or related parametric information may be compared to real-time or presently-acquired data and may be operated upon by one or more of the machine learning models, here real-time alerts in a post-event situation may be used to conduct a root cause analysis that in turn may serve as a predictor for future preventable events, interpreted as generating a required action based on condition state).
As per claim 41, Sobol teaches all the limitations of claim 38 and further teach wherein the at least one prediction action comprises an action plan (Sobol par. 191, 197, 234 teaches personalized wearable devices for personalized medicine and related individualized-profile clinical decision-making).
As per claim 42, teach all the limitations of claim 41 and further teach wherein the action plan comprises a recommended action plan including the recommendation (Sobol par. 356 teaches making a recommendation for the treatment of the patient, such as forming a patient action plan for a patient associated with the wearable electronic device).
As per claim 43, Sobol teaches all the limitations of claim 38 and further teach wherein the prediction agent comprises a chaining agent (Sobol par. 140, 213 teaches software instructions with various rules, here specification par. 24 explains that a chaining agent comprises software instructions that execute implementations of one or more rules sets that extrapolate, when necessary, a plan of action).
As per claim 44, Sobol teaches all the limitations of claim 43 and further teach wherein the operations further include extrapolating, via the chaining agent, a plan of action at a predicted point of care (Sobol par. 307, 340 teaches predicting action plans for various points of care).
As per claim 45, Sobol teaches all the limitations of claim 38 and further teach wherein the operation of obtaining the use case package includes generating the use case package in the memory (Sobol par. 307 teaches use case packages for assisted living facilities).
As per claim 46, Sobol teaches all the limitations of claim 38 and further teach wherein the use case package comprises a healthcare package (Sobol fig. 2F and associated paragraphs, par. 154, 167 teaches using a larger number or larger number of different types of physiological sensors for particular forms of bodily function monitoring, where different modular packages or options made of differing combinations of such sensors may be used).
As per claim 47, Sobol teaches all the limitations of claim 38 and further teach wherein the use case package comprises a physical therapy package (Sobol par. 234, 301 teaches embodiments of actions plans and use cases for physical therapy).
As per claim 48, Sobol teaches all the limitations of claim 38 and further teach wherein the use case package comprises a real-time injury package (Sobol par. 305, 308 teaches reducing or preventing patient falls; passive action is interpreted as real-time injury to the individual wearing the sensors).
As per claim 49, Sobol teaches all the limitations of claim 38 and further teach wherein the at least one sensor includes one or more of the following: a pulse-ox sensor, a heartrate sensor, a piezoelectric sensor, a thermometer, a galvanometer, a location sensor, a magnetometer, an EKG, an EEG, a blood pressure sensor, an accelerometer, a proximity sensor, an infrared sensor, a pressure sensor, a light sensor, an ultrasound sensor, a microphone, a camera, a particle detector, a flow sensor, a color sensor, a LiDAR, a humidity sensor, a gyroscope sensor, a tilt sensor, or a touch sensor (Sobol par. 167, 168, 230 teaches using environmental sensors, activity sensors, and physiological sensors to collect temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, smoke, heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells to collect data grouped according to time, date, type of sensor, or the like and may be in feature vector form to predict health conditions).
As per claim 50, Sobol teaches all the limitations of claim 38 and further teach wherein the at least one prediction agent comprises a trained machine learning model (Sobol par. 165 teaches prediction agents may be random forest-based classifier, regression machine learning models, or trained artificial neural network).
As per claim 51, Sobol teaches all the limitations of claim 50 and further teach wherein the trained machine learning model comprises at least one of the following: a support vector machine model, a random forest model, an artificial neural network model, a nearest neighbor model, and a k-means clustering model, a long short- term memory model, a recurrent neural network, a gated recurrent network, or a multi-layer perceptron model (Sobol par. 165 teaches prediction agents may be random forest-based classifier, regression machine learning models, or trained artificial neural network).
As per claim 52, Sobol teaches all the limitations of claim 38 and further teach wherein the at least one predicted action comprises at least one of the following: a predicted location of care, a predicted time of care, or a predicted urgency (Sobol par. 126, 215, 260 teaches alerts and important messages using time sequence data to predict health conditions, where the transmission of alerts from the wearable electronic device may be sequenced or prioritized based on the relative importance of the type of caregiver to the individual that is sending the alert, by proximity of the various caregivers to the wearable electronic device, or by some other approach).
As per claim 53, teach all the limitations of claim 38 and further teach wherein the at least one predicted action comprises a predicted treatment (Sobol par. 234 teaches performing action plans and providing guidance for medication dosages and therapy plans, interpreted as a predicted treatment).
As per claim 55, Sobol teaches all the limitations of claim 38 and further teach a sensor hub operable to couple with the at least one sensor (Sobol par. 8, 117 teaches a wearable device and smart internet of things configuration, which are interpreted as a smart watch or dedicated device).
As per claim 56, Sobol teaches all the limitations of claim 55 and further teach wherein the sensor hub comprises at least one of the following: a dedicated device, a vehicle, a smart watch, a smart card, or a mobile phone (Sobol par. 121 teaches a wearable device with sensors, which may be interpreted as a dedicated device).
As per claim 57, Sobol teaches all the limitations of claim 38 and further teach wherein the operations further include routing a notification associated with the at least one predicted action and the individual to a stakeholder (Sobol par. 140, 234 teaches sending alerts and notifications to caregivers).
As per claims 58-59, see claim 38 rejection above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 54 are rejected under 35 U.S.C. 103 as being unpatentable over Sobol et al. US2019/0209022 in view of Zhang et al US2021/0375468.
As per claim 54, Sobol teaches all the limitations of claim 53, but does not specifically teach the following limitations met by Zhang, wherein the predicted treatment comprises at least one CPT code (Zhang par. 60 teaches using CPT codes for diagnoses and procedures).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Sobol to use CPT codes as taught by Zhang with the motivation to diagnose and treat without limiting the timeliness and effectiveness of treatment (Zhang par. 3). One of ordinary skill in the art would have recognized that the results of the combination were predictable.
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 38-59 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-37 of U.S. Patent No. 11,881,315. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claim limitations are substantially similar to the pending claim limitations.
Pending Claim 38: A personal sensor system comprising: at least one sensor associated with an individual;
Patented claim 1: A personal area sensor system comprising: a set of sensors capable of capturing sensor data associated with an individual;
at least one computer readable non-transitory memory storing software instructions including a prediction agent;
a sensor hub communicatively coupled with the set of sensors and
and at least one processor coupled with the at least one sensor and the at least one memory that, upon execution of the software instructions, performs the operations of:
comprising at least one computer readable memory and at least one processor that, upon execution of software instructions stored in the memory, performs operations to:
obtaining, in the at least one memory, a use case package related to the individual, the use case package comprising one or more executable code and data structure compilations, and one or more leading indicator rule sets corresponding to a use case in the use case package; obtaining, in the at least one memory, sensor data related to the individual from the at least one sensor;
obtain, in the memory, at least one action prediction agent configured to generate a predicted required action based on at least one condition state related to the individual; determine a use case package related to the individual, the use case package comprising one or more executable code and data structure compilations, and one or more leading indicator rule sets corresponding to the use case;
generating a set of leading indicators from the sensor data according to the one or more leading indicator rule sets corresponding to the use case in the use case package;
generate, using at least one of the one or more executable code and data structure compilations in real-time, a set of leading indicators from the sensor data based on a context of the individual and according to the one or more leading indicator rules sets, the at least one of the one or more executable code and data structure compilations operative to convert the set of leading indicators into;
predicting, via the prediction agent, at least one predicted action including a recommendation for a condition based on the set of leading indicators;
generate at least one predicted required action for a condition via the at least one action prediction agent based on the condition state vector relative to at least one known state related to the individual; and
and causing a computing device to render the at least one predicted action including the recommendation on an output of the computing device.
cause, via transmitting an alert over a network, a computing device to render the predicted required action on an output of the computing device.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686