DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgments
Claims 1-20 are pending.
Applicant did not provide information disclosure statement.
Allowable Subject Matter
Claims 3, 10, and 17 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Saito (US20200349825A1) in view of Gorder (US20080255919A1) in further view of Barkol (US20200327996A1) in further view of Myers (US11315065B1) who teaches transcribing utterances and a predictive analysis system with respect to tasks. However, with respect to exemplary claim 3, 10, and 17, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 3, 10, and 17.
Claims 4 and 11 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Saito (US20200349825A1) in view of Gorder (US20080255919A1) in further view of Barkol (US20200327996A1) in further view of Belleville (US10621575B1) who teaches token and channel data as well as the act of caching. However, with respect to exemplary claim 4 and 11, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 4 and 11.
Claims 6, 13, and 19 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Saito (US20200349825A1) in view of Gorder (US20080255919A1) who teaches response messages with respect to tasks in further view of Barkol (US20200327996A1) in further view of Werth (US20090018890A1) in further view of Sella (US20210201244A1) who teaches skills being displayed. However, with respect to exemplary claim 6, 13, and 19, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 6, 13, and 19.
Claims 7, 14, and 20 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Saito (US20200349825A1) in view of Gorder (US20080255919A1) who teaches priority notifications in further view of Barkol (US20200327996A1) in further view of Khabiya (US 20120110492) who teaches closing messages with respect to message priorities . However, with respect to exemplary claim 7, 14, and 20, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 7, 14, and 20.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to non-transitory computer-readable media, system, and method.
Regarding step 2A-1, Claims 1-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 8 and 15 recite the limitations of
accessing one or more utterances and context associated with a user session in which the utterances were generated; identifying…a task to be scheduled for the user based on the one or more utterances and the context; generating, based on the task and the context, a task entry, wherein generating the task entry comprises: creating a description of the task and criteria for initiating the task, generating metadata associated with the task and the context, and storing the description, the criteria, and the metadata as the task entry in a table…generating a notification configuration entry for the task entry, and associating the notification configuration entry with the task entry in the table…the notification configuration entry including notification instructions for controlling how a notification is to be generated and provided upon satisfaction of the criteria for initiating the task; and upon detecting satisfaction of the criteria for initiating the task, generating the notification based on the notification instructions, appending the metadata to the notification to generate a notification message, and sending the notification message…based on the notification instructions.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of accessing, identifying, generating, storing, and sending data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable media, the claim language encompasses simply identifying a task based on user utterance and context, generating a task entry and storing that task entry, generating a notification configuration entry, generating a notification and sending that notification. These steps are mere data manipulation steps that do not require a computer. For example, a user can determine a task based on an utterance and context of that utterance and make a task entry for it. A user can also store that task entry. Another user can also make a notification configuration entry that has notification instructions. A user can create a message and send that message with respect to the notification instructions from the other user.
The claims also recite identifying tasks and sending messages to users. This clearly teaches task management. Applicant’s specification also states interactions between healthcare providers and patients (See para 0002).These make the claims fall in the abstract idea grouping of certain methods of organizing human activity ( fundamental economic principles or practices; business relations, interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of machine learning model, database, system, processing system, client device, and non-transitory computer readable media.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe what the utterance consist of such as a request to schedule a task. The dependent claims further recite what the metadata consists of such as skills needed with respect to a task.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites machine learning models, database, client device.
Claim 8 recites system, processing systems, computer readable media, client device, database, machine learning models.
Claim 15 recites non-transitory computer readable media, processors, client device, and machine learning models.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general-purpose computer in para 0188.
When looking at the additional elements in combination, the Applicant’s specification merely states a general-purpose computer as seen in para 0188. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 8, 9, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US20200349825A1) in view of Gorder (US20080255919A1) in further view of Barkol (US20200327996A1).
Regarding Claim 1, and similarly claims 8 and 15, Saito teaches
A computer-implemented method comprising (See para 0001-The present disclosure relates to an information processing apparatus and an information processing method.) This teaches a method.
A system comprising: one or more processing systems; and one or more computer readable media storing instructions which, when executed by the one or more processing systems, cause the system to perform operations comprising (See para 0183-Next, a hardware configuration example of the information processing server 20 according to the embodiment of the present disclosure will be described. FIG. 16 is a block diagram illustrating a hardware configuration example of the information processing server 20 according to the embodiment of the present disclosure. Referring to FIG. 16, the information processing server 20 includes, for example, a processor 871, a ROM 872, a RAM 873, ) This teaches a system with a processor and memory. (See fig. 16)
One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising (See para 0187-The ROM 872 is a means for storing a program read by the processor 871, data used for calculation, and the like. The RAM 873 temporarily or permanently stores, for example, a program read by the processor 871, various parameters that change as appropriate when the program is executed, and the like.) This teaches a memory. (See fig. 16)
accessing one or more utterances and context associated with a user session in which the utterances were generated (See fig. 2 and 3) (See para 0049-In the case of the example illustrated in FIG. 2, the information processing server 20 executes the sound recognition processing for the utterance UO2 of the user U1 collected by the information processing terminal 10) This shows the information processing server accesses user utterances that are collected from the information processing terminal. (See para 0007-and a context acquired along with the utterance)(See para 0050-Furthermore, at this time, the information processing server 20 according to the present embodiment is characterized in registering a context acquired along with the utterance UO2 in association with the task. ) This also shows the information processing server accesses/acquires a context along with the user utterance.
Identifying…a task to be scheduled for the user based on the one or more utterances and the context (See para 0051-For example, in the case of the example illustrated in FIG. 2, the information processing server 20 registers three contexts C2 a to C2 c in association with the task “buy issues” generated from the sound recognition result for the utterance UO2 of the user U1, and causes the information processing terminal 10 to display task information regarding the task.) (See para 0053-Note that the information processing server 20 can estimate that the sound recognition result “issues” is intended for “tissues” on the basis of the recognition of the tissue box from the image captured by the information processing terminal 10 and can automatically correct the sound recognition result.) Based on the utterance information and the context information accessed from information processing terminal, the information processing server can identify a task for the user to do such as buying tissues. Fig. 2 shows this task is registered (i.e. scheduled).
generating, based on the task and the context, a task entry (See para 0096-The task management unit according to the present embodiment registers a task to be executed by the user in the task DB 260 on the basis of an analysis result by the semantic analysis unit 220. At this time, the task management unit according to the present embodiment is characterized in registering the task in association with the context acquired along with the utterance and reminding the user of the content of the task in the task DB 260.1)(See fig. 10) This shows the system generates a task entry in the task DB with respect to the task and context of the task.
wherein generating the task entry comprises: creating a description of the task and criteria for initiating the task (Fig. 10) This shows the system makes a task entry table that includes a task description such as “buy milk” and criteria for initiating the task such as who should do the task (i.e. target user/person). Context information can also correspond to criteria for initiating the task such as the execution place to do the task or task urgency (i.e. do this task first compared to another task).
generating metadata associated with the task and the context The system also generates metadata that corresponds to the tasks and the task context such as generating task status as seen here (See para 0164-the task management unit 250 may detects execution of the task by the user U1 and change a status of the task to completion.)
and storing the description, the criteria…as the task entry in a table of a database (See fig. 10) (See para 0145-FIG. 10 is examples of the task information registered in the task DB 260 according to the present embodiment. Referring to FIG. 10, it can be seen that the task information according to the present embodiment includes the target user of the task and various contexts in addition to the content of the task.) This shows the task description and criteria is stored in the table as task entry in a database.
However it is not clear that the meta data is also stored in the database as the task entry, however another section of Saito teaches storing…metadata (See fig. 13) This shows that metadata such as the status of the task is stored on the user’s device to display to the user the metadata.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the metadata also being stored as a task entry along with the task description and criteria in fig. 10 because this would allow the user to see more information about the task in one table located in the task DB. This would allow better organization of task information and give the user the ability to see all the task statuses at once rather than receiving task statuses one by one as seen in fig. 13. This would make the art of Saito more sophisticated.
Even though Saito teaches notifications, it is unclear that it teaches a notification configuration entry, however Gorder teaches
generating a notification configuration entry for the task entry, and associating the notification configuration entry with the task entry (See para 0023-FIG. 2 depicts a notification interface 108 of a system for schedule notification according to one embodiment. The notification interface 108 may include various fields in which employees may define preferences. ) This teaches the system generates a notification preference entry for the user. (See para 0028-There may also be a field to designate the timing of each notification type. Examples of timing preferences may include, but are not limited to, immediately after a schedule is available, when a schedule period begins, a period of time in advance of a scheduled job or task (e.g. 15 minutes, a day, an hour, or other period)) This teaches the notification preferences are with respect to tasks (i.e. task entries). The system associates the notification entry with a task entry by timing when a notification is sent with respect to a task/job beginning.
In addition, Gorder teaches the notification configuration entry including notification instructions for controlling how a notification is to be generated and provided (See para 0029-An employee may enter the desired notification mode in the appropriate field for the desired type of notification, and then set a timing preference. For example, as depicted in FIG. 2, a weekly notification will be sent via the primary mode of SMS text, and also via a secondary mode of email. As depicted in FIG. 2, the weekly notifications will be sent on Sundays at 7:00 pm. ) This shows how the notification is to be generated such as a SMS text and when it is provided.
Saito and Gorder are analogous art because they are from the same problem-solving area of task management and completing tasks and both arts belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Saito’s invention by incorporating the method of Gorder because the user can also specify how they want to be alerted with respect to tasks on their device. This would give the user more control on seeing task notifications and ensure they don’t miss notifications since they would be tailored to their preference. This would make the art of Saito more sophisticated since it adds another layer of control.
Saito further teaches task entry in the table of the database (See figure 10) This shows task entries in a table of the task DB.
notification is to be generated and provided upon satisfaction of the criteria for initiating the task Examiner interprets the satisfaction of the criteria for initiating the task to correspond to the target user actually starting and finishing the task with respect to the execution location. A notification is generated based on this event as seen in fig. 13. (See para 0165-In this case, the output control unit 270 according to the present embodiment can output, for example, a sound SO13 indicating completion of the task by the user U1 to an information processing terminal 10 b possessed by the user U2 that is one of the target users, as illustrated on the right side in FIG. 13.)
and upon detecting satisfaction of the criteria for initiating the task, generating the notification (See fig. 13) Examiner interprets the satisfaction of the criteria for initiating the task to correspond to the target user actually starting and finishing the task with respect to the execution location. A notification is generated based on this event detected as seen in fig. 13. (See para 0165-In this case, the output control unit 270 according to the present embodiment can output, for example, a sound SO13 indicating completion of the task by the user U1 to an information processing terminal 10 b possessed by the user U2 that is one of the target users, as illustrated on the right side in FIG. 13.)
appending the metadata to the notification to generate a notification message, and sending the notification message to a client device (See fig. 13) The metadata which is the status information is added to the notification and part of the notification message that is sent to the client device. (See para 0165-In this case, the output control unit 270 according to the present embodiment can output, for example, a sound SO13 indicating completion of the task by the user U1 to an information processing terminal 10 b possessed by the user U2 that is one of the target users, as illustrated on the right side in FIG. 13.)
Even though Saito teaches generating and sending notification, it’s not clear that it does this with respect to notification settings, however Gorder already teaches based on the notification instructions. (See para 0029-An employee may enter the desired notification mode in the appropriate field for the desired type of notification, and then set a timing preference. For example, as depicted in FIG. 2, a weekly notification will be sent via the primary mode of SMS text, and also via a secondary mode of email. As depicted in FIG. 2, the weekly notifications will be sent on Sundays at 7:00 pm. ) This shows how the notification is to be generated such as a SMS text and when it is provided. These are the notification instructions.
Saito and Gorder are analogous art because they are from the same problem-solving area of task management and completing tasks and both arts belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Saito’s invention by incorporating the method of Gorder because the user can also specify how they want to be alerted with respect to tasks on their device. This would give the user more control on seeing task notifications and ensure they don’t miss notifications since they would be tailored to their preference. This would make the art of Saito more sophisticated since it adds another layer of control.
In addition, even though Saito teaches identifying tasks, it doesn’t do it by machine learning models, however Barkol teaches
machine learning models (See para 0020- The following description relates to various embodiments of a collaborative healthcare system that facilitates communication among care providers of a patient (which may be collectively referred to as a care provider team) and also utilizes machine and/or other deep learning models) This teaches using machine learning models.
Saito and Barkol are analogous art because they are from the same problem-solving area of determining intent with respect to receiving voice data from users. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Saito’s invention by incorporating the method of Barkol because Saito would also be able to use machine learning models when determining tasks from users’ utterances. This would make the art of Saito more sophisticated since machine learning overtime would get more accurate and the system of Saito would accurately determine what task the user is saying. The machine learning models would also be able to handle complex utterances said by the user such as multiple tasks at once.
Regarding Claim 2, and similarly claims 9 and 16, Saito, Gorder and Barkol teach the limitations of claims 1, 8, and 15, however Saito further teaches
wherein: an utterance of the one or more utterances includes a natural language request to schedule a task (See fig. 7) This shows a request to schedule task of sending formal clothes to the cleaners.
However Saito doesn’t teach this is analyzed by machine learning models, however Barkol teaches and the one or more machine-learning models analyze the natural language request using one or more natural language processing techniques (See para 0049- The VHAs may be configured to receive messages from human care providers and utilize natural language processing to determine what information is being conveyed in the messages. For example, the VHAs may utilize natural language processing to determine if a message received on the communication channel includes a request for patient medical information… The VHAs may execute deep learning models (e.g., machine learning or other deep learning models such as neural networking) that are trained to understand medical terminology) (See para 0051- Thus, the VHAs described herein may include artificial intelligence and be adapted to handle natural language which is a way to take human input and map it to intent and entities). (See para 0069- A user may select the link via a suitable input, such as via a mouse click, touch input, or voice command.) This shows machine learning models and language processing techniques are used with respect to utterance data such as voice command.
Saito and Barkol are analogous art because they are from the same problem-solving area of determining intent with respect to receiving voice data from users. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Saito’s invention by incorporating the method of Barkol because Saito would also be able to use machine learning models when determining tasks from users’ utterances. This would make the art of Saito more sophisticated since machine learning overtime would get more accurate and the system of Saito would accurately determine what task the user is saying. The machine learning models would also be able to handle complex utterances said by the user such as multiple tasks at once.
In addition, Saito further teaches and resultantly extract therefrom the description of the task and the criteria for initiating the task. Based on the analysis of the information processing server, the description and criteria for initiating the task is extracted and seen in fig. 10 of the art.
Claim(s) 5, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US20200349825A1) in view of Gorder (US20080255919A1) in further view of Barkol (US20200327996A1) in further view of Werth (US20090018890A1).
Regarding Claim 5, and similarly claims 12 and 18, Saito, Gorder and Barkol teach the limitations of claims 1, 8, and 15, Saito teaches metadata, but it does not teach
wherein the metadata further comprises an identification of a selected skill of a plurality of skills for processing steps of the task.
However Werth teaches wherein the metadata further comprises an identification of a selected skill of a plurality of skills for processing steps of the task. (See para 0010- The centralized service assigns a first task of the plurality of tasks to a first remote technician having a second type of skill from the plurality of skills) (See para 0012- In one embodiment, the centralized service automatically assigns a first task to the first remote technician. In another embodiment, the centralized service assigns the first task based on one or more attributes of the first task corresponding to one or more skill types of the first remote technician.) This shows the systems determines a skill type with respect to a plurality of skill types to process a task to be complete.
Saito and Werth are analogous art because they are from the same problem-solving area of determining tasks and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Saito’s invention by incorporating the method of Werth because Saito would also analyze a user utterance and determine what skills are needed to complete the task. This would make the system of Saito more sophisticated since it looks at other variables of the task such as what skills are required.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Myers (US11315065B1) who teaches transcribing utterances and a predictive analysis system with respect to tasks.
Belleville (US10621575B1) who teaches token and channel data as well as the act of caching.
Sella (US20210201244A1) who teaches skills being displayed.
Khabiya (US 20120110492) who teaches closing messages with respect to message priorities.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MUSTAFA IQBAL/ Primary Examiner, Art Unit 3625