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
Claims 1-14 and 16-21 are currently pending.
Claims 15 is canceled.
Claim 21 is new.
Claims 1, 5-6, 11, 14, 16, and 19 are amended,
Claims 1-14 and 16-21 are 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 1-14 and 16-21 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 1-14 and 19-21 are directed to a method (i.e., process) and claims 16-18 directed toward a computing device (i.e., machine). 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, 16, and 19 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 method for creating a customized message for a user at a computing device, comprising: receiving, from an exercise device associated with the user, a request to generate the customized message for the user; receiving user data; retrieving historical health data that correlates with the user data; generating a contextual prompt for a neural network model comprising a plurality of parameters executed by one or more processors and one or more memories of the computing device, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; providing the contextual prompt comprising the prompt and the contextual information to the neural network model, wherein the contextual prompt causes the neural network model to create the customized message; and transmitting the customized message to the exercise device associated with the user via a network using a wireless communication protocol”.
The limitations of receiving, a request to generate the customized message for the user; receiving user data; retrieving historical health data that correlates with the user data; generating a contextual prompt comprising a plurality of parameters executed, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; providing the contextual prompt comprising the prompt and the contextual information, wherein the contextual prompt causes to create the customized message; and transmitting the customized message associated with the user, 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 receiving, retrieving, providing, and transmitting, 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.
Additionally, claim 16 recites: “A computing device for facilitating creating a customized message for a user, comprising: one or more processors and one or more memories, the one or more memories comprising instructions which, when executed by the one or more processors, cause the computing device to: receive, from an exercise device associated with the user, a request to generate the customized message for the user; receive user data; retrieve historical health data that correlate with the user data; generate a contextual prompt for a neural network model comprising a plurality of parameters executed by the one or more processors and the one or more memories of the computing device, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; provide the contextual prompt comprising the prompt and the contextual information to the neural network model, wherein the contextual prompt causes the neural network model to create the customized message; and transmit the customized message to the exercise device associated with the user via a network using a wireless communication protocol”.
The limitations of receive, a request to generate the customized message for the user; receive user data; retrieve historical health data that correlate with the user data; generate a contextual prompt comprising a plurality of parameters, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; provide the contextual prompt comprising the prompt and the contextual information, wherein the contextual prompt to create the customized message; and transmit the customized message associated with the user, 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 receive, retrieve, generate, provide, and transmit, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, 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.
Additionally, claim 19 recites: “A method for training a neural network model, comprising: receiving a plurality of historical health data, wherein the plurality of historical health data is divided into plurality of subsets; and training the neural network mode that includes a plurality of parameters until an output of the neural network model corresponds to each subset of historical health data of the plurality of subsets, wherein training the neural network model comprises: providing the plurality of subsets as input for the neural network model; performing a validation for the neural network model based at least in part on the output of the neural network model after providing the plurality of subsets as input, wherein performing the validation for the neural network model comprises a cross-validation, a consistency check, a physical model evaluation, or any combination thereof between the plurality of subsets and the output; identifying at least one portion of the output that does not correspond to the provided plurality of subsets; and adjusting one or more of the plurality of parameters of the neural network model that are associated with the at least one portion of the output to finetune the neural network model”.
The limitations of receiving a plurality of historical health data, wherein the plurality of historical health data is divided into plurality of subsets; and a plurality of parameters until an output corresponds to each subset of historical health data of the plurality of subsets; providing the plurality of subsets as input; performing a validation based at least in part on the output after providing the plurality of subsets as input, wherein performing the validation comprises a cross-validation, a consistency check, a physical model evaluation, or any combination thereof between the plurality of subsets and the output; identifying at least one portion of the output that does not correspond to the provided plurality of subsets; and adjusting one or more of the plurality of parameters that are associated with the at least one portion of the output, 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 receiving, providing, performing, identifying, and adjusting, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, 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.
Dependent claims 2-14, 17-18, and 20-21 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, 16, and 19. 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 1-14 and 16-21 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 “model”, “processors”, “memories”, “neural network model”, “exercise device”, “computing device” “wireless communication protocol”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014], [0027], [0031]-[0033], and [0036]-[0041], 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, “from an exercise device associated with the user”, “for a neural network model”, “by one or more processors and one or more memories of the computing device”, “to the neural network model”, “the neural network model”, “to the exercise device”, “via a network using a wireless communication protocol”, “from an exercise device associated with the user”, “for a neural network model”, “executed by the one or more processors and the one or more memories of the computing device”, “to the neural network model”, “causes the neural network model”, “to the exercise device”, “via a network using a wireless communication protocol”, “training the neural network model that includes”, “of the neural network model”, “wherein training the neural network model comprises”, “for the neural network model”, “for the neural network model”, “of the neural network model”, “for the neural network model”, “of the neural network model”, “to finetune the neural network model”, and “one or more processors and one or more memories, the one or more memories comprising instructions which, when executed by the one or more processors, cause the computing device to”, 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, “receiving, from an exercise device associated with the user, a request to generate the customized message for the user; receiving user data; retrieving historical health data that correlates with the user data”, “receive, from an exercise device associated with the user, a request to generate the customized message for the user; receive user data; retrieve historical health data that correlate with the user data”, and “receiving a plurality of historical health data, wherein the plurality of historical health data is divided into plurality of subsets”, 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 2-14, 17-18, and 20-21 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, 16, and 19, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “model”, “processors”, “memories”, “neural network model”, “exercise device”, “computing device” “wireless communication protocol”) 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 claims 2-14, 17-18, and 20-21 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, 16, and 19, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, from an exercise device associated with the user, a request to generate the customized message for the user; receiving user data; retrieving historical health data that correlates with the user data”, “receive, from an exercise device associated with the user, a request to generate the customized message for the user; receive user data; retrieve historical health data that correlate with the user data”, and “receiving a plurality of historical health data, wherein the plurality of historical health data is divided into plurality of subsets”), 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 1-14 and 16-21 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.
Claims 1-14 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over McKirdy (US-20130032634-A1), in view of Crabtree et al (US-20240386015-A1), hereinafter Crabtree.
Regarding claim 1 McKirdy teaches a method for creating a customized message for a user at a computing device, comprising: receiving, from an exercise device associated with the user, a request to generate the customized message for the user (McKirdy, [0023]: As shown, the method 100 includes generating user data via a device (such as an exercise device, a glucometer, a pedometer, etc.) as shown in operational block 152. The user data is then sent to a barcode generator, as shown in operational block 154, where the barcode generator may be integrated into the device generating the data or the barcode generator may be a separate device. The barcode generator then processes the user data (as discussed further herein below) and generates barcode data, as shown in operational block 156. It should be appreciated that the barcode data may be in graphical form (i.e. a barcode) or the barcode data may be in data form where a separate device can generate the graphical barcode (such as a printer or a device with a display screen). The barcode data may then be displayed in graphical form via a display screen (or via a physical printout), as shown in operational block 158. A user may then upload the barcode data by scanning the barcode with their mobile device, as shown in operational block 160, where the mobile device processes the barcode data and if necessary, operates in response to the barcode data, as shown in operational block 162”, [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0027]: “When the data is collected, the exercise device (or other type of equipment) may then generate a barcode responsive to the collected data and display the barcode. The barcode data may be displayed via display device (i.e. LCD, LED, etc.) or the barcode data may be printed out. It should be appreciated that the collected data may include any type of data suitable to the desired result, such as individual and/or machine performance data, machine setting/operation data, programmed regime data and/or biological data (such as heart rate, pulse rate, body fat, weight, height, pulse ox, etc.). Representing the data in a barcode format advantageously allows this data to be easily and securely collected and transfer between devices”, [0028]: “the barcode generator device may be an add-on module (in communication with sensors from the machine and/or sensors attached to the person collecting data) to or it may be a feature integrated with the exercise device”, [0082], [0104]: “Furthermore, the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes”); receiving user data (McKirdy, [0024]: “the method 200 includes receiving user data, as shown in operational block 202”); retrieving historical health data that correlates with the user data (McKirdy, [0031]: “The barcode information might contain the overall exercise history of the individual, the threshold levels specific to that person (heart rate limits, weight limits for lifting, workout set limits for lifting, etc.), information for how long the workout should last for the user, user biometric info (body weight, body fat %, etc.), medical condition information (heart disease, diabetic, etc.), etc.”); and transmitting the customized message to the exercise device associated with the user via a network using a wireless communication protocol (McKirdy, FIG. 8, [0019], [0029]: “It accordance with the present invention, the data may be processed and displayed directly to the user via the mobile device, exercise device and/or the data may be sent (via hardwire, cellular or wireless network) to a backend system for processing and/or display”, [0039]-[0041]: “allowing the user's mobile device to wirelessly connect to the exercise machine or aftermarket TV display or other peripheral devices using NFC technologies (RFID, Bluetooth, Bluetooth Low Energy, Wi-Fi, ZigBee, Ant+, etc.), the system can now allow the user to authenticate themselves on the system based on user profile information that might be stored already on their mobile device”, and [0104]: “the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes. Additionally, radio stations can create business models around selling music air time that is linked to the most popular songs or even talk show radios”).
McKirdy does not teach generating a contextual prompt for a neural network model comprising a plurality of parameters executed by one or more processors and one or more memories of the computing device, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; providing the contextual prompt comprising the prompt and the contextual information to the neural network model, wherein the contextual prompt causes the neural network model to create the customized message.
However, Crabtree teaches generating a contextual prompt for a neural network model (Crabtree, [0091], [0104]: “Various compositional models have been proposed, such as additive models, multiplicative models, and neural network-based models (e.g., recursive neural networks, transformers)”, and [0142]: “The computed embeddings may then be persisted in memory or in a database such as a vector database, which allows for fast and scalable similarity search (e.g., cosine, dot product, Euclidean, etc.) and other vector operations or graph operations or hybrid representations depending on the data type, representation, and elements such as facts, spatial or temporal dynamics of the systems and/or entities of interest. The persisted embeddings serve as input features for downstream ML or AI models, such as neural networks or symbolic reasoning engines, or knowledge base”) comprising a plurality of parameters executed by one or more processors and one or more memories of the computing device (Crabtree, [0030]-[0033]: “returning relevant results by leveraging the vector semantic indices, knowledge graphs, and contextual information” and [0091]: “Additional indices linking vectorized data element representations to ontology elements are created and iteratively refined using contextual information from comparisons between ontological data from knowledge graphs containing facts, entities, and relations using at least vector similarity comparison as part of a comparative objective function for relevance”), wherein the contextual prompt comprises a prompt (Crabtree, [0168]: “According to some embodiments, platform 2120 can be leveraged to develop enterprise-specific or domain-specific models, which can be “small models” that are more efficient, accurate, or predictable in specific contexts and prompts” and [0254]-[0254]) and contextual information (Crabtree, [0091, [0093]: “Contextual information, such as user preferences, search history, device from which a query or recommendation is being sought, recent history of environmental conditions and movement (e.g., just ran through the rain), and location (historical, present and planned-such as from an upcoming calendar invite), plays a role in guiding the reasoning and inference process the system can employ to maximize search or recommendation relevance with minimal user interaction requirements. The system leverages this context to personalize and refine the results, ensuring their relevance to the user's specific needs and intentions”) based on the user data and the historical health data that correlates with the user data (Crabtree, [0030]-[0033]: “returning relevant results by leveraging the vector semantic indices, knowledge graphs, and contextual information” and [0091]: “Additional indices linking vectorized data element representations to ontology elements are created and iteratively refined using contextual information from comparisons between ontological data from knowledge graphs containing facts, entities, and relations using at least vector similarity comparison as part of a comparative objective function for relevance”); providing the contextual prompt comprising the prompt and the contextual information to the neural network model (Crabtree, [0091], [0104]: “Various compositional models have been proposed, such as additive models, multiplicative models, and neural network-based models (e.g., recursive neural networks, transformers)”, [0142]: “The computed embeddings may then be persisted in memory or in a database such as a vector database, which allows for fast and scalable similarity search (e.g., cosine, dot product, Euclidean, etc.) and other vector operations or graph operations or hybrid representations depending on the data type, representation, and elements such as facts, spatial or temporal dynamics of the systems and/or entities of interest. The persisted embeddings serve as input features for downstream ML or AI models, such as neural networks or symbolic reasoning engines, or knowledge base”, and [0392]: “For example, in text generation, the model could be conditioned on a user's previous messages or writing style to generate more personalized responses”), wherein the contextual prompt causes the neural network model to create the customized message (Crabtree, [0282]: “FIG. 13 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 1200, according to one aspect. According to the aspect, a variant messaging arrangement may utilize messaging system 1210 as a messaging broker using a streaming protocol 1310, transmitting and receiving messages immediately using messaging system 1210 as a message broker to bridge communication between service actors 1221 a-b as needed”, [0387]: “For example, in text generation, the model could be conditioned on a user's previous messages or writing style to generate more personalized responses”, and [0392]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Regarding claim 2 McKirdy further teaches the customized message comprises a customized activity suggestion (McKirdy, [0077]: “Thus, this method would allow a person to see a plurality of exercise types related to a specific machine or muscle group. This method would provide an advantage to a person for following a custom designed workout regimen that a personal trainer or expert might have prescribed for them” and [0122]).
Regarding claim 3 McKirdy further teaches the customized activity suggestion comprises an exercise, an exercise length, an intensity level of the exercise, an exercise date, an exercise start time, recommended recovery time, dietary information, or any combination thereof (McKirdy, [0043]: “The data capture approach described above can also apply the data that is obtained to drive advertisements that might be responsive to exercise performance indicators (duration, exertion/intensity, exercise machine performance, evaluation of form during the exercise as compared to a standard for a scoring system)”, [0055], and [0081]-[0082]).
Regarding claim 4 McKirdy further teaches the customized message comprises a customized motivational message (McKirdy, [0043]: “Thus, a higher level of motivation would be provided, allowing the user to apply their personalized workout performance data that is optically captured to motivational member rewards and loyalty programs offered to them”, [0057]: “This can be useful for helping retain memberships and provide motivation for the user to participate in such content programming by adding a reward system into the mix”, [0068]: “ Additionally, the invention can be used to create a motivation system/program for the user where the user may be rewarded and incentivized”, and [0081]-[0082]).
Regarding claim 5 McKirdy further teaches receiving the user data comprises: receiving the user data from a mobile device (McKirdy, [0024], [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0082], [0104]: “Furthermore, the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes”).
Regarding claim 6 McKirdy further teaches receiving the user data comprises: receiving the user data from the exercise device (McKirdy, [0023]: As shown, the method 100 includes generating user data via a device (such as an exercise device, a glucometer, a pedometer, etc.) as shown in operational block 152. The user data is then sent to a barcode generator, as shown in operational block 154, where the barcode generator may be integrated into the device generating the data or the barcode generator may be a separate device. The barcode generator then processes the user data (as discussed further herein below) and generates barcode data, as shown in operational block 156. It should be appreciated that the barcode data may be in graphical form (i.e. a barcode) or the barcode data may be in data form where a separate device can generate the graphical barcode (such as a printer or a device with a display screen). The barcode data may then be displayed in graphical form via a display screen (or via a physical printout), as shown in operational block 158. A user may then upload the barcode data by scanning the barcode with their mobile device, as shown in operational block 160, where the mobile device processes the barcode data and if necessary, operates in response to the barcode data, as shown in operational block 162”, [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0027]: “When the data is collected, the exercise device (or other type of equipment) may then generate a barcode responsive to the collected data and display the barcode. The barcode data may be displayed via display device (i.e. LCD, LED, etc.) or the barcode data may be printed out. It should be appreciated that the collected data may include any type of data suitable to the desired result, such as individual and/or machine performance data, machine setting/operation data, programmed regime data and/or biological data (such as heart rate, pulse rate, body fat, weight, height, pulse ox, etc.). Representing the data in a barcode format advantageously allows this data to be easily and securely collected and transfer between devices”, [0028]: “the barcode generator device may be an add-on module (in communication with sensors from the machine and/or sensors attached to the person collecting data) to or it may be a feature integrated with the exercise device” and [0030]).
Regarding claim 7 McKirdy further teaches the user data comprises a training history, a goal, patterns in training, an activity level, injuries, strengths, weaknesses, or motivation information (McKirdy, [0031]: “The barcode information might contain the overall exercise history of the individual, the threshold levels specific to that person (heart rate limits, weight limits for lifting, workout set limits for lifting, etc.), information for how long the workout should last for the user, user biometric info (body weight, body fat %, etc.), medical condition information (heart disease, diabetic, etc.), etc. The machine might then automatically adjust itself based on the parameters provided by the barcode and/or the machine may also monitor the individual for over exertion or physical failure (i.e. heart attack, asthma attack, etc.) where machine can alert the fitness club workers or call emergency services”).
Regarding claim 8 McKirdy further teaches the patterns in training comprise a time spent on an activity, a time between activities, preferred weekdays for performing an activity, preferred hours for performing an activity, or any combination thereof (McKirdy, [0031]: “The barcode information might contain the overall exercise history of the individual, the threshold levels specific to that person (heart rate limits, weight limits for lifting, workout set limits for lifting, etc.), information for how long the workout should last for the user, user biometric info (body weight, body fat %, etc.), medical condition information (heart disease, diabetic, etc.), etc. The machine might then automatically adjust itself based on the parameters provided by the barcode and/or the machine may also monitor the individual for over exertion or physical failure (i.e. heart attack, asthma attack, etc.) where machine can alert the fitness club workers or call emergency services” and [0055]).
Regarding claim 9 McKirdy further teaches the training history comprises one or more activities performed by the user (McKirdy, [0028] and [0077]: “Thus, this method would allow a person to see a plurality of exercise types related to a specific machine or muscle group. This method would provide an advantage to a person for following a custom designed workout regimen that a personal trainer or expert might have prescribed for them”).
Regarding claim 10 McKirdy further teaches the strengths, the weaknesses, or both are identified by the user (McKirdy, [0003] and [0106]).
Regarding claim 11 McKirdy further teaches the strengths, the weaknesses, or both are automatically identified by the exercise device or a mobile device (McKirdy, [0003] and [0106]).
Regarding claim 12 McKirdy further teaches the historical health data comprises exercise programs, behavioral science information, behavioral health science, injury recovery information, or any combination thereof (McKirdy, [0027], [0034]-[0035], and [0040]).
Regarding claim 13 McKirdy does not teach generating the contextual prompt comprises: generating the contextual prompt with a retrieval augmented generation (RAG) system.
However, Crabtree teaches generating the contextual prompt comprises: generating the contextual prompt with a retrieval augmented generation (RAG) system (Crabtree, [0163]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Regarding claim 14 McKirdy does not teach the neural network model is a large language model (LLM).
However, Crabtree teaches the neural network model is a large language model (LLM) (Crabtree, [0090] and [0183]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Regarding claim 16 McKirdy teaches a computing device for facilitating creating a customized message for a user, comprising: one or more processors and one or more memories, the one or more memories comprising instructions which, when executed by the one or more processors (McKirdy, [0130]), cause the computing device to: receive, from an exercise device associated with the user, a request to generate the customized message for the user (McKirdy, [0023]: As shown, the method 100 includes generating user data via a device (such as an exercise device, a glucometer, a pedometer, etc.) as shown in operational block 152. The user data is then sent to a barcode generator, as shown in operational block 154, where the barcode generator may be integrated into the device generating the data or the barcode generator may be a separate device. The barcode generator then processes the user data (as discussed further herein below) and generates barcode data, as shown in operational block 156. It should be appreciated that the barcode data may be in graphical form (i.e. a barcode) or the barcode data may be in data form where a separate device can generate the graphical barcode (such as a printer or a device with a display screen). The barcode data may then be displayed in graphical form via a display screen (or via a physical printout), as shown in operational block 158. A user may then upload the barcode data by scanning the barcode with their mobile device, as shown in operational block 160, where the mobile device processes the barcode data and if necessary, operates in response to the barcode data, as shown in operational block 162”, [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0027]: “When the data is collected, the exercise device (or other type of equipment) may then generate a barcode responsive to the collected data and display the barcode. The barcode data may be displayed via display device (i.e. LCD, LED, etc.) or the barcode data may be printed out. It should be appreciated that the collected data may include any type of data suitable to the desired result, such as individual and/or machine performance data, machine setting/operation data, programmed regime data and/or biological data (such as heart rate, pulse rate, body fat, weight, height, pulse ox, etc.). Representing the data in a barcode format advantageously allows this data to be easily and securely collected and transfer between devices”, [0028]: “the barcode generator device may be an add-on module (in communication with sensors from the machine and/or sensors attached to the person collecting data) to or it may be a feature integrated with the exercise device”, [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0082], [0104]: “Furthermore, the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes”); receive user data (McKirdy, [0024]: “the method 200 includes receiving user data, as shown in operational block 202”); retrieve historical health data that correlate with the user data (McKirdy, [0031]: “The barcode information might contain the overall exercise history of the individual, the threshold levels specific to that person (heart rate limits, weight limits for lifting, workout set limits for lifting, etc.), information for how long the workout should last for the user, user biometric info (body weight, body fat %, etc.), medical condition information (heart disease, diabetic, etc.), etc.”); and transmit the customized message to the exercise device associated with the user via a network using a wireless communication protocol (McKirdy, FIG. 8, [0019], [0029]: “It accordance with the present invention, the data may be processed and displayed directly to the user via the mobile device, exercise device and/or the data may be sent (via hardwire, cellular or wireless network) to a backend system for processing and/or display”, [0039]-[0041]: “allowing the user's mobile device to wirelessly connect to the exercise machine or aftermarket TV display or other peripheral devices using NFC technologies (RFID, Bluetooth, Bluetooth Low Energy, Wi-Fi, ZigBee, Ant+, etc.), the system can now allow the user to authenticate themselves on the system based on user profile information that might be stored already on their mobile device”, and [0104]: “the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes. Additionally, radio stations can create business models around selling music air time that is linked to the most popular songs or even talk show radios”).
McKirdy does not teach generate a contextual prompt for a neural network model comprising a plurality of parameters executed by the one or more processors and the one or more memories of the computing device, wherein the contextual prompt comprises a prompt and contextual information based on the user data and the historical health data that correlates with the user data; provide the contextual prompt comprising the prompt and the contextual information to the neural network model, wherein the contextual prompt causes the neural network model to create the customized message.
However, Crabtree teaches generate a contextual prompt for a neural network model (Crabtree, [0091], [0104]: “Various compositional models have been proposed, such as additive models, multiplicative models, and neural network-based models (e.g., recursive neural networks, transformers)”, and [0142]: “The computed embeddings may then be persisted in memory or in a database such as a vector database, which allows for fast and scalable similarity search (e.g., cosine, dot product, Euclidean, etc.) and other vector operations or graph operations or hybrid representations depending on the data type, representation, and elements such as facts, spatial or temporal dynamics of the systems and/or entities of interest. The persisted embeddings serve as input features for downstream ML or AI models, such as neural networks or symbolic reasoning engines, or knowledge base”) comprising a plurality of parameters executed by the one or more processors and the one or more memories of the computing device (Crabtree, [0030]-[0033]: “returning relevant results by leveraging the vector semantic indices, knowledge graphs, and contextual information” and [0091]: “Additional indices linking vectorized data element representations to ontology elements are created and iteratively refined using contextual information from comparisons between ontological data from knowledge graphs containing facts, entities, and relations using at least vector similarity comparison as part of a comparative objective function for relevance”), wherein the contextual prompt comprises a prompt (Crabtree, [0168]: “According to some embodiments, platform 2120 can be leveraged to develop enterprise-specific or domain-specific models, which can be “small models” that are more efficient, accurate, or predictable in specific contexts and prompts” and [0254]-[0254]) and contextual information (Crabtree, [0091, [0093]: “Contextual information, such as user preferences, search history, device from which a query or recommendation is being sought, recent history of environmental conditions and movement (e.g., just ran through the rain), and location (historical, present and planned-such as from an upcoming calendar invite), plays a role in guiding the reasoning and inference process the system can employ to maximize search or recommendation relevance with minimal user interaction requirements. The system leverages this context to personalize and refine the results, ensuring their relevance to the user's specific needs and intentions”) based on the user data and the historical health data that correlates with the user data (Crabtree, [0030]-[0033]: “returning relevant results by leveraging the vector semantic indices, knowledge graphs, and contextual information” and [0091]: “Additional indices linking vectorized data element representations to ontology elements are created and iteratively refined using contextual information from comparisons between ontological data from knowledge graphs containing facts, entities, and relations using at least vector similarity comparison as part of a comparative objective function for relevance”); provide the contextual prompt comprising the prompt and the contextual information to the neural network model (Crabtree, [0091], [0104]: “Various compositional models have been proposed, such as additive models, multiplicative models, and neural network-based models (e.g., recursive neural networks, transformers)”, [0142]: “The computed embeddings may then be persisted in memory or in a database such as a vector database, which allows for fast and scalable similarity search (e.g., cosine, dot product, Euclidean, etc.) and other vector operations or graph operations or hybrid representations depending on the data type, representation, and elements such as facts, spatial or temporal dynamics of the systems and/or entities of interest. The persisted embeddings serve as input features for downstream ML or AI models, such as neural networks or symbolic reasoning engines, or knowledge base”, and [0392]: “For example, in text generation, the model could be conditioned on a user's previous messages or writing style to generate more personalized responses”), wherein the contextual prompt causes the neural network model to create the customized message (Crabtree, [0282]: “FIG. 13 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 1200, according to one aspect. According to the aspect, a variant messaging arrangement may utilize messaging system 1210 as a messaging broker using a streaming protocol 1310, transmitting and receiving messages immediately using messaging system 1210 as a message broker to bridge communication between service actors 1221 a-b as needed”, [0387]: “For example, in text generation, the model could be conditioned on a user's previous messages or writing style to generate more personalized responses”, and [0392]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Regarding claim 17 McKirdy further teaches the customized message comprises a customized activity suggestion (McKirdy, [0077]: “Thus, this method would allow a person to see a plurality of exercise types related to a specific machine or muscle group. This method would provide an advantage to a person for following a custom designed workout regimen that a personal trainer or expert might have prescribed for them” and [0122]).
Regarding claim 18 McKirdy further teaches the historical health data comprises exercise programs, behavioral science information, behavioral health science, injury recovery information, or any combination thereof (McKirdy, [0027], [0034]-[0035], and [0040]).
Regarding claim 19 McKirdy teaches receiving a plurality of historical health data, wherein the plurality of historical health data is divided into plurality of subsets (McKirdy, [0024], [0055]: “This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)”, [0082], [0104]: “Furthermore, the scanning of the advertisement might prompt an automatic text message to be sent to the user's phone when the barcode from the radio display is scanned. That text message might also be an email or other message variant that is used to communicate the advertising message, all the while allowing tracking of the advertisement back to the 3rd party and the radio station for ROI reporting purposes”).
McKirdy does not teach a method for training a neural network model, training the neural network model that includes a plurality of parameters until an output of the neural network model corresponds to each subset of historical health data of the plurality of subsets, wherein training the neural network model comprises: providing the plurality of subsets as input for the neural network model; performing a validation for the neural network model based at least in part on the output of the neural network model after providing the plurality of subsets as input, wherein performing the validation for the neural network model comprises a cross-validation, a consistency check, a physical model evaluation, or any combination thereof between the plurality of subsets and the output; identifying at least one portion of the output that does not correspond to the provided plurality of subsets; and adjusting one or more of the plurality of parameters of the neural network model that are associated with the at least one portion of the output to finetune the neural network model.
However, Crabtree teaches a method for training a neural network model (Crabtree, [0184]: “Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content” and [0185]: “Modern LLMs that have emerged within the last decade are based on transformer models, which are neural networks commonly referred to as transformers. With a large number of parameters and the transformer model, LLMs are able to understand and generate accurate responses rapidly, which makes the AI technology broadly applicable across many different domains”), training the neural network model that includes a plurality of parameters (Crabtree, [0184]: “Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content” and [0185]: “Modern LLMs that have emerged within the last decade are based on transformer models, which are neural networks commonly referred to as transformers. With a large number of parameters and the transformer model, LLMs are able to understand and generate accurate responses rapidly, which makes the AI technology broadly applicable across many different domains”) until an output of the neural network model corresponds to each subset of historical health data of the plurality of subsets, wherein training the neural network model comprises (Crabtree, [0093], [0315], [0361]: “Platform may maintain a historical record of data updates, modifications, and deletions to ensure traceability, as well as utilize data cataloging and discovery tools to facilitate easy searching and understanding of the data lineage”, and [0421]: “With respect to contextual analysis, the system may leverage user profiles. This may comprise maintaining comprehensive user profiles that include historical data, preferences, interaction history, and contextual information and continuously updating user profiles with new data to keep the context relevant and current”): providing the plurality of subsets as input for the neural network model (Crabtree, [0143], [0149], and [0188]); performing a validation for the neural network model based at least in part on the output of the neural network model after providing the plurality of subsets as input (Crabtree, [0134] and [0227]: “DCG computing system 121 can take this information and automatically create the workflow, with all the requisite data pipelines, to enable the retrieval of the appropriate data from the appropriate data sources, the processing/preprocessing of the obtained data to be used as inputs into the selected algorithm(s), the training loop to iteratively train the selected algorithms including model validation and testing steps, deploying the trained model, and finally continuously refining the model over time to improve performance”), wherein performing the validation for the neural network model comprises a cross-validation (Crabtree, [0331]: “Model selection techniques, such as cross-validation or Bayesian optimization, may be used to identify the best-performing models or blending strategies for different query types or domains”), a consistency check (Crabtree, [0096], [0150]: “One area where metadata is proving valuable is in improving zero-shot learning through the use of knowledge graphs. Metadata can also help improve out-of-sample generalizability and ensure safety guarantees in neural control systems. Additionally, structured background knowledge is being leveraged to enhance coherence and consistency in neural sequence models”, and [0254]: “Other examples of prompt engineering that may be implemented in various embodiments include, but are not limited to, chain-of-thought, self-consistency, generated knowledge, tree of thoughts, directional stimulus, and/or the like”), a physical model evaluation (Crabtree, [0250] and [0254]: “Other examples of prompt engineering that may be implemented in various embodiments include, but are not limited to, chain-of-thought, self-consistency, generated knowledge, tree of thoughts, directional stimulus, and/or the like”)), or any combination thereof between the plurality of subsets and the output (Crabtree, [0134], [0148], and [0153]); identifying at least one portion of the output that does not correspond to the provided plurality of subsets (Crabtree, [0134], [0349], and [0427]: “For model consensus, comparative analysis may comprise comparing outputs from multiple models to ensure consistency and accuracy and feature mapping to map features across models to identify commonalities and differences, enhancing understanding”); and adjusting one or more of the plurality of parameters of the neural network model that are associated with the at least one portion of the output to finetune the neural network model (Crabtree, [0094], [0185]: “Modern LLMs that have emerged within the last decade are based on transformer models, which are neural networks commonly referred to as transformers. With a large number of parameters and the transformer model, LLMs are able to understand and generate accurate responses rapidly, which makes the AI technology broadly applicable across many different domains. Some LLMs are referred to as foundation models, a term coined by the Stanford Institute for Human-Centered Artificial Intelligence in 2021. A foundation model is so large and impactful that it serves as the foundation for further optimizations and specific use cases”, [0194]-[0195], and [0359]: “Combining the outputs of multiple models and dynamically adjusting their contributions based on relevant factors allows for more robust, accurate, and adaptable AI systems”). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Regarding claim 20 McKirdy further teaches receiving the plurality of historical health data comprises: preprocessing the plurality of historical health data (McKirdy, [0031], [0049], [0074], [0079], and [0092]).
Regarding claim 21 McKirdy does not teach training the neural network model further comprises: determining whether the output of the neural network model corresponds to each subset of historical health data of the plurality of subsets, wherein the neural network model is trained iteratively until the output of the neural network model corresponds to each subset of historical health data of the plurality of subsets.
However, Crabtree teaches training the neural network model further comprises: determining whether the output of the neural network model corresponds to each subset of historical health data of the plurality of subsets, wherein the neural network model is trained iteratively until the output of the neural network model corresponds to each subset of historical health data of the plurality of subsets (Crabtree, [0091]: “This iterative refinement process allows the system to continuously learn and improve the accuracy and relevance of its links between vector semantic representations”, [0131]: “it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise”, [0138]: “Platform 2120 can provide an iterative multi-dimensional optimization and evaluation process to explore the relative performance of the different techniques, datasets, and “fitness of purpose” definitions (e.g., security, licenses, traceability/provenance, etc.) associated with a plurality of AI models”, [0144], [0160], and [0227]: “DCG computing system 121 can take this information and automatically create the workflow, with all the requisite data pipelines, to enable the retrieval of the appropriate data from the appropriate data sources, the processing/preprocessing of the obtained data to be used as inputs into the selected algorithm(s), the training loop to iteratively train the selected algorithms including model validation and testing steps, deploying the trained model, and finally continuously refining the model over time to improve performance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify McKirdy to incorporate the teachings of Crabtree and account for large-scale distributed computing, and more particularly to programmatically or declaratively constructed distributed graph- based computing platforms for artificial intelligence based search, knowledge curation, decision- making, and automation systems including those employing simulations, machine learning models, and artificial intelligence (AI) applications including large language models, generative AI, and associated Al-related services across or amongst heterogeneous cloud, large scale automation and control systems, managed data center, edge devices, and wearable/mobile devices (Crabtree, Abstract and [0026]).
Response to Arguments
Applicant's arguments filed 03/27/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues the claims do not recite an abstract idea of a certain method of organizing human activity, more specifically managing personal behavior or relationships or interactions between people. Examiner respectfully disagrees. The amendments of “training a neural network”, “at a computing device”, etc. are additional elements that are not part of the abstract idea. These additional elements are recited at a high level of generality such that they amount to mere computer tools (see MPEP 2106.04(a)(2)(III)C: “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process”, Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018), FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016), and Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699). MPEP 210604(a)(2)(II) recites “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”. As stated above, the limitations of receiving, providing, performing, identifying, and adjusting are activities a person can perform with a computer (the additional elements of “model”, “processors”, “memories”, “neural network model”, “exercise device”, “computing device” “wireless communication protocol”).
Applicant also argues any alleged abstract idea is integrated into a practical application because the claims include features that improve the technical fields of exercise device control and user-specific message generation. Applicant also claim 19 integrates any alleged abstract idea into a practical application because it recites features that improve the accuracy and tuning of neural networks models. Applicant argues the claims recite the features that improve the technology (as described in the specification) of storing massive amounts of exercise programs make it difficult for a user to searching and find exercise programs. Applicant argues “receiving, from an exercise device associated with the user, a request to generate the customized message for the user," "generating a contextual prompt for a neural network model ... compris[ing] a prompt and contextual information," "providing the contextual prompt ... to the neural network model ... to create the customized message," and "transmitting the customized message to the exercise device associated with the user via a network using a wireless communication protocol," as recited in amended independent claim 1, an exercise device may be controlled automatically, "the user is provided with an activity suggestion that takes into account user's injuries, goals, strengths, weaknesses, preferences, etc.," and the system "provides a customized activity backed up with health data science, behavioral science, and injury recovery information”. Examiner respectfully disagrees. As stated above, the additional elements of “model”, “processors”, “memories”, “neural network model”, “exercise device”, “computing device” “wireless communication protocol” are recited a high level such that they amount to merely “apply it” to the abstract idea. See MPEP 2106.05(f) recites “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)” and “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)”. Apply the abstract idea to the additional elements do not integrate the abstract idea, nor do the claims amount to significantly more under Step 2b (see MPEP 2106.05 recites “Based on this analysis, the Court concluded that the claims amounted to “‘nothing significantly more’ than an instruction to apply the abstract idea of intermediated settlement using some unspecified, generic computer”, and therefore held the claims ineligible because they were directed to a judicial exception and failed the second part of the Alice/Mayo test. Alice Corp., 573 U.S. at 225-27, 110 USPQ2d at 1984”). With respect to Applicant’s arguments of improving selection of exercise programs in a massive library of exercises, it is not a technological improvement, but a business practice improvement. An abstract idea cannot integrate itself into a practical application. Additionally, it is unclear how the limitations in claim 1 can automatically control an exercise device. The claims only appear to suggest a customized activity based on a customized message. Therefore, the 35 U.S.C. 101 Rejection.
Regarding the 35 U.S.C. 102 Rejection, Applicant argues the prior art, McKirdy does not teach the amended features of claims 1 and 16. Applicant further argues the prior art does not teach exercise device or “transmitting the customized message to the exercise device”. Furthermore, Applicant argues McKirdy does not teach the amendments to independent claim 19 (i.e., training a neural network). Examiner respectfully disagrees. Applicant’s arguments with respect to claims 1, 16, and 19 regarding training the neural network have been considered but are moot because the new ground of rejection does not rely the reference, McKirdy, applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. However, under broadest reasonable interpretation, McKirdy does teach exercise device, see paragraph [0133] recites “An apparatus and method for processing exercise information is provided, wherein the method includes obtaining exercise information, processing exercise information responsive to predetermined goals to generate resultant information and displaying the resultant information” and [0023] recites “ the method 100 includes generating user data via a device (such as an exercise device, a glucometer, a pedometer, etc.) as shown in operational block 152”. McKirdy also discloses [0044] recites “This ability allows for a greatly increase capability of messaging and advertising since other systems can only display ad content on a couple dedicated channels, which greatly limits the ability to target the ad placement and viewing of the content by the user since the user can change the channel to watch programming that cannot display targeted advertising or messaging”, [0046] recites “he invention may use the facial recognition software to better control the advertising content/messaging that can be displayed to the user during a workout session. In this case, the invention can employ camera technology (webcam, mobile phone camera, etc.) that is working with facial recognition software and that allows the system to gather data on the user when they are in front of the aftermarket TV display or on an exercise machine that has either a camera hardware device integrated or externally mounted onto it. The software in this instance may be able to verify the gender and age of the user, allowing this data to be captured and used as part of a calculation that drives what type of image file/advertisement should be displayed to the user”, and [0056] recites “the barcode data may include data entered by hand into the mobile device, machine type and characteristics, date/time stamp for the workout/data transaction (this might entered into the mobile device as well if the machine is not capable of creating this), TV channel viewing information (channel viewed, duration of viewing, facility location ID (club ID code), MAC Address info from WIFI hardware that identifies the specific machine, embedded points/rewards output based on workout performance (Gamification output), user ID info (from manual entry or from some external device that uses wireless/wired technology to input a user ID number like RFID key fobs and USB device serial number, manufacturer type, calories, duration, speed, heart rate, incline, exercise program info, resistance, distance (vertical and horizontal), all info related to the health and performance of the machine (such as error codes, utilization, serial number, software version info, etc.), advertising or messaging info that is displayed and streamed by the machine interface that might be sent from some 3rd party advertising/content management source (local PC or networked server) that is directly wired into the machine or sent wirelessly). This would advantageously allow a fitness center to display messages to a user that can be scanned onto their mobile device (such as coupons, programming information, promotions, events, etc.)” (e.g., transmitting the customized message to the exercise device). Therefore, the prior art still teaches the amendments to the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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).
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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.
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/R.S.S./Examiner, Art Unit 3681 /PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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