DETAILED ACTION
This non-final action is in response to the application filed 2 August 2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Claims 1-20 are pending, having a filing date of 2 August 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted 2 August 2023 complies with 37 C.F.R. 1.97. Accordingly, the IDS has been considered by the examiner. An initialed copy of the 1449 form is enclosed herewith.
Drawings
The drawings filed 2 August 2023 are accepted by the examiner.
Claim Objections
Claims 1, 2, 8, 9, 15 and 16 are objected to because of the following informalities:
Claim 1 recites “analysing” (ln. 5) and “analysed” (ln. 10). These recitations appear to be misspellings.
Claim 2 recites “analysed” (ln. 2). This recitation appears to be a misspelling.
Claim 8 recites “analysing” (ln. 10) and “analysed” (ln. 15). These recitations appear to be misspellings.
Claim 9 recites “analysed” (ln. 3). This recitation appears to be a misspelling.
Claim 15 recites “analysing” (ln. 11) and “analysed” (ln. 16). These recitations appear to be misspellings.
Claim 16 recites “analysed” (ln. 2). This recitation appears to be a misspelling.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
• STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
• STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
o STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
o STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
o STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter as shown below.
STEP 1: Do claims 1, 8 and 15 fall within one of the statutory categories? Yes, because claims 1 is directed toward a method, claim 8 is directed toward a computer program product, and claim 15 is directed toward a computer system, all of which fall within one of the statutory categories.
STEP 2A (PRONG 1): Are the claims directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claims 1, 8 and 15 are directed to abstract ideas.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
1. Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
2. Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
3. Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
As per claims 1, 8 and 15, the method (claim 1), the computer program product (claim 8) and the computer system (claim 15) are mental processes that can be performed in the mind and, therefore, an abstract idea. In particular, claims 1, 8 and 15 recites the abstract ideas of:
“analysing and mapping the extracted health features and the extracted terrain features, ... ; and
calculating a unique activity experience for the user to perform a preselected activity on the at least one activity trail based at least in part on the analysed and mapped extracted health features and the extracted terrain features.”
These recitations merely consist of analyzing and mapping health features and terrain features, and calculating (determining) a user an activity experience based on the features. This is equivalent to a person analyzing and mapping health features and terrain features, and determining a user an activity experience based on the features. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person, analyzes and maps health features and terrain features, and determines a user an activity experience based on the features. The mere nominal recitations that aforementioned steps are executed by “one or more computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media,” (claim 8), and the “one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media,” (claim 15) does not take the limitation out of the mental process grouping.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
• an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
• an additional element adds insignificant extra-solution activity to the judicial exception; and
• an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 8 and 15 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into practical application. Claims 1, 8 and 15 further recite the additional elements of:
“obtaining health data for a user and location data for a location that includes at least one activity trail”; and
“extracting health features from the health data and terrain features from the location data ... .”
These additional elements further limit the abstract idea without integrating the abstract idea into practical application or significantly more. In particular, the “obtaining and extracting … “ steps are recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an activity train and user health data) and amount to mere data gathering, a form of insignificant extra-solution activity added to the judicial exception per MPEP 2106.05(g), because the steps characterize pre solution activity, such as an individual observing health and location.
Claim 1 still further includes the additional elements “one or more computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media” (claim 8), and “one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media” (claim 15). These elements are not sufficient to amount to significantly more than the judicial exception because they fail to integrate the exception into practical application. The mere inclusion of instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the steps are executed by the “one or more computer-readable storage media and program instructions” (claim 8), and the “one or more computer processors, one or more computer-readable storage media, and program instructions” (claim 15), i.e. via computers. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated in the practical application. The “one or more computer-readable storage media and program instructions,” (claim 8) and the “one or more computer processors, one or more computer-readable storage media, and program instructions” (claim 15) merely describes how to generally “apply” the otherwise metal judgements in a generic or general purpose computing environment. The one or more computer-readable storage media and program instructions (claim 8), and one or more computer processors, one or more computer-readable storage media, and program instruction (claim 15) are recited at a high level of generality and merely automate the obtaining and extracting steps.
STEP 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception? No, claims 1, 8 and 15 do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
• adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1, 8 and 15 do not recite any specific limitation or combination of limitations that are well-understood, routine, conventional (WURC) activity in the field. Obtaining and extracting and creating data are fundamental, i.e. WURC, activities performed by servers, such as servers, cloud servers, computers operating with data such as the processors and programs recited in claims 1, 8 and 15. Further, applicant’s specification does not provide any indication that the extracting and obtaining activities of the systems are performed using anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Thus, since claims 1, 8 and 15 are: (a) directed toward an abstract idea; (b) do not recite additional elements that integrate the judicial exception into practical application; and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1, 8 and 15 are directed to non-statutory subject matter.
Dependent claims 2-7, 9-14 and 16-20 further limit the abstract idea without integrating the abstract idea into practical application or significantly more. For example, the additional elements of these claims are further limitations that, under their broadest reasonable interpretations, are limitations that are abstract ideas, using similar analysis to claims 1, 8 and 15, 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 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 non-obviousness.
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.
Claims 1, 5, 8, 13, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication Number 2018/0349563 to Bastide et al. (hereafter Bastide), now U.S. Patent Number 10,650,918 in view of U.S. Patent Publication Number 2020/0110814 to Abuelsaad et al. (hereafter Abuelsaad).
As per claim 1, Bastide discloses [a] method of unique hiking interaction via IoT data assimilation (see at least Bastide, Abstract; [0073] disclosing that the computer system/server 512 may also communicate with one or more external devices 514 such as a keyboard, a pointing device, a display 524, etc.; one or more devices that enable a user to interact with computer system/server 512; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 512 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 522. Still yet, computer system/server 512 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 520 <interpreted as Internet of Things (IOT)>), the method comprising:
obtaining health data for a user and location data for a location (see at least Bastide, [0006]; [0011]; [0028] disclosing the use crowdsourcing to determine health improvements routes, by recording location, time, date and health measures, such as activity, blood pressure, speed, ailments, heart rate, calories burned, etc., for users by analyzing a crowd, i.e., a population of users, and their corresponding locations to determine health behaviors in a given location/time to route a given user to a destination based on the crowds healthy behaviors/activity ; [0039] disclosing the inclusion of the following steps, with reference to the flowchart of FIG. 4. Referring now to the figure, a first step 41 is to record users' location and health measures. Embodiments of the disclosure can obtain access to the crowdsourced data by continuously recording user's heartrate/biometric data over a travel route. Embodiments of the disclosure record location and health measures, such as: location, based on GPS data and cell triangulation; elevation; metadata, such as time and date; health measurements, such as activity, blood pressure, speed, ailments, heart rate, calories burned; terrain, such as mud or grass, etc.; and environmental attributes, such as temperature, humidity, etc. The location is recorded along with the health measurements, and the data is joined together into a single data element based on time, e.g., {“HR”: 85, “X”: 1, “Y”: 2, “DATE”: “03MAR2016” }, where “HR” is a heartrate, “X” and “Y” may a longitude and latitude or X-Y positions on a grid, and “DATE” is the date. Each joined data element is limited to a single use ) ... (1) ... ;
extracting health features from the health data and terrain features from the location data (see at least Bastide, [0039]; [0045] disclosing filtering based on personal attributes, e.g., the person is a male aged 18-35 years and likes sports radio, and can create a map that highlight the opportunities to burn extra calories or change to healthier habits. Embodiments can normalize areas without enough data with similar data based on elevation, distance and pitch (i.e. incline or grade) <interpreted as terrain features>; [0046] disclosing an approach to determining the differences and thus the routing options can be performed by simple rule-based approach. For example, if an area around a user and their destination is grassy and hilly <interpreted as terrain features>, it can be determined that more calories will be easily burnt, the result of which is that the distance required for user to reach the destination can be quite short. Alternatively, an aggregated map can be created based on the similarity of the user to a select cohort in the crowdsourced data, based on, e.g., age and weight <interpreted as health data> );
analysing and mapping the extracted health features and the extracted terrain features (see at least Bastide, [0039] disclosing that with regard to FIG. 4, a first step 41 is to record users' location and health measures. ... Embodiments of the disclosure record location and health measures, such as: location, based on GPS data and cell triangulation; elevation; metadata, such as time and date; health measurements, such as activity, blood pressure, speed, ailments, heart rate, calories burned; terrain, such as mud or grass, etc.; and environmental attributes, such as temperature, humidity, etc. The location is recorded along with the health measurements, and the data is joined together into a single data element based on time, e.g., {“HR”: 85, “X”: 1, “Y”: 2, “DATE”: “03MAR2016” }, where “HR” is a heartrate, “X” and “Y” may a longitude and latitude or X-Y positions on a grid, and “DATE” is the date. Each joined data element is limited to a single user; [0045] disclosing filtering based on personal attributes, e.g., the person is a male aged 18-35 years and likes sports radio, and can create a map that highlight the opportunities to burn extra calories or change to healthier habits. Embodiments can normalize areas without enough data with similar data based on elevation, distance and pitch (i.e. incline or grade)), ... (2) ... , and
wherein the extracted terrain features include characteristics of the at least one activity trail (see at least Bastide, [0046] disclosing an approach to determining the differences and thus the routing options can be performed by simple rule-based approach. For example, if an area around a user and their destination is grassy and hilly, it can be determined that more calories will be easily burnt, the result of which is that the distance required for user to reach the destination can be quite short <interpreted as characteristics of the at least one activity trial>); and
calculating a unique activity experience for the user to perform a preselected activity on the at least one activity trail based at least in part on the analysed and mapped extracted health features and the extracted terrain features (see at least Bastide, [0037] disclosing a scenario according to an embodiment involves crowdsourcing health data. Let Bob be a user of a wearable device, and suppose Bob has recorded his weight and his height in the devices corresponding smartphone application. Bob's device is paired with his phone and continually monitors his heart rate and his location. Bob's device application synchronizes the data with the device's data cloud. An embodiment of the disclosure can retrieve the data for health and Bob's location and infers the calories burned in a given location or over a limited distance, such as 100 meters, and show it on a map displayed on the user's computing device. For example, FIG. 1 is a map that highlights a route along a public street and indicates that 10 calories were burned along that route; [0046]). But, Bastide does not explicitly teach the following limitations taught in Abuelsaad:
(1) obtaining ... location data for a location that includes at least one activity trail (see at least Abuelsaad, [0029] disclosing that For example, historical data indicating baseball, jogging, running, walking, swimming, tennis, or any other activities is accessed, and a best-fit comparison may be performed by activity determining module 146 to indicate baseball, jogging, running, walking <interpreted as a trail activity>, swimming, tennis is currently being played or performed by the user. The determined activity type is further utilized as discussed herein. In an embodiment of the invention, activity determining module 146 determines a current user activity by comparing received real-time sensor data with a preconfigured range of sensor data limits for the available sensor data, and determines the current user activity via utilization of a best fit analysis or machine learning based upon the preconfigured range of sensor data) ... ; and
(2) wherein the extracted health features include biometric measurements from at least one IoT device (see at least Abuelsaad, [0016] disclosing that biometric sensor(s) 124, environmental sensor(s) 125, and/or location sensor(s) 127 may be, by each be implemented by means of dedicated sensors, or a smartwatch, a fitness tracking device, a mobile computing device, etc. <interpreted as IoT device> Each sensor 124, 125, 127, if present, may be implemented in the same physical device or different ones.) ... .
Bastide and Abuelsaad are analogous art to claim 1 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Bastide, to provide the benefit of (1) obtaining location data for a location that includes at least one activity trail and (2) having the extracted health features include biometric measurements from at least one IoT device, as disclosed in Abuelsaad, with a reasonable expectation of success. Doing so would provide the benefit of tailoring suggestions in response to the activity of the user (see at least Abuelsaad, [0017]).
As per claim 5, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 1, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience includes an estimated personal completion time, route, progress benchmarks, predicted biometrics, and predicted positions of the user while performing the at least one preselected activity on the at least one activity trail (see at least Bastide, [0028] disclosing the use crowdsourcing to determine health improvements routes, by recording location, time, date and health measures, such as activity, blood pressure, speed, ailments, heart rate, calories burned, etc., for users by analyzing a crowd, i.e., a population of users, and their corresponding locations to determine health behaviors in a given location/time to route a given user to a destination based on the crowds healthy behaviors/activity).
As per claim 8, similar to claim 1, Bastide discloses [a] computer program product (CPP) for unique hiking interaction via IoT data assimilation (see at least Bastide, Abstract; [0019] disclosing non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to perform the method steps for determining exercise routes; [0047]; [0073]), the CPP comprising:
one or more computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method (see at least Bastide, [0019]; [0047]), the method comprising:
obtaining health data for a user and location data for a location (see at least Bastide, [0006]; [0011]; [0028]; [0039]) ... (1) ... ;
extracting health features from the health data and terrain features from the location data (see at least Bastide, [0039]; [0045]);
analysing and mapping the extracted health features and the extracted terrain features (see at least Bastide, [0039]; [0045]), ... (2) ..., and
wherein the extracted terrain features include characteristics of the at least one activity trail (see at least Bastide, [0046]); and
calculating a unique activity experience for the user to perform a preselected activity on the at least one activity trail based at least in part on the analysed and mapped extracted health features and the extracted terrain features (see at least Bastide, [0037]; [0046]). But, Bastide does not explicitly teach the following limitations taught in Abuelsaad:
(1) obtaining ... location data for a location that includes at least one activity trail (see at least Abuelsaad, [0029]) ... ; and
(2) wherein the extracted health features include biometric measurements from at least one IoT device (see at least Abuelsaad, [0016]) ... .
Bastide and Abuelsaad are analogous art to claim 8 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer product, as disclosed in Bastide, to provide the benefit of (1) obtaining location data for a location that includes at least one activity trail and (2) having the extracted health features include biometric measurements from at least one IoT device, as disclosed in Abuelsaad, with a reasonable expectation of success. Doing so would provide the benefit of tailoring suggestions in response to the activity of the user (see at least Abuelsaad, [0017]).
As per claim 12, similar to claims 5, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 8, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience includes an estimated personal completion time, route, progress benchmarks, predicted biometrics, and predicted positions of the user while performing the at least one preselected activity on the at least one activity trail (see at least Bastide, [0028]).
As per claim 15, similar to claims 1 and 8, Bastide discloses [a] computer system (CS) for unique hiking interaction via IoT data assimilation (see at least Bastide, Abstract; [0019]; [0073], the CS comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method (see at least Bastide, [0019]), the method comprising:
obtaining health data for a user and location data for a location (see at least Bastide, [0006]; [0011]; [0028]; [0039] ) ... (1) ... ;
extracting health features from the health data and terrain features from the location data (see at least Bastide, [0039]; [0045] );
analysing and mapping the extracted health features and the extracted terrain features (see at least Bastide, [0039]; [0045] ),
wherein the extracted health features include biometric measurements from at least one IoT device (see at least Bastide, [0046]), ... (2) ...,; and
calculating a unique activity experience for the user to perform a preselected activity on the at least one activity trail based at least in part on the analysed and mapped extracted health features and the extracted terrain features (see at least Bastide, [0037]; [0046]). But, Bastide does not explicitly teach the following limitations taught in Abuelsaad:
(1) obtaining ... location data for a location that includes at least one activity trail (see at least Abuelsaad, [0029] ) ... ; and
(2) wherein the extracted health features include biometric measurements from at least one IoT device (see at least Abuelsaad, [0016]) ... .
Bastide and Abuelsaad are analogous art to claim 15 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Bastide, to provide the benefit of (1) obtaining location data for a location that includes at least one activity trail and (2) having the extracted health features include biometric measurements from at least one IoT device, as disclosed in Abuelsaad, with a reasonable expectation of success. Doing so would provide the benefit of tailoring suggestions in response to the activity of the user (see at least Abuelsaad, [0017]).
As per claim 19, similar to claims 5 and 12, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 15, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience includes an estimated personal completion time, route, progress benchmarks, predicted biometrics, and predicted positions of the user while performing the at least one preselected activity on the at least one activity trail (see at least Bastide, [0028]).
Claims 2-4, 6, 9-11, 13, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bastide and Abuelsaad as applied to claims 2, 9 and 16 above, and further in view of U.S. Patent Publication Number 2015/0081062 to Fyfe et al. (hereafter Fyfe).
As per claim 2, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 1, as shown above. But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features (see at least Fyfe, [0032] disclosing that activity manager 120 may also calculate and maintain, within activity record database 130, a route toughness factor that is a number representing an overall difficulty level of a given route or route segment; [0065] disclosing that pacing engine 112 automatically (e.g., periodically and without prompting from the user) identifies a challenge (e.g., a hill) within a current proximity of the user and automatically generates (e.g., by searching for similar activities within other activity records 132) goal pace profile 138 based upon that activity such that the user receives status and coaching cues.).
Bastide, Abuelsaad and Fyfe are analogous art to claim 2 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the users exercise performance.
As per claim 3, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 2, as shown above. Fyfe further discloses the following limitations:
comparing the determined user fitness level to the determined activity trail difficulty level (see at least Fyfe, [0032] disclosing that activity manager 120 determines or adjusts toughness factor empirically by examining the performance of a significant number of athletes on the route or route segment in comparison those same athletes' performances on different routes and/or route segments, and may publish toughness factor to one or more databases (e.g., database 184) <interpreted as the activity trail difficulty level> ),
wherein the calculated unique activity experience is based at least in part on the comparison of the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0032] disclosing that the activity manager 120 may also calculate and maintain, within activity record database 130, a route toughness factor that is a number representing an overall difficulty level of a given route or route segment. The route toughness factor may be calculated in other modules without departing from the scope hereof. Activity manager 120 determines or adjusts toughness factor empirically by examining the performance of a significant number of athletes on the route or route segment in comparison those same athletes' performances on different routes and/or route segments, and may publish toughness factor to one or more databases (e.g., database 184)).
As per claim 4, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 2, as shown above. Fyfe further discloses the following limitations:
selecting the at least one activity trail from among a plurality of activity trails included at the location by comparing the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0030] disclosing that Activity manager 120, when executed by processor 106, operates to manage access to activity record database 130, storing activity records for an athlete (i.e., a user of mobile device 102) or of other people or organizations. For example, activity record database 130 may store activity records 132 from friends, club or group members, and celebrities. Activity manager 120 allows the user to select activity record 132 from external sources (e.g., via wireless interface 150 and/or a wired interface such as USB--not shown) and store activity record 132 within activity record database 130 for use within mobile device 102; [0032] disclosing that the activity manager 120 determines or adjusts toughness factor empirically by examining the performance of a significant number of athletes on the route or route segment in comparison those same athletes' performances on different routes and/or route segments, and may publish toughness factor to one or more databases (e.g., database 184); [0035] disclosing that the goal selection interface 116 allows a user of mobile device 102 to select a route segment and one or more goals for an activity ).
As per claim 6, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 1, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience is displayed on a user interface (see at least Bastide, [0036] disclosing that output of an embodiment of the disclosure is a map displayed on a computing device or directions for determining a route provided to the computing device) ... . But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
dynamically updated based on real-time biometrics, real-time progress, and real-time terrain features (see at least Fyfe, [0038] disclosing that pacing engine 112 operates to monitor the user's position/performance in real-time and compares that performance to goal pace profile 138 to generate status and coaching cues 136; [0039] disclosing Interface device 160 may comprise one or more indicator lamps or other display elements, and/or one or more audio channels, and/or other biofeedback components that are responsive to control by user interface 122. Interface device 160 may represent one of a wrist watch display, a bicycle computer, and a heads-up/heads-down display.).
Bastide, Abuelsaad and Fyfe are analogous art to claim 6 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of dynamically updating, based on real-time biometrics, real-time progress, and real-time terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the user’s exercise performance.
As per claim 9, similar to claim 2, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 8, as shown above. But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features (see at least Fyfe, [0032]).
Bastide, Abuelsaad and Fyfe are analogous art to claim 9 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer product, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the users exercise performance.
As per claim 10, similar to claim 3, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 9, as shown above. Fyfe further discloses the following limitations:
comparing the determined user fitness level to the determined activity trail difficulty level (see at least Fyfe, [0032] ),
wherein the calculated unique activity experience is based at least in part on the comparison of the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0032]).
As per claim 11, similar to claim 4, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 9, as shown above. Fyfe further discloses the following limitations:
selecting the at least one activity trail from among a plurality of activity trails included at the location by comparing the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0030]; [0032]; [0035]).
As per claim 13, similar to claim 6, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 8, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience is displayed on a user interface (see at least Bastide, [0036]) ... . But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
dynamically updated based on real-time biometrics, real-time progress, and real-time terrain features (see at least Fyfe, [0038]; [0039] ).
Bastide, Abuelsaad and Fyfe are analogous art to claim 13 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer product, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of dynamically updating, based on real-time biometrics, real-time progress, and real-time terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the user’s exercise performance.
As per claim 16, similar to claims 2 and 9, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 15, as shown above. But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features (see at least Fyfe, [0032]).
Bastide, Abuelsaad and Fyfe are analogous art to claim 9 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of determining a user fitness level and an activity trail difficulty level based on the analysed extracted health features and the analysed extracted terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the users exercise performance.
As per claim 17, similar to claims 3 and 10, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 16, as shown above. Fyfe further discloses the following limitations:
comparing the determined user fitness level to the determined activity trail difficulty level (see at least Fyfe, [0032] ),
wherein the calculated unique activity experience is based at least in part on the comparison of the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0032]).
As per claim 18, similar to claims 4 and 11, the combination of Bastide, Abuelsaad and Fyfe discloses all of the limitations of claim 16, as shown above. Fyfe further discloses the following limitations:
selecting the at least one activity trail from among a plurality of activity trails included at the location by comparing the determined user fitness level and the determined activity trail difficulty level (see at least Fyfe, [0030]; [0032]; [0035]).
As per claim 20, similar to claims 6 and 13, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 15, as shown above. Bastide further discloses the following limitation:
wherein the calculated unique activity experience is displayed on a user interface (see at least Bastide, [0036] ) ... . But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Fyfe:
dynamically updated based on real-time biometrics, real-time progress, and real-time terrain features (see at least Fyfe, [0038]).
Bastide, Abuelsaad and Fyfe are analogous art to claim 20 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Fyfe relates to systems and methods provide location-based athlete pacing with biofeedback (see Fyfe, abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of dynamically updating, based on real-time biometrics, real-time progress, and real-time terrain features, as disclosed in Fyfe, with a reasonable expectation of success. Doing so would provide the benefit of improving the user’s exercise performance.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bastide and Abuelsaad as applied to claims 1 and 8 above, and further in view of U.S. Patent Publication Number 2022/0108781 to Hendricks.
As per claim 7, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 1, as shown above. But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Hendricks:
wherein the preselected activity is hiking (see at least Hendricks, [0050] disclosing that the user-determined criteria module 108 allows the user to select the type of exercise to be completed by the user. Non-limiting examples of such types of exercise include, but are not limited to, walking, running, cycling, swimming, hiking, and combinations of these exercises), and
wherein the calculated unique activity experience includes notifying the user of at least one of hazards, considerations, suggested routes, landmarks, and suggested equipment via a user interface (see at least Hendricks, [0017] disclosing that the method may also include a step of selecting at least one of a route from a pre-determined list of routes and a user designed route. Where the user designs their own route, a plurality of options may be selected such as route shape, topography, terrain, surroundings, and landmarks, as non-limiting examples. Then, the method may include a step of selecting a warm-up or cool-down; [0056] disclosing that The user-determined criteria module 108 may be configured to allow the user to select the visual stimuli that the user may see while performing the customized route to travel. Non-limited examples include, but are not limited to, landmarks, bodies of water, famous sites, and other similar visual stimuli; [0058] disclosing that the exercise plan creating module 110 may be configured to create, by the system server 104, the customized route to travel based on user-determined criteria. The exercise plan creating module 110 may be configured to transmit the customized route to travel from the system server 104 to the computing platform 102 such as the mobile device through the wide area network or other similar transmissions; [0059] disclosing that the display generating module 112 may be configured to generate a display 144 of the customized route to travel on the mobile display of the computing platform 102 such as the mobile device.).
Bastide, Abuelsaad and Hendricks are analogous art to claim 7 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Hendricks relates to methods, systems, and storage media for creating a customized exercise plan (see at least Hendricks, [0002]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer system, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of having the preselected activity be hiking and having the calculated unique activity experience includes notifying the user of at least one of hazards, considerations, suggested routes, landmarks, and suggested equipment via a user interface, as disclosed in Hendricks, with a reasonable expectation of success. Doing so would provide the benefit of permitting customization of generated routes (see Hendricks, [0009]).
As per claim 14, similar to claim 7, the combination of Bastide and Abuelsaad discloses all of the limitations of claim 8, as shown above. But, neither Bastide nor Abuelsaad explicitly teach the following limitation taught in Hendricks:
wherein the preselected activity is hiking (see at least Hendricks, [0050]), and
wherein the calculated unique activity experience includes notifying the user of at least one of hazards, considerations, suggested routes, landmarks, and suggested equipment via a user interface (see at least Hendricks, [0017]; [0056]; [0059] ).
Bastide, Abuelsaad and Hendricks are analogous art to claim 14 because they are a similar field related to exercise via data processing. Bastide relates to determining exercise routes based on crowdsourcing (see Bastide, [0001]). Abuelsaad relates to the generating and playing back of media playlists, and more specifically to utilizing biometrics and other data to generate and present a media playlist to a user as they perform various activities, including running, jogging, walking (see Abuelsaad, [0001], [0011]). Hendricks relates to methods, systems, and storage media for creating a customized exercise plan (see at least Hendricks, [0002]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer product, as disclosed in Bastide, as modified by Abuelsaad, to provide the benefit of having the preselected activity be hiking and having the calculated unique activity experience includes notifying the user of at least one of hazards, considerations, suggested routes, landmarks, and suggested equipment via a user interface, as disclosed in Hendricks, with a reasonable expectation of success. Doing so would provide the benefit of permitting customization of generated routes (see Hendricks, [0009]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Patent Publication Number 2023/0420102 to Walters, see at least Abstract: A computing system receives, from a user device, a request to generate an estimated activity profile for a user of the user device for a workout. The computing system identifies route information for the workout. The computing system generates, via a trained prediction system, an initial estimated heartrate for the user based on a personalized training process for the user. The computing system generates, via the trained prediction system, a duration of a portion of the workout based on the route information and environmental information associated with a time and day of the workout. The computing system generates, via the trained prediction system, a projected heartrate of the user during the workout based on the initial estimated heartrate of the user and the generated duration. The computing system outputs the estimated activity profile corresponding to the workout based on the projected heartrate of the use.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICK M. BRADY III whose telephone number is (571)272-7458. The examiner can normally be reached Monday - Friday 7:00 am - 4;30 pm.
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PATRICK M. BRADY III
Examiner
Art Unit 3665
/PATRICK M BRADY/Examiner, Art Unit 3665
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3665
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