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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “In response, the independent claims, namely claims 1, 8, and 15, have been amended to recite limitations that cannot be considered to be methods of organizing human activity. As amended, the claims now recite a number of limitations that are not organizing human activity:… It is respectfully submitted that the claims, as amended, are not directed to any grouping of abstract ideas, including the "organizing human activity" grouping of abstract ideas.,” Examiner respectfully disagrees with Applicant’s assertion. Applicant has cited several abstract limitations, including analyzing and recommending, that are part of the abstract limitations for consideration under Step 2A, Prong 1. These limitations, including the other abstract limitations of the claims, as drafted, but for the language of “client device,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “client device,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “client device” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Regarding Applicant’s assertion of “At least the above-mentioned limitations recited in amended claims 1, 8 and 15 cannot be considered methods of organizing human activity. For example, joining, by the bot user, the first event as a participant by accessing one or more Application Programming Interfaces (APIs) of an event server operated by an audio and video conferencing provider for the first event. This functionality cannot be performed by utilizing any general-purpose computer and the Office Action has not provided any evidence that a regular general-purpose computer is able to perform this technical functionality. Similarly, recording the first event by the bot user by accessing the APIs of the server cannot be considered a form of organizing human activity.” Examiner respectfully disagrees with Applicant’s assertion. The use of an API to transmit data to the bot server, as drafted, is nothing more than mere use of a computer as a tool. As can be seen in paragraph [0033], APIs are used to provide the information to the bot server that participates in the event session. This is nothing more than mere use of a computer as a tool to transmit data. Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “Furthermore, generating one or more informational tags by analyzing pre-event content including an event invitation associated with the first event and pre-event notes associated with the first event for keywords or key phrases and generating a first informational tag based on analyzing the pre-event content, and analyzing session content from the recording of the first event by performing speech- to-text conversion of audio information in the recording of the first event, generating an event transcript based on the speech-to-text conversion and extracting a second informational tag from the event transcript cannot be considered merely forms of organizing human activity.” Examiner respectfully disagrees with Applicant’s assertion. Examiner respectfully asserts that the limitation of performing speech to text conversion, as drafted, is a part of the additional elements for consideration under Step 2A, Prong 2 and Step 2B. However, the other limitations cited above, such as analyzing session content, generating a transcript, and extracting a tag, as drafted, are part of the abstract limitations for consideration under Step 2A, Prong 1. These limitations, under considerations of the broadest reasonable interpretation, do not recite any particular additional elements for consideration under Step 2A, Prong 2 or Step 2B.
Regarding Applicant’s assertion of “Finally, training a reinforcement learning model that increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations, as well as providing the second recommendation to the client device in a format configured for automated integration with a user interface of the client device, are all steps that are not merely organizing human activity.,” Examiner respectfully disagrees with Applicant’s assertion. With respect to the limitation of training the model, this additional element, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). With respect to the limitation of “providing,” this limitation, as drafted, is nothing more than mere use of a computer as a tool. Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “Furthermore, even if the claims were directed to an abstract idea, it is respectfully submitted that the abstract idea is integrated into a practical application by causing information tags to be generated from a recording of the first meeting by the bot user that joined the meeting by accessing the APIs of an event server operated by an audio and video conferencing provider for the first event.,” Examiner respectfully asserts that the claim limitations related to generating information tags, as drafted, is part of the abstract limitations for consideration under Step 2A, Prong 1. This abstract limitation cannot integrate the judicial exception into a practical application because it is not part of the additional elements for consideration under Step 2A, Prong 2 or Step 2B. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “In light of the amendments and remarks above, it is respectfully submitted that claims 1, 8, and 15, and the remaining claims, which depend from one of claims 1, 8, and 15, are patentable over the cited references.,” Examiner has deemed Applicant’s argument persuasive. The present claims, as drafted, are rendered neither obvious nor anticipated by the available field of prior art.
Accordingly, the 35 USC 103 rejection has been withdrawn.
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, 5-8, 12-15, and 19-25 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 5-7, 21, and 24-25 are directed to a system, claims 8, 12-14, and 22 are directed to a method, and claims 15, 19-20, and 23 are directed to a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 8, and 15 recite generating recommendations for a user regarding events, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including social activities. Claim 1 recites limitations, similarly recited in claims 8 and 15, of “generate a first recommendation based at least in part on at least one user parameter associated with a user, the first recommendation comprising a first event; provide the first recommendation to the user; receive a user response to the first recommendation; generate one or more informational tags based on analyzing the recording of the first event, wherein generating the one or more informational tags includes analyzing pre-event content including an event invitation associated with the first event and pre-event notes associated with the first event for keywords or key phrases, and generating a first informational tag based on analyzing the pre- event content, and analyzing session content from the recording of the first event, generating an event transcript based on the speech-to-text conversion and extracting a second informational tag from the event transcript; generate a second recommendation, the second recommendation comprising a second event; and provide the second recommendation.” These limitations, as drafted, but for the language of “client device,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “client device,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “client device” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 7, 14, and 20 further narrow the abstract idea identified in the independent claims and do not further introduce additional elements for consideration.
Dependent claims 5-6, 12-13, 19, and 21-25 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 8, and 15 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “at least one computing device comprising a processor and a memory; machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least,” which are configured to perform the steps of the claim. Claim 8 is a computer implemented method, which is recited in the preamble of the claim. Claim 15 recites “a non-transitory, computer-readable medium for recommending events using machine learning, comprising machine-readable instructions that, when executed by a processor of at least computing device, cause the processor to at least,” which is recited in the preamble of the claim. Claims 1, 8, and 15 further recite the additional elements of “provide the first recommendation to a client device associated with the user,” “receive a user response to the first recommendation from the client device,” “generate, by a recording service, a bot user capable of joining event sessions,” “record the first event by the bot user accessing the one or more APIs of the event server,” and “provide the second recommendation to the client device in a format configured for automated integration with a user interface of the client device.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claim 1 further recites the additional elements, similarly recited in claims 8 and 15, of “join, by the bot user, the first event as a participant by accessing one or more Application Programming Interfaces (APIs) of an event server operated by an audio and video conferencing provider for the first event” and “and analyzing session content from the recording of the first event by performing speech-to-text conversion of audio information in the recording of the first event.” These additional elements, as drafted, are nothing more than generally linking to the field of audio transcribing. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Claim 1 further recites the additional elements, similarly recited in claims 8 and 15, of “train a reinforcement learning model based on the user response, the at least one user parameter and the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of independent claims 1, 8, and 15, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 7, 14, and 20 further narrow the abstract idea identified in the independent claims and do not further introduce additional elements for consideration, which is not sufficient to prove integration into a practical application.
Dependent claims 5 and 12 introduce the additional element of “further cause the at least one computing device to at least initialize the reinforcement learning model using at least one randomized recommendation.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 6, 13, and 19 introduce the additional element of “receive an indication of a subscribed informational tag from the client device” and “and provide the third recommendation to the client device.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claims 21-23 introduce the additional element of “wherein the client device includes a client application configured to join at least one of the first event or the second event as a participant by accessing the one or more APIs of the event server, and wherein the first recommendation and the second recommendation is provided by a recommendation service to the client application.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 24 introduce the additional element of “wherein generating the one or more informational tags is performed using a second reinforcement learning model, wherein the second reinforcement learning model performs text extraction and keyword analysis in the pre-event content and the session content.” Dependent claim 25 introduce the additional element of “wherein generating the one or more informational tags using the second reinforcement model includes generating a third informational tag that is a combination of one or more keyphrases from the pre-event content and the one or more keyphrases from the session content.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the base claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 8, and 15 do not comprise anything significantly more than the judicial exception. Claim 1 is a system comprising “at least one computing device comprising a processor and a memory; machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least,” which are configured to perform the steps of the claim. Claim 8 is a computer implemented method, which is recited in the preamble of the claim. Claim 15 recites “a non-transitory, computer-readable medium for recommending events using machine learning, comprising machine-readable instructions that, when executed by a processor of at least computing device, cause the processor to at least,” which is recited in the preamble of the claim. Claims 1, 8, and 15 further recite the additional elements of “provide the first recommendation to a client device associated with the user,” “receive a user response to the first recommendation from the client device,” “generate, by a recording service, a bot user capable of joining event sessions,” “record the first event by the bot user accessing the one or more APIs of the event server,” and “provide the second recommendation to the client device in a format configured for automated integration with a user interface of the client device.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 1 further recites the additional elements, similarly recited in claims 8 and 15, of “join, by the bot user, the first event as a participant by accessing one or more Application Programming Interfaces (APIs) of an event server operated by an audio and video conferencing provider for the first event” and “and analyzing session content from the recording of the first event by performing speech-to-text conversion of audio information in the recording of the first event.” These additional elements, as drafted, are nothing more than generally linking to the field of audio transcribing. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Claim 1 further recites the additional elements, similarly recited in claims 8 and 15, of “train a reinforcement learning model based on the user response, the at least one user parameter and the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of independent claims 1, 8, and 15, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 7, 14, and 20 further narrow the abstract idea identified in the independent claims and do not further introduce additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 5 and 12 introduce the additional element of “further cause the at least one computing device to at least initialize the reinforcement learning model using at least one randomized recommendation.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 6, 13, and 19 introduce the additional element of “receive an indication of a subscribed informational tag from the client device” and “and provide the third recommendation to the client device.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claims 21-23 introduce the additional element of “wherein the client device includes a client application configured to join at least one of the first event or the second event as a participant by accessing the one or more APIs of the event server, and wherein the first recommendation and the second recommendation is provided by a recommendation service to the client application.” Use of a computer or other machinery in its ordinary capacity for 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 24 introduce the additional element of “wherein generating the one or more informational tags is performed using a second reinforcement learning model, wherein the second reinforcement learning model performs text extraction and keyword analysis in the pre-event content and the session content.” Dependent claim 25 introduce the additional element of “wherein generating the one or more informational tags using the second reinforcement model includes generating a third informational tag that is a combination of one or more keyphrases from the pre-event content and the one or more keyphrases from the session content.” These additional elements, as drafted, are nothing more than generally linking to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the base claims, do not comprise anything significantly more than the judicial exception.
Accordingly, claims 1, 5-8, 12-15, and 19-25 are rejected under 35 USC 101.
Allowable Subject Matter
The claims overcome the prior art of record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 USC 101, as set forth above. The closest prior art of the record discloses:
Neckermann et al. (US 20220138697 A1) discloses at least one computing device comprising a processor and a memory; machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least: generate a first recommendation based at least in part on at least one user parameter associated with a user, the first recommendation comprising a first event; provide the first recommendation to a client device associated with the user; receive a user response to the first recommendation from the client device; and analyzing session content from the recording of the first event by performing speech-to-text conversion of audio information in the recording of the first event, train a reinforcement learning model based on the user response, the at least one user parameter; generate a second recommendation based at least in part on the reinforcement learning model, the second recommendation comprising a second event; and provide the second recommendation to the client device in a format configured for automated integration with a user interface of the client device. However, Neckermann fails to explicitly teach or disclose generate, by a recording service, a bot user capable of joining event sessions; join, by the bot user, the first event as a participant by accessing one or more Application Programming Interfaces (APIs) of an event server operated by an audio and video conferencing provider for the first event; record the first event by the bot user accessing the one or more APIs of the event server; generate one or more informational tags based on analyzing the recording of the first event, wherein generating the one or more informational tags includes analyzing pre-event content including an event invitation associated with the first event and pre-event notes associated with the first event for keywords or key phrases, and generating a first informational tag based on analyzing the pre- event content, generating an event transcript based on the speech-to-text conversion and extracting a second informational tag from the event transcript; train a reinforcement learning model based on the user response, the at least one user parameter and the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.
Sahasi et al. (US 20230004832 A1) discloses generate, by a recording service, a bot user capable of joining event sessions; join, by the bot user, the first event as a participant by accessing one or more Application Programming Interfaces (APIs) of an event server operated by an audio and video conferencing provider for the first event; record the first event by the bot user accessing the one or more APIs of the event server; generating an event transcript based on the speech-to-text conversion. However, Sahasi fails to explicitly teach or disclose generate one or more informational tags based on analyzing the recording of the first event, wherein generating the one or more informational tags includes analyzing pre-event content including an event invitation associated with the first event and pre-event notes associated with the first event for keywords or key phrases, and generating a first informational tag based on analyzing the pre- event content, extracting a second informational tag from the event transcript; train a reinforcement learning model based on the user response, the at least one user parameter and the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.
Fahrendorff et al. (US 20210056860 A1) discloses generate one or more informational tags based on analyzing the recording of the first event, wherein generating the one or more informational tags includes analyzing pre-event content including an event invitation associated with the first event and pre-event notes associated with the first event for keywords or key phrases, and generating a first informational tag based on analyzing the pre- event content; extracting a second informational tag from the event transcript. However, Fahrendorff fails to explicitly teach or disclose train a reinforcement learning model based on the user response, the at least one user parameter and the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.
Yin et al. (US 20220101264 A1) discloses train a reinforcement learning model based the one or more informational tags received from analyzing the recording of the first event, such that the reinforcement learning model increases a probability. However, Yin fails to explicitly teach or disclose such that the reinforcement learning model increases a probability of recommending events having tags correlated to accepted recommendations and decreases a probability of recommending events having tags correlated to declined recommendations.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hamlin et al. (US 20230342682 A1) discloses suggesting an optimum time for a collaboration session using reinforcement learning
Deole et al. (US 20220327494 A1) discloses creating tags during the recording of a meeting
Chirakkil et al. (US 20210365279 A1) discloses generating recommendations by a chatbot using reinforcement learning that can present recommendations based on a likelihood of success
Singh et al. (US 20200334641 A1) discloses utilizing a reinforcement learning model to generate an event recommendation
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625