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 .
Claim Objections
Claim 6 is objected to because of the following informalities:
Element number 100 should be removed.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1 – Statutory Category
The claim is directed to a method (a statutory category under § 101).
Step 2A, Prong One – Identify Judicial Exception
The claim’s core operations:
Monitoring Interactions.
Determining a task and associated objects from observed interactions.
Generating semantic description of that information.
At a high level, the claim still describes information processing and organization of human/machine interactions.
This type of activity falls squarely into recognized abstract ideas:
Certain methods of organizing human activity (managing interactions).
Mental processes (generating semantic description).
At a high level, a person could hear a question, determine what object the speaker means, determine the associated goal, consult stored information and formulate an answer. The electronic implementation, additional elements, does not make the claims statutory.
Thus, Step 2A, Prong One: Yes, the claim recites an abstract idea.
Step 2A, Prong Two – Integration into a Practical Application
The present claim language under its broadest reasonable interpretation, covers performance of mental processing and recites generic computer components, which all falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements which are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B – Inventive Concept
The claims do not include the additional elements (beyond the abstract idea) that amount to an inventive concept/significantly more than well-understood, routine, and conventional computer functions. These elements appear to be conceptual design choices implemented on generic computing devices. The claim does not recite a novel hardware architecture, a new AI model training technique, or a unique algorithm that improves the underlying technology.
Thus, under Step 2B, the claim lacks an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
The dependent claims recite receiving, generating, determining, identifying, filtering, storing, correlating and retrieving data, which is all mental processing and organizing human activity, which is non-statutory.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al. (PGPUB 2019/0347518), hereinafter referenced as Shrestha in view of Miller et al. (PGPUB 2018/0268337), hereinafter referenced as Miller and in further view of Bruemmer et al. (PGPUB 2018/0038694), hereinafter referenced as Bruemmer.
Regarding claims 1 and 12, Shrestha discloses a method and device, hereinafter referenced as a method for providing Ultra-Wide Band (UWB) based voice assistance to a user by an electronic device, comprising:
memory storing one or more computer programs (p. 0118);
one or more processors communicatively coupled to the memory (p. 0118);
an intelligent response generator, coupled to the memory and the one or more processors, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors individually or collectively (p. 0121), causing:
monitoring over time, by the electronic device, interactions between objects in an environment using at least one UWB sensor of the electronic device (performs object detection, activity recognition, identification and motion analysis, derives relationship vectors and generates semantic information; p. 0040-0050, 0063-0080);
determining, by the electronic device, corresponding to the monitored interactions (performs object detection, activity recognition, identification and motion analysis, derives relationship vectors and generates semantic information; p. 0040-0050, 0063-0080);
generating, by the electronic device, a semantic description of the at least one task and the associated objective in a natural language for each object (relationship vectors and feature outputs are supplied to scene story generator to generate semantic descriptions; p. 0024, 0035, 0057, 0071-0080); and
providing, by the electronic device, the voice assistance to the user based on the semantic description in the natural language (natural language wherein the user interface present data; p. 0035, 0097), but does not specifically teach at least one task and an associated objective of the at least one task and at least one UWB sensor
Miller discloses a method comprising:
determining at least one task and associated objective corresponding to monitored activity (In some implementations, task determiner determines task lists for objectives based on extracting patterns of events from user activity data. The patterns may be extracted from user activity data using any suitable machine learning approach, including unsupervised or supervised learning approaches, to learn task lists for objectives. In doing so, factors and features employed in the pattern extraction and analysis can include any combination user context (e.g., personal features) and event context. For example, patterns could be extracted from the user activity data based on any combination of these various factors. In addition or instead, any combination of the factors may be used to filter user activity data (e.g., events), and the filtered user activity data may be analyzed for patterns. As a further option, any combination of the factors may be used to determine subpatterns from patterns and/or further filter the patterns or information derived therefrom. Task lists may be extracted from any of the various patterns of user activity data; p. 0121-0140), to enhance the system.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to allow semantic descriptions to convey objectives related to the activity.
Bruemmer discloses comprising:
at least one UWB sensor (p. 0023), for monitoring locations and movements of objects.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to assist with tracking objects during the activities.
Regarding claims 2 and 13, it is interpreted and rejected for similar reasons as set forth above. In addition, Shrestha teaches retrieving the information in response to a voice query (microphone; p. 0036).
Regarding claims 3 and 16, it is interpreted and rejected for similar reasons as set forth above. In addition, Shrestha identifying, by the electronic device, the objects and the interactions between the objects based on the at least one parameter of the objects (performs object detection, activity recognition, identification and motion analysis, derives relationship vectors and generates semantic information; p. 0040-0050, 0063-0080). Furthermore, Bruemmer discloses a method wherein the monitoring over time, by the electronic device, of the interactions between the objects using the at least one UWB sensor, comprises:
receiving, by the electronic device, UWB signals reflected from the objects (p. 0042-0043, 0006);
determining, by the electronic device, at least one parameter of the objects comprising a form, a shape (p. 0073), a location, a movement, and an association based on the received UWB signals (p. 0023, 0039); and
identifying, by the electronic device, the objects and the interactions between the objects based on the at least one parameter of the objects.
Regarding claims 4 and 17, it is interpreted and rejected for similar reasons as set forth above. In addition, Miller discloses a method wherein determining, by the electronic device, the at least one task and the associated objective of the at least one task corresponding to the monitored interactions, comprises: filtering, by the electronic device, the interactions correlated to past interaction of the user; and deriving, by the electronic device, the at least one task and the associated objective of the at least one task corresponding to the filtered interactions (In addition or instead, any combination of the factors may be used to filter user activity data (e.g., events), and the filtered user activity data may be analyzed for patterns. As a further option, any combination of the factors may be used to determine subpatterns from patterns and/or further filter the patterns or information derived therefrom. Task lists may be extracted from any of the various patterns of user activity data.; p. 0123-0134, 0014-0015).
Regarding claims 5 and 18, it is interpreted and rejected for similar reasons as set forth above. In addition, Miller discloses a method wherein the method, comprises: storing, by the electronic device, at least one of past occurrences of the at least one task and the associated objective in a different environment (In some embodiments, information determined by user activity monitor 280 may be provided to inference engine 230 including information regarding events, contextual features of those events, and historical features (e.g., based on historical observations, which may be provided from records in user profile 250), such as personal features of users; o. 0051-0052).
Regarding claims 6 and 19, it is interpreted and rejected for similar reasons as set forth above. In addition, Shrestha discloses determining, by the electronic device, the objects located in proximity to the user (p. 0072).
Regarding claims 7 and 20, Shrestha discloses a method wherein the objects include at least one of users, devices, pets, plants, or utensils, whereas the environment includes at least one of home, office or any similar enclosed building (animal; p. 0085).
Regarding claim 14, it is interpreted and rejected for similar reasons as set forth above. In addition, Bruemmer discloses a method wherein the intelligent response generator includes an entity resolver configured to perform UWB based object detection using the UWB sensor (p. 0042-0043, 0006).
Regarding claim 15, it is interpreted and rejected for similar reasons as set forth above. In addition, Miller discloses a method wherein the entity resolver is configured to give a score to detected objects based on the query (scores; 0091-0099).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (PGPBU 2019/0042988), hereinafter referenced as Brown in view of Miller et al. (PGPUB 2018/0268337), hereinafter referenced as Miller.
Regarding claim 8, Brown discloses a method for voice assistance to a user by an electronic device determining proximity of a user (p.0208-0210), comprising:
receiving, by the electronic device, a voice query from the user (voice and language services; p. 0145-0169);
identifying, by the electronic device, at least one object in the received voice query (intent and an entity of a question; p. 0285-286);
deriving, by the electronic device, at least one task and associated objective corresponding to the at least one identified object from the voice query (intent and an entity of a question; p. 0178, 0232-0235, 0272-0290);
retrieving, by the electronic device, a sematic description by referring to a semantic task (world data model of activities; p. 0050-0052, 0068, 0086-0102); and
generating, by the electronic device, a response to the voice query using the retrieved semantic description (semantic; p. 0083, 0097-0104, 0280-0283), but does not specifically teach tasks of an objective from user activity/events.
Miller discloses determining at least one task and associated objective corresponding to monitored activity (In some implementations, task determiner determines task lists for objectives based on extracting patterns of events from user activity data. The patterns may be extracted from user activity data using any suitable machine learning approach, including unsupervised or supervised learning approaches, to learn task lists for objectives. In doing so, factors and features employed in the pattern extraction and analysis can include any combination user context (e.g., personal features) and event context. For example, patterns could be extracted from the user activity data based on any combination of these various factors. In addition or instead, any combination of the factors may be used to filter user activity data (e.g., events), and the filtered user activity data may be analyzed for patterns. As a further option, any combination of the factors may be used to determine subpatterns from patterns and/or further filter the patterns or information derived therefrom. Task lists may be extracted from any of the various patterns of user activity data; p. 0121-0140), to enhance the system.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to allow semantic descriptions to convey objectives related to the activity.
Claim(s) 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown in view of Miller and in further view of Bruemmer.
Regarding claim 9, Brown in view of Miller discloses the method as described above, but does not specifically teach using at least on UWB sensor.
Bruemmer discloses comprising:
at least one UWB sensor (p. 0023), for monitoring locations and movements of objects.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to assist with tracking objects during the activities.
Regarding claim 10, it is interpreted and rejected for similar reasons as set forth above. In addition, Miller discloses identifying, by the electronic device, the objects and the interactions between the objects based on the at least one parameter of the objects (In some implementations, task determiner determines task lists for objectives based on extracting patterns of events from user activity data. The patterns may be extracted from user activity data using any suitable machine learning approach, including unsupervised or supervised learning approaches, to learn task lists for objectives. In doing so, factors and features employed in the pattern extraction and analysis can include any combination user context (e.g., personal features) and event context. For example, patterns could be extracted from the user activity data based on any combination of these various factors. In addition or instead, any combination of the factors may be used to filter user activity data (e.g., events), and the filtered user activity data may be analyzed for patterns. As a further option, any combination of the factors may be used to determine subpatterns from patterns and/or further filter the patterns or information derived therefrom. Task lists may be extracted from any of the various patterns of user activity data; p. 0121-0140). Furthermore, Bruemmer discloses a method wherein the monitoring over time, by the electronic device, of the interactions between the objects using the at least one UWB sensor, comprises:
receiving, by the electronic device, UWB signals reflected from the objects (p. 0042-0043, 0006);
determining, by the electronic device, at least one parameter of the objects comprising a form, a shape (p. 0073), a location, a movement, and an association based on the received UWB signals (p. 0023, 0039); and
identifying, by the electronic device, the objects and the interactions between the objects based on the at least one parameter of the objects.
Regarding claim 11, it is interpreted and rejected for similar reasons as set forth above. In addition, Miller discloses a method wherein determining, by the electronic device, the at least one task and the associated objective of the at least one task corresponding to the monitored interactions, comprises: filtering, by the electronic device, the interactions correlated to past interaction of the user; and deriving, by the electronic device, the at least one task and the associated objective of the at least one task corresponding to the filtered interactions (In addition or instead, any combination of the factors may be used to filter user activity data (e.g., events), and the filtered user activity data may be analyzed for patterns. As a further option, any combination of the factors may be used to determine subpatterns from patterns and/or further filter the patterns or information derived therefrom. Task lists may be extracted from any of the various patterns of user activity data.; p. 0123-0134, 0014-0015).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This information has been detailed in the PTO 892 attached (Notice of References Cited).
Masuoka et al. discloses task computing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAKIEDA R JACKSON whose telephone number is (571)272-7619. The examiner can normally be reached Mon - Fri 6:30a-2:30p.
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/JAKIEDA R JACKSON/ Primary Examiner, Art Unit 2657
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