DETAILED ACTION
This office action is responsive to the response filed 1/7/2026. The application contains claims 1-3, 9-11, 14-16, 19-29, all examined and rejected.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/7/2026 has been entered.
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 1-3, 9-11,14-16,19-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 19 and 20 are each directed to a statutory category, it recites a series of steps pertaining to analyzing context data to produce an action based on the context data, which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-3, 9-11,14-16,19-29 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include machine, manufacture and a process as in independent Claim 1, 19, and 20 and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to analyze context data to provide based on the analysis suggested actions (Mental process, observation, evaluation and judgment) which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
processing, the first input to generate an output that describes one or more semantic entities referenced by the context data; generating, based on a second input comprising the one or more semantic entities referenced by the context data, a second output comprising text descriptive of a suggested action that can be taken by the artificial intelligence system or a computer application under direction of the artificial intelligence system; providing, based on the output, a suggested action with respect to the one or more semantic entities described by the output (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
A computing system, the computing system comprising: an artificial intelligence system with memory-based context storage (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
wherein the artificial intelligence system comprises one or more machine-learned models (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)),
wherein the artificial intelligence system is configured to perform operations, the operations comprising:
during a first time interval:
receiving, by the artificial intelligence system, a model input that includes context data (insignificant extra-solution activity, MPEP 2106.05(g));
artificial intelligence system, the model input with the one or more machine-learned models to generate a model output, by the artificial intelligence system, (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
storing, in the memory-based context storage, the one or more semantic entities referenced by the context data; and during a second time interval that is after the first time interval (insignificant extra-solution activity, MPEP 2106.05(g));
automatically providing, via a user interface of the computing system, a selectable interface for selecting the suggested action described in the second model output, the suggested action selectable via the user interaction with the user interface to cause the suggested action to be performed (insignificant extra-solution activity, MPEP 2106.05(g));
receiving a user interaction via the user interface of the computing system selecting the suggested action (insignificant extra-solution activity, MPEP 2106.05(g)); and
wherein the computing system is configured to perform the suggested action responsive to the user interaction (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
A computing system, the computing system comprising: an artificial intelligence system with memory-based context storage (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
wherein the artificial intelligence system comprises one or more machine-learned models (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
artificial intelligence system, the model input with the one or more machine-learned models to generate a model output, by the artificial intelligence system(merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)),
during a first time interval: receiving, by the artificial intelligence system, a model input that includes context data (WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
artificial intelligence system, the first model input with the one or more machine-learned models to generate a first model output (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
storing, in the memory-based context storage, the one or more semantic entities referenced by the context data; and during a second time interval that is after the first time interval (WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY, storing data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii));
artificial intelligence system, the second model input, second model output (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
and during a second time interval that is after the first time interval (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use);
automatically providing, via a user interface of the computing system, a selectable interface for selecting the suggested action described in the second model output, the suggested action selectable via the user interaction with the user interface to cause the suggested action to be performed (Well-Understood, Routine, Conventional activity, See at least US 20150378531 A1, ¶22, “user selectable display spaces may include a tab, graphical or textual icons, or include any other known form. In accordance with an embodiment, the GUI further comprises of a plurality of user selectable display spaces to control and configure various operations”, US 20020186262 A1, ¶47, “labels may be selected by using known technologies, such as navigation buttons, a stylus, and the like, to select the icon on the display”, US 20150067603 A1, ¶3, “ A known method is to display a solid body having icons on a display screen to make a user select one of the icons, which are used to give various instructions to information devices including computers with displays”, US 20090217173 A1, ¶36, “ GUI 200 may display, begin, or activate the image, graphic, rich media file, etc. associated with an icon when the icon is selected such as for example by passing cursor 21 over the icon, clicking on the icon, or other techniques known in the art”;
receiving a user interaction via a user interface of the computing system selecting the suggested action (Well-Understood, Routine, Conventional activity, storing data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii));
wherein the computing system is configured to perform the suggested action responsive to the user interaction (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 19 recites a system comprising “non-transitory, computer readable medium” configured to perform the same method as set forth in claim 1, the added element of “non-transitory, computer readable medium” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Independent claims 19 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 19 and method independent claim 20 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claim 2 disclose “computing system of claim 1, wherein the context data comprises visual or audio information displayed or played by the computing system” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 3 disclose “computing system of claim 2, wherein the context data comprises a screenshot automatically captured during use of the computing system” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 9 disclose “computing system of claim 1, wherein the artificial intelligence system operates to store multiple model outputs in the memory over time (storing data is extra-solution that is Well-Understood, Routine, Conventional Activity storing data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii)), and wherein the artificial intelligence system operates to rank, sort, or categorize the multiple model outputs to manage an amount of data that is stored in the memory.” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 10 disclose “wherein the artificial intelligence system is configured to automatically remove or overwrite the stored one or more semantic entities based on one or more rules related to the age of the stored one or more semantic entities” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 11 disclose “wherein the suggested action includes one or more of a communication action, an information retrieval action, a booking action, or an information storage action” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 14 disclose “wherein the artificial intelligence system displays the suggested action in response to an event, and wherein the artificial intelligence system displays the suggested action at a predetermined time interval prior to the event.” (extra-solution that is Well-Understood, Routine, Conventional Activity display data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 15 disclose “wherein the event is an identified change to at least one of the one or more semantic entities.” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 16 disclose “wherein the second time interval is at least one day later than the first time interval” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 21 disclose “wherein the text descriptive of the suggested action comprises text identifying the computer application to perform the suggested action” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 22 disclose “wherein the text descriptive of the suggested action conforms to a predefined format” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 23 disclose “wherein the predefined format comprises a first portion for a semantic entity value and a second portion for a computer application value” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 24 disclose “storing, in the memory-based context storage, in association with the one or more semantic entities referenced by the context data, explanation information indicating a time when the context data was obtained” (storing data is extra-solution that is Well-Understood, Routine, Conventional Activity storing data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 25 disclose “wherein the operations comprise: detecting a user interaction that requests the explanation information; and in response to detecting the user interaction, presenting, via the user interface, the explanation information” (receiving and displaying data is extra-solution that is Well-Understood, Routine, Conventional Activity displaying data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 26 disclose “receiving, via the user interface, an adjustment to rules controlling how the artificial intelligence system collects the context data (receiving data is extra-solution that is Well-Understood, Routine, Conventional Activity receiving data is common and basic functions in computer technology, MPEP 2106.05(d)(II)(iii)); and removing, after receiving the adjustment, data that is stored in the memory-based context storage based on the rules (mental process)” It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 27 disclose “a user computing device comprising a camera; wherein the context data comprises image data from the camera that is automatically captured as the user computing device is used to perform a task”; description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 28 disclose “wherein the image data describes a surrounding environment of a user” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 29 disclose” wherein the one or more semantic entities referenced by the context data are not specifically identified by the user” description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims, are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 11, 16, and 19-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Liu et al. [US 11,715,042 B1, hereinafter Liu].
With regard to Claim 1,
Liu teach a computing system, the computing system comprising (Fig. 13, Col. 47, lines 10-25):
an artificial intelligence system with memory-based context storage (Fig. 13, Col. 47, lines 29-46, “desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these”), wherein the artificial intelligence system comprises one or more machine-learned models (Fig. 4, Abstract, “training a target machine-learning model iteratively by accessing training data of content objects, training an intermediate machine-learning model that outputs contextual evaluation measurements based on the training data, generating state-indications associated with the training data, wherein the state-indications comprise user-intents, system actions, and user actions, training the target machine-learning model based on the contextual evaluation measurements, the state-indications, and an action set comprising possible system actions, extracting rules based on the target machine-learning model by a sequential pattern-mining model”, Col. 11, lines 51-61, Col. 19-20, lines 61-16, “the meta-intent classifier may be based on a machine-learning model”, Col. 12, lines 54-57), and
wherein the artificial intelligence system is configured to perform operations, the operations comprising:
during a first time interval:
receiving, by the artificial intelligence system, a first model input that includes context data (“NLU module 220 may identify a domain, an intent, and one or more slots from the user input”, Col, 18, lines 57-67, “The CU object generator 307 may generate particular content objects relevant to the user request. The content objects may comprise dialog-session data and features associated with the user request, which may be shared with all the modules of the assistant system 140. In particular embodiments, the request manager 305 may store the contextual information and the generated content objects in data store 310 which is a particular data store implemented in the assistant system 140. In particular embodiments, the request manger 305 may send the generated content objects to the NLU module 220”);
processing, by the artificial intelligence system, the first model input with the one or more machine-learned models to generate a first model output comprising text that describes one or more semantic entities referenced by the context data (Col. 12, lines 12- 24, “ The NLU module 220 may classify the text/speech input … e.g., for the input “Play Beethoven's 5th,” the NLU module 220 may classify the input as having the intent [IN:play_music]. … a set of valid or expected named slots may be conditioned on the classified intent. As an example and not by way of limitation, for [IN:play_music], a slot may be [SL:song_name]”, Col. 13, lines 50-56, “entity resolution module 240 may therefore accurately resolve the entities associated with the user input in a personalized and context-aware manner”); and
storing, in the memory-based context storage, the one or more semantic entities referenced by the context data (Col. 20, lines 10-14, “The processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time”, Col. 18, lines, 61-65, “request manager 305 may store the contextual information and the generated content objects in data store 310”);
generating, by the artificial intelligence system, based on a second model input comprising the one or more semantic entities referenced by the context data, a second model output comprising text descriptive of a suggested action that can be taken by the artificial intelligence system or a computer application under direction of the artificial intelligence system (Col. 21, lines 63-66, “task completion module 335 may comprise an action selection component 336”, Col. 22, lines 25-29, “the action selection component 336 may therefore select an action based on the dialog intent, the associated content objects, and the guidance from dialog policies 320”, Col. 22, lines 9-14, “a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine 235. A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm_artist, args:{artist: “Madonna”}} “, Col. 12, lines 14-25, “for the input “Play Beethoven's 5th,” the NLU module 220 may classify the input as having the intent [IN:play_music]. In particular embodiments, a domain may be conceptually a namespace for a set of intents, e.g., music. A slot may be a named sub-string with the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [SL:dish]. In particular embodiments, a set of valid or expected named slots may be conditioned on the classified intent. As an example and not by way of limitation, for [IN:play_music], a slot may be [SL:song_name]. The semantic information aggregator 230 may additionally extract information“, Col. 25, lines 30-35, “ if two users are discussing a plan to meet, the proactive assistant should provide a suggestion to set up a meeting reminder only after the users have agreed on a time and place of the meeting”);and
during a second time interval that is after the first time interval:
automatically providing, via a user interface of the computing system, a selectable interface for selecting the suggested action described in the second model output (Col. 15, lines 32-36, “CU composer 270 may comprise a natural language generator (NLG) 271 and a user interface (UI) payload generator 272. The natural-language generator 271 may generate a communication content based on the output 35 of the dialog engine 235”, Col. 15-16, lines 65-67, “surface realization component may determine specific 65 words to use, the sequence of the sentences, and the style of the communication content”), the suggested action selectable via the user interaction with the user interface to cause the suggested action to be performed (Fig. 4-7, Fig. 8, Col. 27, lines 42-44, “extract state features {I, C} from a set of conversations 400, i.e., NLU intents I for all turns and action related features (impression 424 and click 426)”, Col. 26, lines 15-22, “one or more user actions may comprise user-interactions with one or more content objects. p is the likelihood of a user clicking on the suggestion”, Col. 27, lines 10-13, “use a reward value of +2.5 for a suggestion which is clicked, and −0.1 for a suggestion that is shown but not clicked“, Col. 28, lines 1-5, Col. 30, lines 31-34, “GBDT model seems to treat user clicking on the suggestion displayed in previous turns as an important feature that can be used to predict a click in the most recent turn”, Col. 15, lines 5-7, “user input may comprise “book me a ride to the airport”); and
receiving a user interaction via a user interface of the computing system selecting the suggested action (Fig. 4, Fig. 8, col. 26, lines 5-9 “Ck represents a set of action features such as whether the assistant had shown a suggestion at step k (impression 424) and whether the user had clicked on the suggestion (click 426)”, Col. 27, lines 10-13, “use a reward value of +2.5 for a suggestion which is clicked, and −0.1 for a suggestion that is shown but not clicked“, Col. 28, lines 1-5, Col. 30, lines 31-34, “GBDT model seems to treat user clicking on the suggestion displayed in previous turns as an important feature that can be used to predict a click in the most recent turn”);
wherein the computing system is configured to perform the suggested action responsive to the user interaction (Fig. 8, col. 26, lines 5-9 “Ck represents a set of action features such as whether the assistant had shown a suggestion at step k (impression 424) and whether the user had clicked on the suggestion (click 426)”, Col. 27, lines 10-13, “use a reward value of +2.5 for a suggestion which is clicked, and −0.1 for a suggestion that is shown but not clicked“, Col. 22, lines 29-34, “the selected action may require one or more agents 340 to be involved. As a result, the task completion module 335 may inform the agents 340 about the selected action”, Col. 15, lines 5-7, “user input may comprise “book me a ride to the airport.” A transportation agent may execute the task of booking the ride”).
With regard to Claim 2,
Lui teach the computing system of claim 1, wherein the context data comprises visual or audio information displayed or played by the computing system (Col. 2, lines 7-14“assistant system may assist a user to obtain information or services. The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video) in stateful and multi-turn conversations to get assistance “, Col. 16, 15-19, “contextual information associated with the user profile may indicated that the user is using a device that only outputs audio signals so the UI payload generator 272 may determine the modality of the communication content as audio”, Col. 46, lines 33-37, “use, as inputs, personal or biometric information of a user for user-authentication or experience-personalization purposes”, lines 48-57, “user may provide a voice recording of his or her own voice to provide a status update on the online social network. The recording of the voice-input may be compared to a voice print of the user to determine what words were spoken by the user”).
With regard to Claim 11,
Lui teach the computing system of claim1,wherein the suggested action includes one or more of a communication action, an information retrieval action, a booking action, or an information storage action (Col. 15, lines 1-11, “retrieve a user profile from the user context engine 225 to execute tasks in a personalized and context-aware manner … a user input may comprise "book me a ride to the airport." A transportation agent may execute the task of booking the ride. The transportation agent may retrieve the user profile of the user from the user context engine 225 before booking the ride. For example, the user profile may indicate that the user prefers taxis, so the transportation agent may book a taxi for the user”).
With regard to Claim 16,
Lui teach the computing system of claim 1, wherein the second time interval is at least one day later than the first time interval (Col. 20, lines 4-11, “The offline aggregators 226 may process a plurality of data associated with the user that are collected from a prior time window. As an example and not by way of limitation, the data may include news feed posts/comments, interactions with news feed posts/comments, Instagram posts/comments, search history, etc. that are collected from a prior 90-day window. The processing result may be stored in the user context engine 225 as part of the user profile”, Col. 15, lines 1-11, “retrieve a user profile from the user context engine 225 to execute tasks in a personalized and context-aware manner ... a user input may comprise "book me a ride to the airport.” ... transportation agent may retrieve the user profile of the user from the user context engine 225 before booking the ride. For example, the user profile may indicate that the user prefers taxis, so the transportation agent may book a taxi for the user”).
Regarding claims 19 and 20,
Claims 19 and 20 are similar in scope to claim 1; therefore they are rejected under similar rationale. Liu further teach a non-transitory computer readable medium (Col. 50 lines 32-46, claims 19-20).
With regard to Claim 21,
Lui teach the computing system of claim 1, wherein the text descriptive of the suggested action comprises text identifying the computer application to perform the suggested action (Col. 21, lines 63-66, “task completion module 335 may comprise an action selection component 336”, Col. 22, lines 25-29, “the action selection component 336 may therefore select an action based on the dialog intent, the associated content objects, and the guidance from dialog policies 320”, Col. 22, lines 29-34, “the selected action may require one or more agents 340 to be involved. As a result, the task completion module 335 may inform the agents 340 about the selected action”, Col. 15, lines 5-7, “user input may comprise “book me a ride to the airport.” A transportation agent may execute the task of booking the ride”, Col. 22, lines 9-14, “a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine 235. A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm_artist, args:{artist: “Madonna”}}).
With regard to Claim 22,
Lui teach the computing system of claim 21, wherein the text descriptive of the suggested action conforms to a predefined format (Col. 22, lines 9-14, “a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine 235. A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm_artist, args:{artist: “Madonna”}}, Col. 12, lines 14-25, “for the input “Play Beethoven's 5th,” the NLU module 220 may classify the input as having the intent [IN:play_music]. In particular embodiments, a domain may be conceptually a namespace for a set of intents, e.g., music. A slot may be a named sub-string with the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [SL:dish]. In particular embodiments, a set of valid or expected named slots may be conditioned on the classified intent. As an example and not by way of limitation, for [IN:play_music], a slot may be [SL:song_name]. The semantic information aggregator 230 may additionally extract information“, col. 26, lines 5-9 “Ck represents a set of action features such as whether the assistant had shown a suggestion at step k (impression 424) and whether the user had clicked on the suggestion (click 426)”).
With regard to Claim 23,
Lui teach the computing system of claim 22, wherein the predefined format comprises a first portion for a semantic entity value and a second portion for a computer application value (Col. 22, lines 9-14, “a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine 235. A goal may be represented by an identifier (e.g., string) with one or more named arguments … {confirm_artist, args:{artist: “Madonna”}}”, Col. 12, lines 14-25, “NLU module 220 may classify the input as having the intent [IN:play_music] … domain may be conceptually a namespace for a set of intents, e.g., music. A slot may be a named sub-string with the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [SL:dish]. In particular embodiments, a set of valid or expected named slots may be conditioned on the classified intent”, Col. 22, lines 29-34, “the selected action may require one or more agents 340 to be involved. As a result, the task completion module 335 may inform the agents 340 about the selected action”, Col. 15, lines 5-7, “user input may comprise “book me a ride to the airport.” A transportation agent may execute the task of booking the ride”).
With regard to Claim 24,
Lui teach the computing system of claim 1, wherein the operations comprise: storing, in the memory-based context storage, in association with the one or more semantic entities referenced by the context data, explanation information indicating a time when the context data was obtained (Fig. 4-8, Col. 25, lines 30-35, “ if two users are discussing a plan to meet, the proactive assistant should provide a suggestion to set up a meeting reminder only after the users have agreed on a time and place of the meeting”, Col. 15, lines 12-16, “the contextual information associated with the user profile may indicate that the user is in a hurry so the transportation agent may book a ride from a ride-sharing service (e.g., Uber, Lyft) for the user since it may be faster to get a car from a ride-sharing service than a taxi company”, system infer that the user is late/in hurry using context information that include the user request time (i.e. now vs arrival time or event start)).
With regard to Claim 25,
Lui teach the computing system of claim 24, wherein the operations comprise: detecting a user interaction that requests the explanation information; and in response to detecting the user interaction, presenting, via the user interface, the explanation information (Fig. 4-8, Col. 25, lines 30-35, “ if two users are discussing a plan to meet, the proactive assistant should provide a suggestion to set up a meeting reminder only after the users have agreed on a time and place of the meeting”, Col. 30, lines 31-34, “GBDT model seems to treat user clicking on the suggestion displayed in previous turns as an important feature that can be used to predict a click in the most recent turn”, col. 26, lines 5-9 “Ck represents a set of action features such as whether the assistant had shown a suggestion at step k (impression 424) and whether the user had clicked on the suggestion (click 426)”, Col. 15, lines 15-16, “user input may comprise "book me a ride to the airport." A transportation agent may execute the task of booking the ride … the contextual information associated with the user profile may indicate that the user is in a hurry so the transportation agent may book a ride from a ride-sharing service (e.g., Uber, Lyft) for the user since it may be faster to get a car from a ride-sharing service than a taxi company.”).
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Kaplan et al. [US 2014/0149330 A1, hereinafter Kaplan].
With regard to Claim 3,
Lui teach the computing system of claim 2.
Liu does not explicitly teach wherein the context data comprises a screenshot automatically captured during use of the computing system.
Kaplan teach the context data comprises a screenshot automatically captured during use of the computing system (Fig. 5-8, ¶11, “method further comprises running a background service on the computer, wherein said detecting, intercepting, collecting and injecting are performed by said background service “, ¶14, “aid collecting is performed fully-automatically, such that computer work of the user is not interrupted”, ¶39, “present contextual knowledge management system may automatically intercept the data being gathered online”, screen shots are context data that the system process to extract information).
Liu and Kaplan are analogous art to the claimed invention because they are from a similar field of endeavor of extracting context data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by Kaplan with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to allow capturing and preserving displayed content to allow further analysis and learning about user interests which improve recommendation abilities of the system. This is applying a known technique to a known device (method, or product) ready for improvement to yield predictable results and yield predictable results (MPEP 2143)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Gruper et al. [US 2013/0275164 A1, hereinafter Gruper].
With regard to Claim 9,
Lui teach the computing system of claim 1.
Liu appears to show outputs ranking during aggregation suggesting prioritizing for downstream usage (Col. 20, lines 19-44, “semantic information aggregator 230 may process the retrieved information from the user context engine 225 … At step 234, the semantic information aggregator 230 may rank the entities tagged by the entity tagger).
However in effort to expedite persecution Garber explicitly teach artificial intelligence system operates to rank, sort, or categorize the multiple model outputs to manage an amount of data that is stored in the memory (¶¶750-756, sorting and ranking different outputs and use different match level (strong, week, none)).
Liu and Garber are analogous art to the claimed invention because they are from a similar field of endeavor of extracting context data for providing recommendations. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by Garber with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to apply matching levels and precedence to filter and prioritize data, implying Efficiency in selection and memory usage which improve recommendation abilities of the system. This is applying a known technique to a known device (method, or product) ready for improvement to yield predictable results and yield predictable results (MPEP 2143).
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Erman et al. [US 2011/0307354 A1, hereinafter Erman].
With regard to Claim 10,
Lui teach the computing system of claim 1, stored one or more semantic entities (Col. 12, lines 12- 24, “ The NLU module 220 may classify the text/speech input … e.g., for the input “Play Beethoven's 5th,” the NLU module 220 may classify the input as having the intent [IN:play_music]. … a set of valid or expected named slots may be conditioned on the classified intent. As an example and not by way of limitation, for [IN:play_music], a slot may be [SL:song_name]”, Col. 13, lines 50-56, “entity resolution module 240 may therefore accurately resolve the entities associated with the user input in a personalized and context-aware manner”, Col. 20, lines 10-14, “The processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time”, Col. 18, lines, 61-65, “request manager 305 may store the contextual information and the generated content objects in data store 310”).
Liu does not explicitly teach automatically remove or overwrite the stored
Erman teach artificial intelligence system is configured to automatically remove or overwrite the stored [model output] (¶19, “application recommendation capability is configured for automatically providing application recommendations to users. The application recommendation capability uses an application guide server that is configured for selecting recommended applications for a user and for providing recommended application information associated with the recommended applications to the user”, ¶45, “MD 110 receives the recommended application information from AGS 120. The MD 110 stores the recommended application information for presentation to the user via MD 110”, ¶157, ¶159) based on one or more rules related to the age of the [model output] (¶47, “MD 110 is adapted to replace at least a portion of existing recommended application information with new recommended application information. In one embodiment, for example, MD 110 may replace existing recommended application information with new recommended application information in response to receiving new recommended application information (e.g., where new recommended application information is pushed to MD 110 by AGS 120, which may be periodically, in response to indications and/or requests received at AGS 120 from MD 110, and the like, as well as various combinations thereof). In one embodiment, for example, MD 110 may replace existing recommended application information with new recommended application information by initiating requests to AGS 120 for new recommended application information in response to one or more trigger conditions (e.g., in response to some or all of the existing recommended application information being outdated, in response to memory constraints, and the like, as well as various combinations thereof”, ¶150, “recommended application icon that is displayed for a recommended application may be removed from main screen 511 in response to a determination that the user has not selected the recommended application icon after a threshold length of time after the recommended application icon was first made available via main screen 511 (e.g., after one day, two days, a week, or any other suitable length of time, which may or may not be configurable by the user)”, ¶161, “recommended application in the list of recommended applications is removed from the list of recommended applications upon expiration of a timer associated with the recommended application”).
Liu and Erman are analogous art to the claimed invention because they are from a similar field of endeavor of providing recommendations to users based on context data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by Erman with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to facilitate locating the most relevant data to the user. This is applying a known technique to a known device (method, or product) ready for improvement to yield predictable results and yield predictable results (MPEP 2143).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Gunasekaran Govindarajan [US 2010/0211425 A1, hereinafter D1].
With regard to Claim 14,
Lui teach the computing system of claim 1,
Liu does not explicitly teach wherein the artificial intelligence system displays the suggested action in response to an event, and wherein the artificial intelligence system displays the suggested action at a predetermined time interval prior to the event.
D1 teach displays the suggested action in response to an event, and wherein the artificial intelligence system displays the suggested action at a predetermined time interval prior to the event (Abstract, , system calculate and display ETA for attendees of a meeting event (displaying and suggesting actions based on event), ¶41, ¶43, ¶¶55, “At a predetermined first time interval before a scheduled beginning of the meeting … (1) the interrogating of its then geographical location by the communications device of each prospective meeting attendee, … At this time the ETA of each prospective meeting attendee is calculated, but the time interval (the 2 h) has intentionally been chosen so that all attendees can potentially timely transit to the meeting. If any are already likely late for the meeting … then these persons are automatically messaged that their optimal time of departure (to make the meeting on time) is already past! “, ¶56, ¶60, “until some predetermined second time interval before the scheduled start of the meeting, normally one hour (1 h) before this scheduled “start time”. Again the ETAs are displayed, and any persons “running late” are so notified if they have not previously been so notified”, ¶71, “Preferably the supplying and the calculating and the sending and the displaying are held in abeyance until a predetermined first time interval before the meeting. Then, after this predetermined first time interval before the meeting, the calculating indicates any prospective attendee of the meeting is already late for the meeting, so notifying that attendee”, ¶72).
Liu and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of providing recommendations to users based on context data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to know the current status of user to effectively provide recommendations that help the user to manage ones time and resources in the fast mobile working environment. (D1, ¶12).
With regard to Claim 15,
Liu and D1 teach the computing system of claim 14, wherein the event is an identified change to at least one of the one or more semantic entities (Abstract ¶41, ¶43, ¶¶55, “At a predetermined first time interval before a scheduled beginning of the meeting … (1) the interrogating of its then geographical location by the communications device of each prospective meeting attendee, … At this time the ETA of each prospective meeting attendee is calculated, but the time interval (the 2 h) has intentionally been chosen so that all attendees can potentially timely transit to the meeting. If any are already likely late for the meeting … then these persons are automatically messaged that their optimal time of departure (to make the meeting on time) is already past! “, ¶56, ¶60, “until some predetermined second time interval before the scheduled start of the meeting, normally one hour (1 h) before this scheduled “start time”. Again the ETAs are displayed, and any persons “running late” are so notified if they have not previously been so notified”, ¶71, “Preferably the supplying and the calculating and the sending and the displaying are held in abeyance until a predetermined first time interval before the meeting. Then, after this predetermined first time interval before the meeting, the calculating indicates any prospective attendee of the meeting is already late for the meeting, so notifying that attendee”, ¶72, Meeting and attendees are entities and change in their status trigger an update or suggested action (like notification)).
The same motivation to combine for claim 14 equally applies for current claim.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Dev [US 2018/0173706 A1].
With regard to Claim 26,
Lui teach the computing system of claim 1, wherein the operations comprise: receiving, via the user interface, an adjustment to rules controlling how the artificial intelligence system collects the context data (Col. 10, lines 32-40, “A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by the social-networking system 160 or shared with other systems”, Col. 43, lines 1-5).
Liu does not explicitly teach removing, after receiving the adjustment, data that is stored in the memory-based context storage based on the rules.
Dev teach receiving, via the user interface, an adjustment to rules controlling how the artificial intelligence system collects the context data (¶36, “tooling application 424 also allows a user to define rules that are stored in the rules repository 426 included in a rule processing module 428. The cloud platform 406 also includes an administration application 430 that allows viewing, editing, applying or revoking context profile and rules for a particular application”, ¶23, “ rules may be received at an administrator tool from a user. The received rule may be in an event-condition-action format … The format of the rule may be “upon condition-when condition-do action”, ¶25, “additional rules are received at an administrator tool from a user. The additional rules may define rules for receiving, storing, and processing context data corresponding to the defined context profiles”, ¶37, “context integration module 420 includes a broker 432 that processes the context data based on context profile, context parameters, and filters, and updates the processed context data to the context database”); and removing, after receiving the adjustment, data that is stored in the memory-based context storage based on the rules (¶25,” additional rules may also include retention rules for retaining previously stored context data and automatically deleting the previously stored context data after a pre-determined period”, ¶37, “context database 434 may also have retention rules that defines retention of historical data related to the different context parameters”, ¶26, “additional rules may also include filters that are defined to filter the context data based on pre-determined conditions. In one embodiment, the filter may be applied to context data to obtain filtered context data. The filtered context data may be provided as facts to the rule for rule execution. The filters may be applied on any of the context parameters. For example, the filter may be applied on user, device, application, date/time range, or frequency context parameter”, ¶¶27-28).
Liu and Dev are analogous art to the claimed invention because they are from a similar field of endeavor of records data from devices and generate context data, and perform actions based on the recorded context data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by Dev with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to provide more accurate and personalized recommendations to the user which will increase the user’s satisfaction by providing a system that records data from mobile devices, mobile applications and/or IoT devices, generate context data, and perform actions based on the recorded context data (Dev, ¶2). This is applying a known technique to a known device (method, or product) ready for improvement to yield predictable results and yield predictable results (MPEP 2143).
Claims 27-29 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. [US 11,715,042 B1, hereinafter Liu] in view of Lyon et al . [US 2019/0080180 A1, hereinafter Lyon].
With regard to Claim 27,
Lui teach the computing system of claim 1, comprising: a user computing device comprising a camera; wherein the context data comprises image data from the camera (Col. 1, lines 28-30, “ user input may include text (e.g., online chat), especially in an instant messaging application or other applications, voice, images, or a combination of them”, Col. 2, lines 7-9, “assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video) in stateful and multi-turn conversations to get assistance”, Col. 11, lines 32-36, Fig. 13, 1308, Col. 36, lines 12-13, “one or more images (e.g., an image of the cover page of a book)”, Col. 6, lines 58-64, “ client system 130 may include a computer system such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera”).
Lui does not teach automatically captured as the user computing device is used to perform a task.
Lyon teach a user computing device comprising a camera (¶2, “sensors can include … visible light camera devices, infrared camera devices, near-infrared camera devices, depth camera devices”, ¶19, “ A sensor 220 can include one or more of a camera device which generates images”); wherein the context data comprises image data from the camera that is automatically captured as the user computing device is used to perform a task (¶2, “Automated navigation and control systems may process data collected by the sensors in order to detect and characterize objects in the environment”, ¶16, “automated capture of image data for points of interest … Monitoring 120 may be performed based on such sensors to monitor the same or different portions of the environment”, ¶17, “sunset ocean view, a famous landmark (e.g., the Brooklyn Bridge), a passing parade, or wildlife may be points of interest which a passenger or user of vehicle 110 may wish to capture for a historical record of the trip in image data. Various kinds of machine vision, computer vision, and/or pattern recognition may be performed on the sensor data to detect points of interest automatically, without user input”, ¶¶39-40, “In response to detecting the point of interest, image data may be captured for the point of interest”).
Liu and Lyon are analogous art to the claimed invention because they are from a similar field of endeavor of recording data from devices and generating context data, and perform actions based on the recorded context data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu resulting in resolutions as disclosed by Lyon with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Liu as described above to detect and characterize objects in the environment and the collected sensor data may also be utilized to provide services (Lyon, ¶2). This is applying a known technique to a known device (method, or product) ready for improvement to yield predictable results and yield predictable results (MPEP 2143).
With regard to Claim 28,
Lui-Lyon teach the computing system of claim 27, wherein the image data describes a surrounding environment of a user (¶2, “Automated navigation and control systems may process data collected by the sensors in order to detect and characterize objects in the environment”, ¶16, “automated capture of image data for points of interest … Monitoring 120 may be performed based on such sensors to monitor the same or different portions of the environment”, ¶17, “sunset ocean view, a famous landmark (e.g., the Brooklyn Bridge), a passing parade, or wildlife may be points of interest which a passenger or user of vehicle 110 may wish to capture for a historical record of the trip in image data. Various kinds of machine vision, computer vision, and/or pattern recognition may be performed on the sensor data to detect points of interest automatically, without user input”, ¶¶39-40, “In response to detecting the point of interest, image data may be captured for the point of interest”).
With regard to Claim 29,
Lui-Lyon teach the computing system of claim 28, wherein the one or more semantic entities referenced by the context data are not specifically identified by the user (Lui, “user request may comprise “find me the nearest Walmart and direct me there”. The co-reference module 315 may interpret “there” as “the nearest Walmart”“, Col. 15, lines 15-16, “user input may comprise "book me a ride to the airport." A transportation agent may execute the task of booking the ride … the contextual information associated with the user profile may indicate that the user is in a hurry so the transportation agent may book a ride from a ride-sharing service (e.g., Uber, Lyft) for the user since it may be faster to get a car from a ride-sharing service than a taxi company”).
Response to Arguments
Applicant argue that the current amendments overcome the 35 USC 101 rejection as the amendments provide an improvement to the technology by providing a solution to a technical problem of efficiently enabling user control of operations on computing device.
Examiner respectfully disagrees the claims provide an abstract idea as analyzing and identifying contextual data to be used for providing recommendations which is mental process. The applicant further argue that the claims disclose the ability to process users requests with fewer user interactions resulting in use of less energy. Examiner respectfully disagrees the specifications and the claims does not provide any details regarding how the selection of input would save energy, the claims and specification do not even provide a specific form of interaction or selection. For example, if the user provide a voice command to activate a song or drive to specific location, this would save more energy than the process to analyze and provide a selectable recommendation. Even assuming arguendo, that the claims require a touch input, the specification does not clarify how the touch inputs energy consumption would be more than the energy required for analyzing and providing the recommendation in addition to the processing of the final touch input. The specification ¶45 set forth an improvement but in a conclusory manner that do not determine that the claims improves technology (MPEP 2106.05(a), “ if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology”).
Accordingly the additional elements is not an improvement to the function of a computer or to any other technology or technical field. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG").
Applicant argue that Liu fails to disclose “"automatically providing, via a user interface of the computing system, a selectable interface for selecting the suggested action described in the second model output, the suggested action selectable via the user interaction with the user interface to cause the suggested action to be performed" as presently recited in claim 1.
Examiner respectfully disagrees, Li teach the argued limitation See at least Col. 15, lines 32-36, “CU composer 270 may comprise a natural language generator (NLG) 271 and a user interface (UI) payload generator 272. The natural-language generator 271 may generate a communication content based on the output 35 of the dialog engine 235”, Col. 15-16, lines 65-67, “surface realization component may determine specific 65 words to use, the sequence of the sentences, and the style of the communication content”, Fig. 4-7, Fig. 8, Col. 27, lines 42-44, “extract state features {I, C} from a set of conversations 400, i.e., NLU intents I for all turns and action related features (impression 424 and click 426)”, Col. 26, lines 15-22, “one or more user actions may comprise user-interactions with one or more content objects. p is the likelihood of a user clicking on the suggestion”, Col. 27, lines 10-13, “use a reward value of +2.5 for a suggestion which is clicked, and −0.1 for a suggestion that is shown but not clicked“, Col. 28, lines 1-5, Col. 30, lines 31-34, “GBDT model seems to treat user clicking on the suggestion displayed in previous turns as an important feature that can be used to predict a click in the most recent turn”, Col. 15, lines 5-7, “user input may comprise “book me a ride to the airport”.
Applicant argue that claims 19 and 20 are allowable for the same arguments presented in claim 1.
Examiner respectfully disagrees, claims 19 and 20 are rejected based on the same reasons of rejecting claim 1.
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 2017/0147154 filed by Steiner et al. that disclose techniques for context-aware recommendations of relevant presentation content are disclosed See at least Abstract
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references 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. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148