Detailed Notice
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
The following Office Action is a 2nd Non-Final:
Claims 1-3, 5, 7-11, 13, and 15-19 are currently pending.
Claims 1, 7, 9, 11, 13, 15-17, and 19 are amended.
Claims 4, 6, 12, 14, and 20-24 are canceled.
Claims -3, 5, 7-11, 13, and 15-19are rejected.
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 1-3, 5, 7-11, 13, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-3, 5, and 7-8 are directed toward a motor function improvement assistance apparatus (i.e., machine), claims 9-11, 13, and 15-16 are directed toward a motor function improvement assistance method (i.e., process), and claims 17-19 are directed toward a non-transitory computer-readable medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 9, and 17 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A motor function improvement assistance apparatus comprising: background knowledge storage unit configured to store background knowledge in which a state of an observable body part and a kinematic problem of the state are associated with each other based on a causal relationship; observation reception unit configured to receive an observation including examination information and medical interview information of a subject; hypothesis generation unit configured to generate, based on the background knowledge and the observation, hypotheses of a plurality of kinematic problems by using hypothesis inference; and hypothesis link generation unit configured to generate a combination of hypotheses including an interaction between the hypotheses, wherein the background knowledge storage unit stores, as the background knowledge, an exclusive relationship among the plurality of the kinematic problems that are not established simultaneously, a backward inference operation and a unification operation, and the hypothesis generation unit generates the hypotheses, based on the background knowledge including the exclusive relationship”.
The limitations of store background knowledge in which a state of an observable body part and a kinematic problem of the state are associated with each other based on a causal relationship; receive an observation including examination information and medical interview information of a subject; generate, based on the background knowledge and the observation, hypotheses of a plurality of kinematic problems by using hypothesis inference; and generate a combination of hypotheses including an interaction between the hypotheses, as the background knowledge, an exclusive relationship among the plurality of the kinematic problems that are not established simultaneously, a backward inference operation and a unification operation, and generates the hypotheses, based on the background knowledge including the exclusive relationship, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of store, receive, and generate, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 9 and 17 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-3, 5, 7-8, 10-11, 13, 15-16, and 18-19 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, and 17. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-3, 5, 7-11, 13, and 15-19 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “observation reception unit”, “hypothesis generation unit”, “hypothesis link generation unit”, “background knowledge unit”, and “hypothesis generation unit”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0014], [0017]-[0018], [0094], and [0116], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “background knowledge storage unit configured to”, “observation reception unit configured to”, “hypothesis generation unit configured to”, “hypothesis link generation unit configured to”, “wherein the background knowledge storage unit stores”, and “the hypothesis generation unit”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receive an observation including examination information and medical interview information of a subject”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-3, 5, 7-8, 10-11, 13, 15-16, and 18-19 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 6, and 17, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims2-3, 5, 7-8, 10-11, 13, 15-16, and 18-19 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 9, and 17, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receive an observation including examination information and medical interview information of a subject”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-3, 5, 7-11, 13, and 15-19 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sobol et al. (US 20210319894 A1), hereinafter Sobol, in view of Ebrahimi et al. (US 20220066456 A1), hereinafter Ebrahimi.
Regarding claim 1 Sobol teaches a motor function improvement assistance apparatus (Sobol, [0007]: “wireless application that improves the ability to track the location and associated environment, activity and physiological information of a person”) comprising: background knowledge storage unit configured to store background knowledge in which a state of an observable body part (Sobol, [0220]: “the approach of the present disclosure may employ an observation-based or example-based way to create new forms of probabilistic health diagnosis logic”, [0226]: “an HMI facilitates the modeling of a given event or process with a hidden state that is based on observable parameters, particularly in determining the likelihood of a given sequence”) and a kinematic problem of the state are associated with each other based on a causal relationship (Sobol, [0227], [0232], and [0249]: “a feature vector could be a summary of one or more of a patient's kinematic data (which may form indicia of activity) and related location data, physiological data, or environmental data such that the ensuing clinical observation of symptoms may lead to an enhanced diagnosis of a particular condition”); observation reception unit configured to receive an observation including examination information and medical interview information of a subject (Sobol, [0290], [0297], [0313]: “The design methodology included using several approaches. For example, a single cohort pre-post design was used to evaluate the use of the location pattern models in reducing falls. In addition, a descriptive design was used to address the acceptability and potential for implementation; this design was verified through the use of interviews and focus groups”, [0350]: “where activity-related information pertaining to a patient's gait, falling tendency, wandering tendency, level of agitation, restlessness or the like can help provide answers to an algorithmic series of questions used to determine the appropriateness of a particular medication regime”); hypothesis generation unit configured to generate, based on the background knowledge and the observation, hypotheses of a plurality of kinematic problems by using hypothesis inference (Sobol, [0279]: “It will be appreciated that such data mining and machine learning may be employed as a component of cognitive computing to help extend conventional predictive analytics in order to provide CDS (in one form) or more comprehensive diagnosis activities (in another form)”, [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”); and hypothesis link generation unit configured to generate a combination of hypotheses including an interaction between the hypotheses (Sobol, [0256], [0262], [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”).
Sobol does not teach wherein the background knowledge storage unit stores, as the background knowledge, an exclusive relationship among the plurality of the kinematic problems that are not established simultaneously, a backward inference operation and a unification operation, and the hypothesis generation unit generates the hypotheses, based on the background knowledge including the exclusive relationship.
However, Ebrahimi teaches wherein the background knowledge storage unit stores, as the background knowledge, an exclusive relationship among the plurality of the problems (Ebrahimi, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, [0881], [0964], and [01396]) that are not established simultaneously (Ebrahimi, [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”), a backward inference operation and a unification operation (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition”), and the hypothesis generation unit generates the hypotheses, based on the background knowledge including the exclusive relationship (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition” , [0510]: “the processor may monitor the relationship between each of the light points and respective features as the robot moves in following time slots. The processor may disassociate some associations between light points and features and generate some new associations between light points and features. For example, two captured images include three features (a tree, a small house, a large house) and light points associated with each of the features. The associated features and light points are included within the same dotted shape. A first image is captured at a first time point, a second image at a second time point, and a third image at a third time point as the robot moves within the environment. As the robot moves, some features and light points associated at one time point become disassociated at another time point, such as when a feature (the large house) from the first image is no longer in the third image. Or some new associations between features and light points emerge at a next time point, wherein a new feature (a person) is captured in the image. In some embodiments, the robot may include an LED point generator that spins. For example, a robot may include a spinning LED light point generator. Light points may be emitted by a light point generator and a camera captures images of light points. In some embodiments, the camera of the robot captures images of the projected light point. In some embodiments, the light point generator is faster than the camera resulting in multiple light points being captured in an image fading from one side to another. In some embodiments, the robot may include a full 360 degrees LIDAR. In some embodiments, the robot may include multiple cameras. This may improve accuracy of estimates based on image data”, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, and [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Sobol to incorporate the teachings of Ebrahimi and account for robotic devices may include a drone, a robotic vacuum cleaner, a robotic lawn mower, a robotic mop, or other robotic devices. To operate autonomously or with minimal (or less than fully manual) input and/or external control within an environment, methods such as mapping, localization, object recognition, and path planning methods, among others, are required such that robotic devices may autonomously create a map of the environment, subsequently use the map for navigation, and devise intelligent path and task plans for efficient navigation and task completion (Ebrahimi, Abstract and [0004]).
Regarding claim 2 Sobol further teaches multiple solution integration unit configured to integrate the generated hypotheses (Sobol, [0230], [0256], and [0262]); and graph structure display unit configured to generate a graph structure from the integrated hypotheses (Sobol, [0171]: “Thus, the ISA acts as an interface between the hardware of the processor 173A and the system or application software through the implementation of the machine code 173E all of which are predefined within the ISA. As such, the machine code 173E imparts structure to the successive architectures of processor 173A, logic device 173, PCB assembly 170 and wearable electronic device 100, specifically in the form of a program structure that may be made up of a set of individual codes that together may be depicted herein as a flow diagram or related sequence that operates on the data structure that itself may be in one form an organized list, array, tree or graph of collected LEAP data”, [0221], [0248], and [0320]).
Regarding claim 3 Sobol further teaches the observation reception unit receives the generated hypotheses as the observation (Sobol, [0279]: “It will be appreciated that such data mining and machine learning may be employed as a component of cognitive computing to help extend conventional predictive analytics in order to provide CDS (in one form) or more comprehensive diagnosis activities (in another form)”, [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”).
Regarding claim 5 Sobol further teaches the hypothesis generation unit evaluates each of the generated hypotheses by using an evaluation function for evaluating the hypothesis (Sobol, [0256]-[0257], [0259], [0260], and [0262]).
Regarding claim 7 Sobol further teaches the hypothesis generation unit generates the hypotheses of a plurality of the kinematic problems by using formulation into an integer linear programming problem or a satisfiability problem (Sobol, [0256]-[0257] and [0259]-[0262]).
Regarding claim 8 Sobol further teaches the hypothesis generation unit uses a solver capable of outputting multiple solutions and generates a plurality of the hypotheses having the same evaluation function value for evaluating the generated plurality of hypotheses (Sobol, [0256]-[0257] and [0259]-[0262]).
Regarding claim 9 Sobol teaches a motor function improvement assistance method (Sobol, [0007]: “wireless application that improves the ability to track the location and associated environment, activity and physiological information of a person”) comprising: storing background knowledge in which a state of an observable body part (Sobol, [0220]: “the approach of the present disclosure may employ an observation-based or example-based way to create new forms of probabilistic health diagnosis logic”, [0226]: “an HMI facilitates the modeling of a given event or process with a hidden state that is based on observable parameters, particularly in determining the likelihood of a given sequence”) and a kinematic problem of the state are associated with each other based on a causal relationship (Sobol, [0227], [0232], and [0249]: “a feature vector could be a summary of one or more of a patient's kinematic data (which may form indicia of activity) and related location data, physiological data, or environmental data such that the ensuing clinical observation of symptoms may lead to an enhanced diagnosis of a particular condition”); receiving an observation including examination information and medical interview information of a subject (Sobol, [0290], [0297], [0313]: “The design methodology included using several approaches. For example, a single cohort pre-post design was used to evaluate the use of the location pattern models in reducing falls. In addition, a descriptive design was used to address the acceptability and potential for implementation; this design was verified through the use of interviews and focus groups”, [0350]: “where activity-related information pertaining to a patient's gait, falling tendency, wandering tendency, level of agitation, restlessness or the like can help provide answers to an algorithmic series of questions used to determine the appropriateness of a particular medication regime”); generating, based on the background knowledge and the observation, hypotheses of a plurality of kinematic problems by using hypothesis inference (Sobol, [0279]: “It will be appreciated that such data mining and machine learning may be employed as a component of cognitive computing to help extend conventional predictive analytics in order to provide CDS (in one form) or more comprehensive diagnosis activities (in another form)”, [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”); generating a combination of hypotheses including an interaction between the hypotheses (Sobol, [0256], [0262], [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”).
Sobol does not teach when storing the background knowledge, storing an exclusive relationship among the plurality of the kinematic problems that are not simultaneously established, as the background knowledge: applying a backward reasoning operation and a unification operation: and when generating the hypotheses, generating the hypotheses, based on the background knowledge including the exclusive relationship.
However, Ebrahimi teaches when storing the background knowledge, storing an exclusive relationship among the plurality of the problems (Ebrahimi, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, [0881], [0964], and [01396]) that are not simultaneously established, as the background knowledge (Ebrahimi, [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”): applying a backward reasoning operation and a unification operation (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition”): and when generating the hypotheses, generating the hypotheses, based on the background knowledge including the exclusive relationship (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition” , [0510]: “the processor may monitor the relationship between each of the light points and respective features as the robot moves in following time slots. The processor may disassociate some associations between light points and features and generate some new associations between light points and features. For example, two captured images include three features (a tree, a small house, a large house) and light points associated with each of the features. The associated features and light points are included within the same dotted shape. A first image is captured at a first time point, a second image at a second time point, and a third image at a third time point as the robot moves within the environment. As the robot moves, some features and light points associated at one time point become disassociated at another time point, such as when a feature (the large house) from the first image is no longer in the third image. Or some new associations between features and light points emerge at a next time point, wherein a new feature (a person) is captured in the image. In some embodiments, the robot may include an LED point generator that spins. For example, a robot may include a spinning LED light point generator. Light points may be emitted by a light point generator and a camera captures images of light points. In some embodiments, the camera of the robot captures images of the projected light point. In some embodiments, the light point generator is faster than the camera resulting in multiple light points being captured in an image fading from one side to another. In some embodiments, the robot may include a full 360 degrees LIDAR. In some embodiments, the robot may include multiple cameras. This may improve accuracy of estimates based on image data”, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, and [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Sobol to incorporate the teachings of Ebrahimi and account for robotic devices may include a drone, a robotic vacuum cleaner, a robotic lawn mower, a robotic mop, or other robotic devices. To operate autonomously or with minimal (or less than fully manual) input and/or external control within an environment, methods such as mapping, localization, object recognition, and path planning methods, among others, are required such that robotic devices may autonomously create a map of the environment, subsequently use the map for navigation, and devise intelligent path and task plans for efficient navigation and task completion (Ebrahimi, Abstract and [0004]).
Regarding claim 10 Sobol further teaches integrating the generated hypotheses (Sobol, [0256]-[0257] and [0259]-[0262]); and generating a graph structure from the integrated hypotheses (Sobol, [0171]: “Thus, the ISA acts as an interface between the hardware of the processor 173A and the system or application software through the implementation of the machine code 173E all of which are predefined within the ISA. As such, the machine code 173E imparts structure to the successive architectures of processor 173A, logic device 173, PCB assembly 170 and wearable electronic device 100, specifically in the form of a program structure that may be made up of a set of individual codes that together may be depicted herein as a flow diagram or related sequence that operates on the data structure that itself may be in one form an organized list, array, tree or graph of collected LEAP data”, [0221], [0248], and [0320]).
Regarding claim 11 Sobol further teaches when receiving the observation, receiving the generated hypotheses as the observation (Sobol, [0290], [0297], [0313]: “The design methodology included using several approaches. For example, a single cohort pre-post design was used to evaluate the use of the location pattern models in reducing falls. In addition, a descriptive design was used to address the acceptability and potential for implementation; this design was verified through the use of interviews and focus groups”, [0350]: “where activity-related information pertaining to a patient's gait, falling tendency, wandering tendency, level of agitation, restlessness or the like can help provide answers to an algorithmic series of questions used to determine the appropriateness of a particular medication regime”).
Regarding claim 13 Sobol further teaches when generating the hypotheses, evaluating each of the generated hypotheses by using an evaluation function for evaluating the hypotheses (Sobol, [0256]-[0257] and [0259]-[0262]).
Regarding claim 15 Sobol further teaches formulating as an integer linear programming problem or a satisfiability problem when generating the hypotheses, thereby generating the hypotheses of the plurality of kinematic problems (Sobol, [0256]-[0257] and [0259]-[0262]).
Regarding claim 16 Sobol further teaches when generating the hypotheses, using a solver capable of outputting multiple solutions thereby generating a plurality of the hypotheses having the same evaluation function value for evaluating the generated plurality of hypotheses (Sobol, [0256]-[0257] and [0259]-[0262]).
Regarding claim 17 Sobol teaches a non-transitory computer-readable medium storing a motor function improvement assistance program that causes a computer to execute (Sobol, [0007]: “wireless application that improves the ability to track the location and associated environment, activity and physiological information of a person”): storing background knowledge in which a state of an observable body part (Sobol, [0220]: “the approach of the present disclosure may employ an observation-based or example-based way to create new forms of probabilistic health diagnosis logic”, [0226]: “an HMI facilitates the modeling of a given event or process with a hidden state that is based on observable parameters, particularly in determining the likelihood of a given sequence”) and a kinematic problem of the state are associated with each other based on a causal relationship (Sobol, [0227], [0232], and [0249]: “a feature vector could be a summary of one or more of a patient's kinematic data (which may form indicia of activity) and related location data, physiological data, or environmental data such that the ensuing clinical observation of symptoms may lead to an enhanced diagnosis of a particular condition”); receiving an observation including examination information and medical interview information of a subject (Sobol, [0290], [0297], [0313]: “The design methodology included using several approaches. For example, a single cohort pre-post design was used to evaluate the use of the location pattern models in reducing falls. In addition, a descriptive design was used to address the acceptability and potential for implementation; this design was verified through the use of interviews and focus groups”, [0350]: “where activity-related information pertaining to a patient's gait, falling tendency, wandering tendency, level of agitation, restlessness or the like can help provide answers to an algorithmic series of questions used to determine the appropriateness of a particular medication regime”); generating, based on the background knowledge and the observation, hypotheses of a plurality of kinematic problems by using hypothesis inference (Sobol, [0279]: “It will be appreciated that such data mining and machine learning may be employed as a component of cognitive computing to help extend conventional predictive analytics in order to provide CDS (in one form) or more comprehensive diagnosis activities (in another form)”, [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”); generating a combination of hypotheses including an interaction between the hypotheses (Sobol, [0256], [0262], [0312]: “there is a potential to detect early changes in acute conditions such as UTI, pneumonia, agitation, medication side effects (including orthostatic hypotension, bradycardia or the like) as well as changes in gait, all of which are potential predictors of a fall”, and [0371]: “reliance may be had on other analytical techniques (including those based on a priori reasoning) that could be used in conjunction with such machine learning-based approaches in order to obtain meaningful analytic or predictive results”).
Sobol does not teach when storing background knowledge, storing, as the background knowledge, an exclusive relationship among the plurality of the kinematic problems that are not simultaneously established; applying a backward reasoning operation and a unification operation: and when generating the hypotheses, generating the hypotheses, based on the background knowledge including the exclusive relationship.
However, Ebrahimi teaches when storing background knowledge, storing, as the background knowledge, an exclusive relationship among the plurality of the problems (Ebrahimi, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, [0881], [0964], and [01396]) that are not simultaneously established (Ebrahimi, [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”); applying a backward reasoning operation and a unification operation (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition”): and when generating the hypotheses, generating the hypotheses, based on the background knowledge including the exclusive relationship (Ebrahimi, [0294]: “for each training set provided to the network, the network outputs a prediction in a forward pass, determines the error in its prediction, reverses (i.e., backpropagates) through each of the layers to determine the cell from which the errors are stemming, and reduces the weight for that respective connection. In embodiments, the network repeats the forward pass, each time tweaking the weights to ultimately reduce the error with each repetition” , [0510]: “the processor may monitor the relationship between each of the light points and respective features as the robot moves in following time slots. The processor may disassociate some associations between light points and features and generate some new associations between light points and features. For example, two captured images include three features (a tree, a small house, a large house) and light points associated with each of the features. The associated features and light points are included within the same dotted shape. A first image is captured at a first time point, a second image at a second time point, and a third image at a third time point as the robot moves within the environment. As the robot moves, some features and light points associated at one time point become disassociated at another time point, such as when a feature (the large house) from the first image is no longer in the third image. Or some new associations between features and light points emerge at a next time point, wherein a new feature (a person) is captured in the image. In some embodiments, the robot may include an LED point generator that spins. For example, a robot may include a spinning LED light point generator. Light points may be emitted by a light point generator and a camera captures images of light points. In some embodiments, the camera of the robot captures images of the projected light point. In some embodiments, the light point generator is faster than the camera resulting in multiple light points being captured in an image fading from one side to another. In some embodiments, the robot may include a full 360 degrees LIDAR. In some embodiments, the robot may include multiple cameras. This may improve accuracy of estimates based on image data”, [0854]: “the robot may include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components and data received by various software components from RF and/or external ports such as USB, firewire, or Ethernet”, and [1396]: “atomicity may occur when a data point is inconsistent with a previous data point and corrupts the map. In some cases, a set of constraints or rules may be used to provide consistency of the map… These observations may be included at all levels of implementation and may be used in data sensing subsystems, data aggregation subsystems, schedulers, or algorithm level subsystems. In some embodiments, mutual exclusion techniques may be used to provide consistency of data. In some embodiments, inlining small functions may be used to optimize performance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Sobol to incorporate the teachings of Ebrahimi and account for robotic devices may include a drone, a robotic vacuum cleaner, a robotic lawn mower, a robotic mop, or other robotic devices. To operate autonomously or with minimal (or less than fully manual) input and/or external control within an environment, methods such as mapping, localization, object recognition, and path planning methods, among others, are required such that robotic devices may autonomously create a map of the environment, subsequently use the map for navigation, and devise intelligent path and task plans for efficient navigation and task completion (Ebrahimi, Abstract and [0004]).
Regarding claim 18 Sobol further teaches integrating the generated hypotheses (Sobol, [0256]-[0257] and [0259]-[0262]); and generating a graph structure from the integrated hypotheses (Sobol, [0171]: “Thus, the ISA acts as an interface between the hardware of the processor 173A and the system or application software through the implementation of the machine code 173E all of which are predefined within the ISA. As such, the machine code 173E imparts structure to the successive architectures of processor 173A, logic device 173, PCB assembly 170 and wearable electronic device 100, specifically in the form of a program structure that may be made up of a set of individual codes that together may be depicted herein as a flow diagram or related sequence that operates on the data structure that itself may be in one form an organized list, array, tree or graph of collected LEAP data”, [0221], [0248], and [0320]).
Regarding claim 19 Sobol further teaches when receiving the observation, receiving the generated hypotheses as the observation (Sobol, [0290], [0297], [0313]: “The design methodology included using several approaches. For example, a single cohort pre-post design was used to evaluate the use of the location pattern models in reducing falls. In addition, a descriptive design was used to address the acceptability and potential for implementation; this design was verified through the use of interviews and focus groups”, [0350]: “where activity-related information pertaining to a patient's gait, falling tendency, wandering tendency, level of agitation, restlessness or the like can help provide answers to an algorithmic series of questions used to determine the appropriateness of a particular medication regime”).
Response to Arguments
Applicant's arguments filed 02/12/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues the rejection is improper in establishing the claims fall within the abstract idea of a certain method of organizing human activity due to MPEP 2106.04(a)(2)(II) and MPEP 2106.04(a)(3). Examiner respectfully disagrees. The claims fall within the abstract of idea of a certain method of organizing human activity, more specifically (and as stated in the above and previous office action), the subgrouping of managing personal behavior and relationships or interactions between people. Furthermore, MPEP 2106.04(a)(2)(II) states “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping”. Therefore, the limitations of “store”, “receive”, and “generate” are all steps a person can perform with a pen and paper, persons, or computer tools.
Applicant also argues the any alleged abstract idea is integrated into a practical application because the “background knowledge includes ‘an exclusive relationship among a plurality of the kinematic problems that are not established simultaneously’”, and it is specified that the hypothesis generation is performed by weighted abduction applying a backward inference operation and a unification operation, thus integration into a specific computer process of hypothesis search and integration under the exclusion constraint in terms of implementation is an improvement to technology. Examiner respectfully disagrees. The steps of “background knowledge includes ‘an exclusive relationship among a plurality of the kinematic problems that are not established simultaneously’”, are not an additional element, but is part of the abstract idea. An abstract idea cannot integrate itself into a practical solution. Additionally, using a “background knowledge storage unit” (which is an additional element) to execute the functions of storing an exclusive relationship among a plurality of the kinematic problems that are not established simultaneously, is recited at a high level, and amounts to applying the abstract idea to computer tools. See MPEP 2106.05(f) states “TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea”. Also, the “hypothesis generation is performed by weighted abduction applying a backward inference operation and a unification operation” is recited at a high level of generality and is also being applied to computer tools. MPEP 2106.05(f) states “A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words “apply it” to the judicial exception. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes “the effect or result dissociated from any method by which maintaining the state is accomplished” and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result”). Furthermore, the claims do not amount to significantly more as stated above, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the abstract idea to a particular technological environment or field of use or amount to insignificant extra solution activity in the form of data gathering, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Regarding the 35 U.S.C 102 Rejection, Applicant’s arguments with respect to the claim amendments and the previous rejection have been considered but are moot because the new ground of rejection due to the issuance of the 2nd Non-Final. The claims are now rejected under 35 U.S.C. 103 and are obvious over Sobol and Ebrahimi.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 7 AM - 7 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.S.S./ Examiner, Art Unit 3681
/PETER H CHOI/ Supervisory Patent Examiner, Art Unit 3681
Prosecution Insights