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 .
Claims 1-20 are pending, of which claim 1 is an independent claim.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for Application No. 202210532411.X filed on May 11, 2022, Application No. 202210565079.7 filed May 23, 2022, and Application No. 202310511468.6 filed on May 8, 2023. All applications filed in China.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on November 21, 2024, and November 11, 2025, have been considered by the examiner.
Specification Objections
The title of the invention is not fully descriptive. A new title is required that includes the invention to which the claims are directed.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 1, line 9, insert “and” after “item;”.
Claim 2, line 5, insert “at least one” before “device configuration item”.
Claim 2, line 5, insert “and” after “operation;”.
Claim 4, line 1, insert “the” before “execution”.
Claim 4, line 10, insert “and” after “operation;”.
Claim 5, line 8, insert “and” after “operation;”.
Claim 5, line 10, replace “the total” with “a total”.
Claim 6, line 3, insert “and” after “period;”.
Claim 7, line 6, insert “and” after “currently;”.
Claim 8, line 13, insert “and” after “operation;”.
Claim 9, line 1, replace “determining” with “the determining of”.
Claim 9, line 5, replace “have performed” with “has performed”.
Claim 9, line 7, insert “and” after “scene;”.
Claim 9, line 8, replace “yes” with “the at least one device that has performed the new action after the execution of the configured target scene”.
Claim 9, line 9, replace “at least one device” with “the device”.
Claim 10, line 8, insert “and” after “consecutively;”.
Claim 11, line 3, replace “the scene” with “a scene”.
Claim 12, line 12, insert “and” after “period;”.
Claim 13, line 9, insert “and” after “state;”.
Claim 14, line 5, insert “and” after “result;”.
Claim 15, line 5, insert “a” after “based on”.
Claim 15, line 8, insert “and” after “triggered;”.
Claim 16, line 5, insert “and” after “states;”.
Claim 17, line 1, insert “further comprising:” before “after”.
Claim 17, line 5, insert “and” after “same;”.
Claim 17, line 6, replace “not” with “the third device parameters under the various environmental states are not the same”.
Claim 18, line 14, insert “and” after “period;”.
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter, which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
The last function of claim 1 recites “responding to a configuration operation for the selected target device configuration item, performing configuration processing on a corresponding device based on a target device configuration item, to apply the target device configuration item”. However, it is unclear whether “the selected target device configuration item” and “a target device configuration item” should be the same or should be different target device configuration items. If the target device configuration item has been selected, why would configuration processing be performed for another target device configuration item, that is, “a target device configuration item”?
For the claim to be clear, the function of “performing configuration processing on a corresponding device” should be “based on the selected target device configuration item”. Also, it is unclear the intended meaning of “to apply the target configuration item”. It is unclear whether “the target configuration item” is “the selected target device configuration item” or “a target device configuration item”. The recitations of the claim are confusing, and it is respectfully recommended that the claim be amended to clarify them. For purposes of examination, the last function will be construed as follows: “responding to a configuration operation for the selected target device configuration item, performing configuration processing on a corresponding device based on [[a]] the selected target device configuration item
Claim 2 also recites “wherein the responding to the configuration operation for the selected target device configuration item, performing configuration processing on the corresponding device based on the target device configuration item”. The claim is rejected for similar reasons to those presented with respect to independent claim 1. The Office respectfully recommends that claim 2 be amended as suggested in independent claim 1.
Regarding claim 7, this claim recites “before pushing and displaying the device configuration recommendation information when obtaining the configuration recommendation information for the device under the target space, the method further comprises: after completing a voice interaction, detecting whether there are any unread device configuration items currently; when there are an unread device configuration item, outputting voice prompt information, wherein the voice prompt information is configured to remind a user to view the unread device configuration item”. However, the intended scope of the claim is unclear and confusing. The Specification does not seem to offer clear support of what is intended to be “an unread device configuration item”. Perhaps it is referring to the description provided in Paragraphs [0084]-[0086] of the published Specification, but the Office is not certain that such is the case. In the alternative, it could refer to the description provided in FIG. 8 as described in the Specification. The Office is not clear as to the intended scope of claim 7. Also, “an unread device configuration item” is not clear. Is it one of “any unread device configuration items”? If such is the case, the claim should be clarified. Regardless, the entirety of the claim needs to be rewritten to provide a definite scope intended to be claimed. The Office is unable to conduct a proper prior art search of claim 7. Appropriate substantive claim amendments to claim 7 are respectfully requested.
Regarding claim 8, this claim recites in part “before when obtaining the device configuration recommendation information for the device under the target space, pushing and displaying the device configuration recommendation information”. If the device configuration recommendation information has not been obtained; that is, before obtaining such information, how is it possible to push and display such device configuration recommendation information? The recitation is unclear and indefinite. The Office is unable to conduct a proper prior art search of such recitation. For purposes of examination, the recitation will be omitted from prior art consideration. Appropriate claim amendment is respectfully requested.
Regarding claim 10, this claim recites in part “when based on the operating attribute of the device recorded in the historical device operation data, determining that frequency of the device performing the new action within a first preset duration exceeds a preset frequency threshold, then determining that there is one device performed the new action; the first preset duration refers to time period during which the configured target scene is executed multiple times consecutively”. The highlighted limitation is confusing because “the device is performing the new action” but the highlighted limitation appears to be referring to a different “one device”. If the device is already performing the new action, why is there a determination that there is one device performed the new action? The recitation is confusing.
In addition, claim 10 further recites “determining that the number of devices performing the new action after the execution of the configured target scene exceeds a quantity threshold, then determining that multiple devices have performed the new action”. Now the claim refers to “the number of devices” which is different from the device and the one device. The intended meaning and scope of this limitation is unclear and indefinite. The Office is unable to conduct a proper prior art search of claim 10. Appropriate substantive claim amendments to claim 10 are respectfully requested.
In view of their dependencies to a rejected base claim, claims 2-20 are also found to be indefinite.
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 therefore, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because computer readable storage medium is not a patentable subject matter. In the claim, the “computer-readable storage medium” is not described as non-transitory in the specification, in which case the computer readable storage medium may be transitory. Such a recitation does not exclude the computer readable storage medium from being a signal per se. Thus, the broadest, reasonable interpretation of the “computer readable storage medium” in view of the specification encompasses non-statutory subject matter that is unpatentable under 35 USC 101.
The Examiner suggests amending claim 20 to recite “…the computer program product comprising a non-transitory computer-readable storage medium…”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6, 8, 9, 11, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Knox et al. (US Patent Application No. 2020/0364588 A1) (“Knox”).
Regarding independent claim 1, Knox teaches:
A device processing method, executed by a computer device, wherein the method comprises: Knox: FIG. 1A and Abstract (“Methods include machine learning and deep learning applications that analyze data to generate preference based recommendations to assist, inform, and guide subjects interacting with a connected living space and their connected social network.”)
when obtaining device configuration recommendation information for a device under a target space, pushing and displaying the device configuration recommendation information; wherein the device configuration recommendation information is obtained by analyzing a historical device operation data; Knox: Paragraph [0009] (“The ubicomp devices may include a smart meter, a smart thermostat, … an IoT device, a smartphone, a smart watch, a smart window shade, a smart garage door, a smart outlet, a smart switch, a smart tablet, smart audio system, a smart TV, a smart mirror, a smart monitor screen, a smart camera, a smart pet feeder, a smart dishwasher, a smart washer/dryer, a smart microwave, a smart oven, a smart toaster, a smart coffee maker, a smart dishwasher, a smart refrigerator, a smart water faucet, a smart vacuum, a smart toy, a smart tracker, a smart door locks, a smart irrigation system, a smart energy monitoring, and/or a smart voice activated service.” Which read on “device”.) Knox: Paragraph [0072] (“…human activity and behavioral data collected from engagement data 80 extracted from ubicomp 12, and related components including API 24 and computing device 26, may be identified, indexed, labeled, formatted and stored according to processing and analytics requirements and specifications of respective recommendation engine 18 and inference intelligence 20 applications. Where recommendation engine(s) 18 operate to identify and name activities, habits, routines and behaviors derived from processing and analyzing engagement data 80 associated with a subject in ubicomp 12, inference intelligence 20 applications operate to correlate activity, habit, routine, and behavior data identified by recommendation engine 18 with measurable data indicating user intent, preference, context, desired outcome, unique conditions and etc., such as facial expressions, emotions, moods, biometrics, event frequency, and natural language data generated by system 10 usage.”) Knox: Paragraph [0078] (“For reference, recommendation engine 100 is disclosed herein for the purposes of presenting interaction recommendations based on identifiable engagement data 110 input from systems and methods that capture and present relevant data, including but not limited to, inputs such as human modalities, human actions and activities, physical environments, behavior, sentiments, lifestyle preferences, biometrics, user devices, networked communication devices, networked appliances, media content, external systems, platform systems, unique user profiles, and the like… recommendation engine 100 generates two key datasets. First, from collected engagement data 110, recommendation engine 100 implements computerized process and analysis steps to determine a dataset of information or interaction data 130 that represents a known or desired human activity or behavior... Second, from collected engagement data 110, recommendation engine 100 implements computerized process and analysis steps to determine a dataset of information or inference intelligence 132 that correlates preference or contextual information to interaction datasets 130 assigned to basic user activity within ubicomp 12, user interactions with specific ubicomp 12 components, interactions between users, and interactions between users and external services.”) Knox: Paragraph [0092] (“… system or method 320 may be performed by recommendation engine 100 coupled to ubicomp 12. At block 322, processing logic identifies engagement data 110 events and interactions 130 associated with a subject. In some aspects, a subject and/or associated user may interact with ubicomp 12 and one another from within ubicomp 12 ... At block 324, processing logic applies engagement data 110 events and interactions 130 to train a neural network model to identify inference intelligence 132 information... In some aspects, the inference intelligence may be calculated using individual datasets or a combination therein including but not limited to environmental data, experiential data, user profile data, user preference data, historical data, and generic training data. In some aspects, the relevancy of the identified engagement data 110 is a calculated value, used as weights and biases, based on individual or multiple datasets including real-time data, historical data of user profile and previous interactions, training data, and probability values related to user preference, intentions, desired outcomes, and the like.”) Knox: Paragraph [0095] (“At block 382, processing logic generates a neural network model using computer application 78 to correlate engagement data 110 associated with interaction data 130 to identify user intention information and desired outcome information. At block 384, using the neural network model, the processing logic calculates and assigns weights and bias values to engagement data associated with user intention information 344 and desired outcome information 346... At block 392, using the neural network model, the processing logic determines an interaction recommendation hierarchy based higher correlation scores between user intent information and desired outcome information. At block 394, using the neural network model the processing logic generate triggers using assigned weight and bias values to identify comparable engagement data 110, including weights and biases that indicate conditions, prerequisites, and precursors for interaction outcomes. At block 396, the processing logic presents and displays recommendations to users and, where applicable, include conditions, prerequisites, and precursors for interaction outcomes.”) [The recommendation engine implementing computerized process and analysis steps to determine a dataset of information or interaction data that represents a known or desired human activity or behavior to generate recommendations displayed to users based on historical data reads on “when obtaining device configuration recommendation information for a device under a target space, pushing and displaying the device configuration recommendation information; wherein the device configuration recommendation information is obtained by analyzing a historical device operation data”.]
responding to a selection operation for at least one device configuration item in the device configuration recommendation information, displaying device control information corresponding to a selected target device configuration item; Knox: Paragraph [0095] [As described above.] Knox: Paragraph [0085] (“An interaction recommendation selection can be defined as a single or collection of user choices of actions and decisions, based on analyzed interaction data 130 and inference intelligence 132, that may be presented as ranked and/or rated lists based on favorable outcome, user intentions, circumstantial actions based on a data instance, scheduled or time based decisions, and responses to direct communications between user devices such as an electronic request for information on a smart device... For example, if a subject is presented a specific media content type with varying engagement measurements for each respective media exposure event, recommendation module computer application 128 may use training 114 and engagement data 110 to provide a more accurate understanding of the variance in engagement measurements (defined by rules, weights and biases) that enable more concise , and/or , who should present the media, when to present the media, what environmental conditions are ideal, what technical means is optimal, how long the media presentation should last, how often should the media be presented, what alternative media choices are comparable, and etc. More specific, if a subject enjoys viewing images of yellow roses on a tablet device at noon while eating lunch alone on weekdays (and not weekends), the recommendation module will identify interaction data 130 including all measurable user data, environmental conditions, preferences, and etc. by class such as, in the present example (frequency: once a day; subject: yellow roses; method: tablet device; activity: lunch; behavior: alone; schedule; only weekdays at noon) to create recommendations for interactions that are presented to the subject and/or the subject's associated users to guide their decisions and actions. Electronically, with a push notification of the aforementioned interaction description, either by designated schedule settings or an impromptu instance when the components of the optimal conditions are detected by recommendation engine 100... Interaction recommendations can be created for the subject where, based on scheduled or impromptu detection of optimal conditions, recommendation module computer application 128 can deliver interaction recommendations via API 24 to a subject's computing device 26 or ubicomp user interface 70 within ubicomp 12 and associated user devices 26 located outside ubicomp 12. For example, based on empirical and training data, system 10 can identify voice commands, physical gestures and actions, behaviors, habits and patterns that anticipate a subject's lifestyle preferences with recommendations such as suggestions of favorite television program viewing times; availability of favorite user associates via networked devices 26; schedule for house cleaning, building maintenance, personal caregiving, interactions with an object or networked appliance; transportation or meals from external services 28; advanced lead time for personal grooming routines; social visitation schedule, meal delivery lead time, transport schedule and etc. In some aspects, recommendation module computer application 128 may use a data identifier that recognizes patterns, trends, routines and habits associated with interaction datasets 130 to be used by recommendation engine 100 as benchmarks, thresholds, precursor, triggers and the like that predict or anticipate user activity or behaviors that precede or follow an interaction with ubicomp 12, system 10 components, API 24, or users operating computing device 26.”) Knox: Paragraph [0094] (“At block 368, the processing logic coupled to API 24 selects at least one recommendation from data files associated with user profile data 114 and serves the recommendations to user interface 70 within ubicomp 12 or a user interface operating on computing device 26 using a distribution method and confirmation means. In some aspects, a distribution method and confirmation means can include an audible tone, voice or sound, visual image or graphic, alphanumeric value, video clip and the like. In some aspects, the rendered recommendations may be conditional and require a response that is time sensitive, the number of users that select the same recommendation, based which audience members respond, and the like.”) Knox: Paragraph [0117] (“In some aspects, users 516, 518 may use the API 512 via robotic surrogate program 538 to obtain physical support and assistance lists for user 516 such as walking assistance, retrieving food, washing clothes, cleaning household items, dusting fixtures and furniture, security and surveillance, managing Internet of Things (IoT) appliances and devices, online VoIP interfaces, and the like. In the present example, recommendation engine 508 may use computer application 78…”) [The recommendation selected as a response for physical support or assistance including walking assistance, retrieving food, washing clothes, cleaning household items, dusting fixtures and furniture, security and surveillance, managing Internet of Things (IoT) appliances and devices, online VoIP interfaces to play, or the execution of the recommendation selected as a response from the user or subject of a network appliance, meal delivery, transport, and/or media genre or artist, reads on “responding to a selection operation for at least one device configuration item in the device configuration recommendation information, displaying device control information corresponding to a selected target device configuration item”.]
responding to a configuration operation for the selected target device configuration item, performing configuration processing on a corresponding device based on a target device configuration item, to apply the target device configuration item. Knox: Paragraphs [0085], [0094], [0095], and [0117] [As described above.]
Regarding claim 2, Knox teaches all the claimed features of claim 1, from which claim 2 depends. Knox further teaches:
The method according to claim 1, wherein the responding to the configuration operation for the selected target device configuration item, performing configuration processing on the corresponding device based on the target device configuration item, comprises: Knox: Paragraphs [0085], [0094], [0095], and [0117] [As described in claim 1.]
receiving an update operation for the selected target device configuration item, and updating the device configuration item according to the update operation; responding to a confirmation operation for an updated selected target device configuration item, performing configuration processing on the corresponding device based on the target device configuration item. Knox: Paragraph [0084] (“In some aspects, contextual agent computer application 128 may operate in conjunction with learning module computer application 128 to train data analysis models, fine-tune models for optimal performance, reduce analytic errors, identify bias, generalization, and the like. In some aspects, where previously unknown data input is presented, the contextual agent computer application 128 and learning module computer application 128 may operate to solicit feedback from system 10 users to confirm and validate newly presented engagement data 110, interaction data 130 or training data 112 from unknown data using reference information from external sources such as the internet, external services 28, the subject(s) of the data being compared, and associated users of said subject(s) via automated notification, requests for information, surveys, and the like.”) Knox: Paragraph [0091] (“At block 308, processing logic originates a list of recommendations for interactions. In some aspects, the list is ranked where recommendations are associated with a score or value based on user preferences, user status, schedule, or other interaction related dataset in ranked or rated lists according to favorability, unfavourability or neutrality. In some aspects, the recommendations are associated with interaction conditions or standards that clarify or qualify terms and/or requirements of a prospective interaction that identifies a single or range that may be considered optimal or acceptable based on available data. To further the example above, a recommendation to visit a subject by associated users may include conditions of day, time, permitted or non-permitted individuals, and the like. At block 310, processing logic receives an interaction confirmation from relevant parties including subject matter, method, schedule, location, terms or requirements, and prospective participants. In some aspects, the interaction confirmation information is presented based on parameters set based on user profile data or pre-set conditional data managed by the system. At block 312, the neural network computational model is updated with interaction outcome data. At block 314, the neural network computational model is updated with changes to system data, user profile data, preference data, and interaction data associated with respective users. In some aspects, the process of the system or method 300 can continue to any of blocks 302, 303, 306, 308, 310, 312, or 314.”) Knox: Paragraph [0094] (“At block 368, the processing logic coupled to API 24 selects at least one recommendation from data files associated with user profile data 114 and serves the recommendations to user interface 70 within ubicomp 12 or a user interface operating on computing device 26 using a distribution method and confirmation means. In some aspects, a distribution method and confirmation means can include an audible tone, voice or sound, visual image or graphic, alphanumeric value, video clip and the like. In some aspects, the rendered recommendations may be conditional and require a response that is time sensitive, the number of users that select the same recommendation, based which audience members respond, and the like. At block 370, the processing logic can change, edit or revise the recommendation list where the presented options are not accepted or selected by a single or multiple participants of the distributed interaction recommendations.”)
Regarding claim 3, Knox teaches all the claimed features of claim 1, from which claim 3 depends. Knox further teaches:
The method according to claim 1, wherein the historical device operation data comprises: control operations of devices under the target space and execution parameters of the control operations within a target time period, and Knox: Paragraph [0062] (“In some aspects, ubicomp 12 may be represented by common components of a home automation system such as networked lighting controls, audio controls, video controls, temperature sensors, alarm sensors, smoke and fire sensors, internet enabled devices, IoT devices and sensors, and other consumer peripherals.”) Knox: Paragraph [0079] (“User profile data 114 may also include user experience and historical user activity data such as lifestyle engagement routines, inference intelligence 132, platform activity, device activity, user interaction relationships and the like.”) Knox: Paragraph [0088] (“At step 210, process 200 collects inference intelligence 132 from recommendation engine 100 and computer application 128. Inference intelligence 132 may be indicative of … historical activities associated with the subject, audience, or unique engagement data 110 conditions recognized by trigger 204… In step 222, process 200, using the model, creates a record of the engagement conditions including calendar time stamp, category, type, unique identifier labels, related scores and values of possible interaction recommendations, interaction instructions for respective participants, reminder schedule, and user distribution list. Process 200, using the model, compiles the engagement information and interaction recommendations data records for distribution to respective users in the form of communication language such as alphanumeric characters, symbols, audible signals, or other executable commands. In step 224, based on the preset methods of communication associated with a user's profile information 206, process 200 prepares engagement information and interaction recommendations for distribution to system peripherals 10, API 24, computing devices 26, external networks 28, and other ubicomp 12 components in forms and styles consistent with the recipients preferred method.”) Knox: Paragraph [0009] (“The ubicomp devices may include a smart meter, a smart thermostat, … an IoT device, a smartphone, a smart watch, a smart window shade, a smart garage door, a smart outlet, a smart switch, a smart tablet, smart audio system, a smart TV, a smart mirror, a smart monitor screen, a smart camera, a smart pet feeder, a smart dishwasher, a smart washer/dryer, a smart microwave, a smart oven, a smart toaster, a smart coffee maker, a smart dishwasher, a smart refrigerator, a smart water faucet, a smart vacuum, a smart toy, a smart tracker, a smart door locks, a smart irrigation system, a smart energy monitoring, and/or a smart voice activated service.” Which read on “devices”.) [The compiled historical activities associated with ubicomp components or devices in a home automation system reads on “control operations of devices under the target space and execution parameters of the control operations within a target time period”.]
the device configuration recommendation information is obtained by: analyzing the control operations and execution parameters of the control operations of the devices under the target space within the target time period to obtain the device configuration recommendation information. Knox: Paragraphs [0085], [0094], [0095], and [0117] [As described in claim 1.] Knox: Paragraphs [0062], [0079], [0088], and [0009] [As described above.]
Regarding claim 6, Knox teaches all the claimed features of claim 1, from which claim 6 depends. Knox further teaches:
The method according to claim 1, wherein the device configuration recommendation information is also obtained by: obtaining a user habit data within the target time period; Knox: Paragraph [0114] (“Generally, user 516 preference information may include any lifestyle and wellness related data measurable by system 500 including but not limited to … activity schedule, sleep schedule, sleep quality, food consumption habits, food consumption schedule, media consumption schedule, media consumption habits, inter-personal social activity schedule, online activity habits, technology usage, and the like. In some examples, user 516 preference information may be generated and shared with group 544 or specific members 516, 518 based on detected behavioral data within ubicomp 602 such as a wearable biometric reading, video or audio device for person to person communications, emotion recognition signals, environmental sensors, motion or contact sensors, smart device online activity data, download and streaming content activity, facial recognition of users, networked appliances, and the like. In some aspects, users 516, 518 may use the API 512 via lifestyle and wellness program 532 to obtain recommended lifestyle and wellness interaction lists for user 516 such as probable sleep hours, probable food consumption schedules, … preferred media consumption schedules, preferred medicine administration timetable... In the present example, recommendation engine 508 may use computer application 78 to analyze and process ubicomp data 514 from individual users 516, 518 including lifestyle and wellness preference data from activity in system 500 and aggregated data from multiple 516, 518 users and groups 544 that are active within a group of systems 500. In some examples, computer application 78 implements automated recommendation engine 508 processes to identify and recommend health care practices based on individual user preference data or aggregated data from a select group of individual users identified amongst all available users in a networked group of systems 500.”) [The measured habits during hours or schedule reads on “obtaining a user habit data within the target time period”.]
analyzing correlation between the user habit data and the control operations of the devices under the target space, as well as the execution parameters of the control operations within the target time period, to determine the device configuration recommendation information corresponding to the user habit data. Knox: Paragraph [0009] (“The ubicomp devices may include a smart meter, a smart thermostat, … an IoT device, a smartphone, a smart watch, a smart window shade, a smart garage door, a smart outlet, a smart switch, a smart tablet, smart audio system, a smart TV, a smart mirror, a smart monitor screen, a smart camera, a smart pet feeder, a smart dishwasher, a smart washer/dryer, a smart microwave, a smart oven, a smart toaster, a smart coffee maker, a smart dishwasher, a smart refrigerator, a smart water faucet, a smart vacuum, a smart toy, a smart tracker, a smart door locks, a smart irrigation system, a smart energy monitoring, and/or a smart voice activated service.” Which read on “devices”.) Knox: Paragraph [0085] (“In some aspects, recommendation module computer application 128 may use a data identifier that recognizes patterns, trends, routines and habits associated with interaction datasets 130 to be used by recommendation engine 100 as benchmarks, thresholds, precursor, triggers and the like that predict or anticipate user activity or behaviors that precede or follow an interaction with ubicomp 12, system 10 components, API 24, or users operating computing device 26.”)
Regarding claim 8, Knox teaches all the claimed features of claim 1, from which claim 8 depends. Knox further teaches:
The method according to claim 1, wherein the device configuration recommendation information comprises a scene configuration plan for a target scene; Knox: Paragraph [0072] [As described in claim 1.] Knox: Paragraph [0045] (“Generally, an interaction recommendation system can provide recommendations for an unlimited set of user engagement instances or events, where optimal performance assumes the availability of adequate sensor data, computing capacity, and reference information.”) Knox: Paragraph [0078] (“For example, recommendation engine 100 detects and identifies a set of human actions and gestures that are assigned an interaction data 130 labeled “late night beverage” where the subject is observed with a networked camera in a kitchen opening a refrigerator and pouring a beverage in a glass during certain hours of the evening. A camera observing this interaction repeatedly may use object and label recognition to determine the type of drink and brand most often chosen by the subject and extract inference intelligence 132 associated with this specific user interaction dataset 130. In some aspects, inference intelligence 132 is inference intelligence 20. Using networked smart refrigerator inventory management technology, the recommendation engine 100 can alert the subject, other users, automated grocery list applications, and relevant external services to replenish the subject's preferred choice of late night beverages.”) Knox: Paragraph [0085] (“An interaction recommendation selection can be defined as a single or collection of user choices of actions and decisions, based on analyzed interaction data 130 and inference intelligence 132, that may be presented as ranked and/or rated lists based on favorable outcome, … circumstantial actions based on a data instance, scheduled or time based decisions, and responses to direct communications between user devices such as an electronic request for information on a smart device… Electronically, with a push notification of the aforementioned interaction description, either by designated schedule settings or an impromptu instance when the components of the optimal conditions are detected by recommendation engine 100, the subject's associated users can have reminders of each respective interaction element that comprises an optimal engagement which in turn empowers them to create the prescribed conditions for the subject, i.e.—order delivery of lunch at the designated time, find suitable images based on the subject's tastes, deliver images to the subject's tablet device, and send a reminder to other users to respect the subject's privacy during the activity.”) [The prescribed conditions for the subject and/or the events such as replenish preferred late night beverages reads on “a target scene”.)
before when obtaining the device configuration recommendation information for the device under the target space, pushing and displaying the device configuration recommendation information, the method comprises: [See indefiniteness rejection under 35 USC 112(b) presented above.]
obtaining the historical device operation data, wherein the historical device operation data records at least operating attribute of the device; Knox: Paragraph [0092] [As described in claim 1.] Knox: Paragraphs [0045] and [0078] [As described above.]
wherein the operating attribute is used to indicate at least one of an action performed by the device, a state after the device performs the action, and a time when the device performs the action; Knox: Paragraph [0045] and [0078] [As described above.] Knox: Paragraph [0085] (“An interaction recommendation selection can be defined as a single or collection of user choices of actions and decisions, based on analyzed interaction data 130 and inference intelligence 132, that may be presented as ranked and/or rated lists based on favorable outcome, … circumstantial actions based on a data instance, scheduled or time based decisions, and responses to direct communications between user devices such as an electronic request for information on a smart device… Electronically, with a push notification of the aforementioned interaction description, either by designated schedule settings or an impromptu instance when the components of the optimal conditions are detected by recommendation engine 100, the subject's associated users can have reminders of each respective interaction element that comprises an optimal engagement which in turn empowers them to create the prescribed conditions for the subject, i.e.—order delivery of lunch at the designated time, find suitable images based on the subject's tastes, deliver images to the subject's tablet device, and send a reminder to other users to respect the subject's privacy during the activity.” Which reads on “at least one of an action performed by the device, a state after the device performs the action, and a time when the device performs the action”.)
determining device configuration requirement for the target scene based on the operating attribute of the device recorded in the historical device operation data; Knox: Paragraph [0092] [As described in claim 1.] Knox: Paragraphs [0045] and [0078] [As described above.]
the device configuration requirement is used to indicate that at least one device needs to configure corresponding action in the target scene; generating the scene configuration plan for the target scene according to the device configuration requirement, so that at least one device performs corresponding action configured according to the scene configuration plan in the target scene, when the target scene is executed. Knox: Paragraphs [0045], [0078], and [0085] [As described above.] Knox: Paragraph [0091] (“To further the example above, a recommendation to visit a subject by associated users may include conditions of day, time, permitted or non-permitted individuals, and the like. At block 310, processing logic receives an interaction confirmation from relevant parties including subject matter, method, schedule, location, terms or requirements, and prospective participants. In some aspects, the interaction confirmation information is presented based on parameters set based on user profile data or pre-set conditional data managed by the system. At block 312, the neural network computational model is updated with interaction outcome data. At block 314, the neural network computational model is updated with changes to system data, user profile data, preference data, and interaction data associated with respective users. In some aspects, the process of the system or method 300 can continue to any of blocks 302, 303, 306, 308, 310, 312, or 314.”) Knox: Paragraph [0094] (“In some aspects, a distribution method and confirmation means can include an audible tone, voice or sound, visual image or graphic, alphanumeric value, video clip and the like. In some aspects, the rendered recommendations may be conditional and require a response that is time sensitive, the number of users that select the same recommendation, based which audience members respond, and the like. At block 370, the processing logic can change, edit or revise the recommendation list where the presented options are not accepted or selected by a single or multiple participants of the distributed interaction recommendations. Additional recommendations are re-distributed to respective users and added to the associated user profile data 114. In some aspects, the processing logic can change, edit or revise the recommendation list using a neural network model based at least in part on the available user data, inference information, audience status, schedule, notification protocol, user status and the like.”) Knox: Paragraph [0109] (“In another example, user 516 within ubicomp 512 is disabled or incapacitated and outside user 518 controls automated systems 520 and device 522 controls such as lighting, surveillance cameras, 2-way audio, HVAC zones, video calls, biometric measuring systems, motion sensor areas, home appliance, selection, delivery and presentation of audio, graphics, text and video content via streaming service, cloud server, cellular network, commercial subscription service, local network, or file stored on a local database. In yet another example, outside user 518 may create or adjust API 512 filters and settings for acquiring and analyzing ubicomp data 514 generated by user 516 within ubicomp 512 based on information provided by recommendation engine 508 regarding observed, historical and anticipated lifestyle habits and preferences.”)
Regarding claim 9, Knox teaches all the claimed features of claim 8, from which claim 9 depends. Knox further teaches:
The method according to claim 8, wherein determining the device configuration requirement for the target scene based on the operating attribute of the device recorded in the historical device operation data, comprises: based on the operating attribute of the device recorded in the historical device operation data, determining whether at least one device have performed a new action after the execution of a configured target scene; Knox: Paragraph [0088] (“At step 210, process 200 collects inference intelligence 132 from recommendation engine 100 and computer application 128. Inference intelligence 132 may be indicative of current activities and historical activities associated with the subject, audience, or unique engagement data 110 conditions recognized by trigger 204. In some aspects, a benchmark or threshold level reference file is established, labeled, and stored with user profile data 114 based on the available, triggered, extrapolated and interpreted data conditions, for future use by system 10 and recommendation engine 100. At step 212, process 200 generates a computational model using activity data 202, trigger data 204, user profile information 206, audience data 208, and inference intelligence 210. In step 214, process 200, using the model, determines possible interactions for the subject and audience members... In step 222, process 200, using the model, creates a record of the engagement conditions including calendar time stamp, category, type, unique identifier labels, related scores and values of possible interaction recommendations, interaction instructions for respective participants, reminder schedule, and user distribution list. Process 200, using the model, compiles the engagement information and interaction recommendations data records for distribution to respective users in the form of communication language such as alphanumeric characters, symbols, audible signals, or other executable commands. In step 224, based on the preset methods of communication associated with a user's profile information 206, process 200 prepares engagement information and interaction recommendations for distribution to system peripherals 10, API 24, computing devices 26, external networks 28, and other ubicomp 12 components in forms and styles consistent with the recipients preferred method. Similarly, in step 226, process 200 delivers engagement information and interaction recommendations to system peripherals 10, user devices 26, external networks 28, and other ubicomp 12 components on a schedule and sequence with the recipients preferred method of receiving information. In some examples, a recipient may reject one or all presented interaction recommendations, thus creating new rule and filter information and new inference intelligence 132, upon which process 200 returns to step 210. In step 228, upon a user executable function or automated command control, process 200 may provide additional information about the current engagement or interaction recommendations such as engagement and outcome historical data for the current subject and engagement matter, graphs and charts of interaction data analytics of the subject, rated and ranked recommendations based on aggregated data from related local, national, anonymized and/or demographic statistical data. In step 230, process 200, upon a user executable function or automated command control, will generate a computational model to track, analyze and compare expected engagement conditions with anticipated interaction outcomes based on known data (202, 204, 206, 208, 210) using probability calculations that, based on changes in benchmark or threshold values for the known data, will cause process 200 to recalculate the interaction recommendations starting with step 202. In this step, process 200, upon a user executable function or automated command control, creates a new contextual data record 210 to be used by process 200 and associated with a user profile information 206 and unique engagement conditions 204.”)
wherein the new action is different from an action configured for the device in a configured target scene; when yes, then act at least one new action performed by the device as device configuration requirement for the configured target scene, to indicate that at least one device needs to reconfigure the new action in the configured target scene. Knox: Paragraph [0088] [As described above.]
Regarding claim 11, Knox teaches all the claimed features of claim 8, from which claim 11 depends. Knox further teaches:
The method according to claim 8, wherein generating the scene configuration plan for the target scene based on the device configuration requirements comprises: creating the scene configuration attribute for the device in the target scene, according to the action that the device needs to configure in the target scene as indicated by the device configuration requirement, the scene configuration attribute indicating at least the action configured for the device in the target scene; Knox: Paragraphs [0045], [0078], [0085], [0091], [0094], and [0109] [As described in claim 8.]
generating the scene configuration plan based on the scene configuration attributes that have been created for each device in the target scene; or obtaining the scene configuration attribute that have been created for the device in the target scene; adjusting the scene configuration attribute for the device in the target scene, based on the action that the device needs to configure in the target scene as indicated by the device configuration requirement; generating the scene configuration plan based on adjusted scene configuration attributes for each device in the target scene. Knox: Paragraph [0088] (“At step 210, process 200 collects inference intelligence 132 from recommendation engine 100 and computer application 128. Inference intelligence 132 may be indicative of current activities and historical activities associated with the subject, audience, or unique engagement data 110 conditions recognized by trigger 204. In some aspects, a benchmark or threshold level reference file is established, labeled, and stored with user profile data 114 based on the available, triggered, extrapolated and interpreted data conditions, for future use by system 10 and recommendation engine 100. At step 212, process 200 generates a computational model using activity data 202, trigger data 204, user profile information 206, audience data 208, and inference intelligence 210. In step 214, process 200, using the model, determines possible interactions for the subject and audience members... In step 222, process 200, using the model, creates a record of the engagement conditions including calendar time stamp, category, type, unique identifier labels, related scores and values of possible interaction recommendations, interaction instructions for respective participants, reminder schedule, and user distribution list. Process 200, using the model, compiles the engagement information and interaction recommendations data records for distribution to respective users in the form of communication language such as alphanumeric characters, symbols, audible signals, or other executable commands. In step 224, based on the preset methods of communication associated with a user's profile information 206, process 200 prepares engagement information and interaction recommendations for distribution to system peripherals 10, API 24, computing devices 26, external networks 28, and other ubicomp 12 components in forms and styles consistent with the recipients preferred method. Similarly, in step 226, process 200 delivers engagement information and interaction recommendations to system peripherals 10, user devices 26, external networks 28, and other ubicomp 12 components on a schedule and sequence with the recipients preferred method of receiving information. In some examples, a recipient may reject one or all presented interaction recommendations, thus creating new rule and filter information and new inference intelligence 132, upon which process 200 returns to step 210. In step 228, upon a user executable function or automated command control, process 200 may provide additional information about the current engagement or interaction recommendations such as engagement and outcome historical data for the current subject and engagement matter, graphs and charts of interaction data analytics of the subject, rated and ranked recommendations based on aggregated data from related local, national, anonymized and/or demographic statistical data. In step 230, process 200, upon a user executable function or automated command control, will generate a computational model to track, analyze and compare expected engagement conditions with anticipated interaction outcomes based on known data (202, 204, 206, 208, 210) using probability calculations that, based on changes in benchmark or threshold values for the known data, will cause process 200 to recalculate the interaction recommendations starting with step 202. In this step, process 200, upon a user executable function or automated command control, creates a new contextual data record 210 to be used by process 200 and associated with a user profile information 206 and unique engagement conditions 204.”)
Regarding independent claim 19, the claim recites similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding independent claim 20, the claim recites similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over Knox in view of US Patent Publication No. 2021/0123771 to Vega et al. (“Vega”).
Regarding claim 4, Knox teaches all the claimed features of claim 3, from which claim 4 depends. Knox does not expressly teach the features of claim 4. However, Vega describes optimizing utility consumption associated with at least one premise. Vega teaches:
The method according to claim 3, wherein analyzing the control operations and execution parameters of the control operations of the devices under the target space within the target time period to obtain the device configuration recommendation information comprises: for each control operation of the devices under the target space within the target time period, when execution frequency of the control operation within the target time period is greater than or equal to a frequency threshold, determining the control operation as a pending analysis control operation; Vega: Paragraph [0290] (“In step 13030 of FIGS. 26-27, advisory recommendations are triggered when the energy advisor system and method associates deviations in energy consumption patterns to situations requiring remediation. Once a predetermined variation threshold in the baseline is exceeded, based on consumer preferences and personalized models, flag and notify customer and prompting for input (including changes in: behavior, lifestyle, household features, appliances, etc.). Then, recalculate baseline usage predictions based on customer input and reassess variation threshold. If usage deviations are explained by the updated baseline model, clear the flag and update baseline model. When a deviation cannot be mapped to the baseline model updated with customer inputs, the system conducts diagnostics analysis and recommends possible remediation to eliminate or mitigate usage increases (e.g., set point adjustments, maintenance, repairs, replacement, upgrades of household features or appliances). A remediation may include for example, but not be limited to, adjustments in set points in end-use energy consuming appliances for energy consumption reduction, improved performance, and cost savings based on customer's predetermined goals, premises or appliance maintenance, premises or appliance repair, premises or appliance replacement, premises or appliance upgrade, etc.”) Vega: Paragraph [0292] (“In step 13040 of FIGS. 26-27, the information about end-use energy consuming appliances and smart devices from earlier energy fingerprint calculations may be complemented with actual integration (e.g., cloud or on-premises) to allow automated actions based on optimum set points determined by the models and analytics in the energy advisor system from time series usage, building features, and behavioral data.”) Vega: Paragraph [0322] (“Further, Home Modes, Data is received from Smart Meter & Devices 3910, Define Optimization is Run based on user settings and goals 3920, Partition data in time series cycles 3930, Aggregate Data 3940, Evaluate, Rate, Rank Impact (e. g. cost, environmental behavior) 3950 and Aggregate Results, Prioritize, Optimization Recommendation 3960.”) [The diagnostic analysis reads on “a pending analysis control operation”.]
analyzing execution parameters of pending analysis control operations, to obtain a target control operation and a target execution parameter corresponding to the target control operation; Vega: Paragraphs [0290], [0292], and [0322] [As described above.] Vega: Paragraph [0344] (“Referring now to FIG. 41, there may be seen one example of a process for machine learning for prescriptive analytics with customer interactions. More particularly, the energy analytics engine (optimization advisory engine) starts with the customer historical usage, preferences and behavioral data 6010, reviews the baseline personalized model (energy fingerprint) and descriptive analytics 6020 and monitors the machine learning portion of the baseline personalized model and its descriptive analytics 6030 and determines if the model thresholds 6040 for variations that exceed those thresholds 6045 and then evaluates if there are explanations for the variance based on location events or weather factors 6050, and if so, alerts and queries the customer 6060. If the customer is satisfied with the variance, then the customer may initiate modifications to existing schedules or set points to clear the variance 6062. If not, a query is made regarding changes in occupancy 6064 and if not a query is made regarding changes in appliances and features 6066. For items 6062, 6064, and 6064, if change have been made then the baseline personalized model monitoring step 6030 is changed or modified to reflect these changes. And, if the baseline model is modified then the optimization recommendations and actuations are modified 6070, which in turn results in modifications to the baseline model 6080 and then the energy optimization score (e.g. Energy IQ) 6090.”) [The updated baseline model reads on “a target control operation”.]
based on the target control operation and the target execution parameter corresponding to the target control operation, obtaining the device configuration recommendation information. Vega: Paragraphs [0290], [0292], [0322], and [0344] [As described above.] [The modified optimization recommendations read on “obtaining the device configuration recommendation information”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Knox and Vega before them, for each control operation of the devices under the target space within the target time period, when execution frequency of the control operation within the target time period is greater than or equal to a frequency threshold, determining the control operation as a pending analysis control operation; analyzing execution parameters of pending analysis control operations, to obtain a target control operation and a target execution parameter corresponding to the target control operation; based on the target control operation and the target execution parameter corresponding to the target control operation, obtaining the device configuration recommendation information because the references are in the same field of endeavor as the claimed invention and they are focused on device optimization recommendations.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would improve optimizing utility consumption associated with at least one premises and for machine learning algorithms to continuously learn new patterns and update existing patterns from the users refining suggestions and actions to improve energy consumption with minimal discomfort. Vega Paragraphs [0006] and [0336].
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter
The subject matter of claims 5 and 12-18 is found to be allowable over the prior art of record and would be considered allowable pending indefiniteness rejection under 35 USC 112(b) given above to base claim independent claim 1.
While the prior art describes that when obtaining device configuration recommendation information for a device under a target space, pushing and displaying the device configuration recommendation information; wherein the device configuration recommendation information is obtained by analyzing a historical device operation data; responding to a selection operation for at least one device configuration item in the device configuration recommendation information, displaying device control information corresponding to a selected target device configuration item; responding to a configuration operation for the selected target device configuration item, performing configuration processing on a corresponding device based on a target device configuration item, to apply the target device configuration item (See Knox et al. (US Patent Publication No. 2020/0364588 A1); Vega et al. (US Patent Publication No. 2021/0123771 A1); York (US Patent Publication No. 2012/0303377 A1); Rufo et al. (US Patent Publication No. 2018/0342329 A1)) the prior art, individually or combined, does not teach or suggest “obtaining an expected time of the execution times of the pending analysis control operations; calculating differences between the expected time and the execution times of all the pending analysis control operations; when the number of differences less than a preset time difference accounts for a proportion greater than or equal to a preset proportion threshold of the total number of differences, determining the expected time of the execution times of the pending analysis control operations as target execution time corresponding to the target control operation,” as recited in claim 5; “after performing configuration processing on the corresponding device based on the target device configuration item, to apply the target device configuration item, the method further comprises: obtaining a first device parameter of a configured target scene, wherein the first device parameter is generated when the smart device executing an initial action configured in the target scene; obtaining a scene steady-state set of the configured target scene, wherein the scene steady-state set comprises second device parameters generated during multiple scene observation periods; wherein each second device parameter is generated when the smart device executing an additional action during a single scene observation period; updating the device control command when a target scene is triggered based on the first device parameter and the second device parameters in the scene steady-state set”, as recited in claim 12; and “after performing the configuration processing on the corresponding device based on the target device configuration item, to apply the target device configuration item, the method further comprises: displaying an instruction optimization data for the target scene, wherein the instruction optimization data is used to indicate an updated device control command; the updated device control command is determined by the control terminal based on a first device parameter of the target scene and second device parameters in a scene steady-state set; the first device parameter is generated by the smart device executing an initial action configured in the target scene, indicating a device state of the smart device; wherein the scene steady-state set comprises the second device parameters generated during multiple scene observation periods; each second device parameter is generated by the smart device an executing additional action during a single scene observation period”, as recited in claim 18. These concepts define the present application over the prior art of record.
In view of their dependencies to an allowable claim, claims 13-17 are found to be allowable over prior art.
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.
US Patent Publication No. 2018/0342329 A1 to Rufo et al. describes in Paragraph [0160] (“As part of the still image upload to the remote server 100, combined motion events on the cameras 193 and 112 and usage on the HAPPIE home unit 103, as well as possible sound received by microphones 113 within the home, the HAPPIE server 100 will assemble a “pattern” for the resident. This should include the resident's normal waking hours, the resident's activities like watching TV, how often the resident eats, normal times for opening the doors, etc. The pattern can be tracked over time, and appropriate alerts generated to a caregiver and family members via the caregiver portal 101 if the resident's pattern changes significantly. Data associated with any relevant patterns uncovered by the machine learning algorithms will be transmitted to the HAPPIE home unit 103, and in appropriate circumstances, to the caregiver via the caregiver portal 101.”) Rufo describes in Paragraph [0283] (“The trend analysis performed by the HAPPIE home unit 103 may be able to pinpoint the cause as a missed medication, the wrong dosage, a missed meal, consuming something off of the resident's nutritional plan, over exertion while exercising, or even the onset of a cold or the flu. The HAPPIE home unit 103 is able to provide analytics that would be otherwise unavailable to the caregiver or medical professional without the comprehensive data collection performed as a routine by the HAPPIE home unit 103. Based upon the data analysis, the HAPPIE home unit 103 may, if appropriate, generate text, video, or audio alerts and messages to be received by the caregiver on the caregiver's smart phone 178, or which can be accessed by the caregiver from the caregiver portal 101 or by medical professionals from the doctor portal 102.”)
York (US Patent Publication No. 2012/0303377 A1) describes personalized content, such as health care plans, health actions, and interactive games, to a user. Based on such health data provided by a user, the health management application may provide content, such as health information or health care plans, that is tailored to the current health status or condition of the user. In some instances, a user may request to participate in a particular health action or task. The health management application may recommend personalized tasks or actions for the user to complete to promote and improve health. Following partial or successful completion of a chosen health action, a user may earn points or rewards that may be redeemable for various prizes. A user may also earn points by completing an educational or informational task such as quizzes, questionnaires, or surveys. Points may also be earned by participating in or playing a game.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M. CHOI whose telephone number is (571)272-1473. The examiner can normally be reached on Monday - Friday 7:30 am to 5:00 pm.
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
