DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) were filed on 11/6/2023, 3/18/2024, 11/21/2024, and 7/28/2025. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it uses the term “disclosure.” A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Drawings
The drawings filed 10/17/2023 were accepted.
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.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the user focus". There is insufficient antecedent basis for this limitation in the claim. (Note: user focus is used in claim 2, but since claim 3 depends on claim 1, it still lacks antecedent basis)
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 12-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. (Note: This rejection is in regards to an interpretation regarding transitory signals)
The claims do not fall within at least one of the four categories of patent eligible subject matter because the claim falls outside the scope of patent-eligible subject matter at least because the claimed "computer-readable storage medium" in light of the supporting disclosure is broad enough to encompass transitory embodiments. The specification does not define the “computer-readable storage medium.” A reasonable interpretation of the "computer-readable storage medium" includes a signal. See MPEP 2106(I). Examiner suggests using "non-transitory computer-readable storage medium" to overcome the rejection.
Non-limiting examples of claims that are not directed to one of the statutory categories: transitory forms of signal transmission (for example, a propagating electrical or electromagnetic signal per se), In re Nuijten, 500 F.3d 1346, 1357, 84 USPQ2d 1495, (Fed.Cir. 2007).
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim 1 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 because they are directed to an abstract idea without significantly more. The claims recite the abstract idea of selecting an object, determining user intentions, obtaining an object, applying an analytical model, and generating an input.
Step 2A, Prong 1
The limitations that describe the selecting an object, determining user intentions, obtaining an object, applying an analytical model, and generating an input are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claims also include elements of receiving voice input, accessing stored history, accessing a set of input types, applying a machine learning model, providing input, dynamically generating or altering virtual object, however nothing in the claims precludes the steps from practically being performed in the mind.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application because the additional elements regarding receiving voice input, accessing stored history, accessing a set of input types, applying a machine learning model, providing input, dynamically generating or altering virtual object are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the extrasolutionary elements are not considered significantly more than just applying the steps of selecting an object, determining user intentions, obtaining an object, applying an analytical model, and generating an input.
Step 2B
In addition to the abstract idea, the claims have the receiving voice input, accessing stored history, accessing a set of input types, applying a machine learning model, providing input, dynamically generating or altering virtual object, but they represent only well-understood, routine, conventional activity that can be performed on generic computers. The sending/receiving of data (receiving voice, accessing data, providing inputs) has been recognized by the courts as being well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) and MPEP 2106.05(d), subsection II.
Mairesse et al (US 9484021 B1; filed 3/30/2015) discloses how well-understood, routine, and conventional applying a machine learning model is: paragraph 49: “Various machine learning techniques may be used to train and/or operate the first model and second model. In machine learning techniques an adaptive system is “trained” by repeatedly providing it examples of data and how the data should be processed using an adaptive model until it can consistently identify how a new example of the data should be processed, even if the new example is different from the examples included in the training set from which it learned.”
Powderly et al (US20180307303A1; filed 4/17/2018) discloses how well-understood, routine, and conventional dynamically generating or altering a virtual object is: paragraph 3: “Modern computing and display technologies have facilitated the development of systems for so called “virtual reality”, “augmented reality”, or “mixed reality” experiences… new environments where physical and virtual objects co-exist and interact in real time;” paragraph 52: “an interaction event, such as e.g., a task for selecting, moving, resizing or targeting a virtual object.”
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 2, this claim recites an additional abstract idea of identifying (a direction or proximity of user focus), obtaining the target virtual object, and applying the analytical model. The identifying (a direction or proximity), obtaining the target virtual object, and applying the analytical model are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
As per claim 3, this claim has similar identifying of user focus and is rejected similarly to claim 2.
As per claim 4, this claim has similar dynamically controlling a virtual object and is rejected similarly to claim 1. Claim 4 also recites an additional element of executing functions of applications.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding executing functions of applications are considered merely applying the steps of processing the input and dynamically controlling the target virtual object. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the executing functions of applications are not considered significantly more than the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 5, this claim recites an additional abstract idea of securing input and enforcing protocol. The securing input and enforcing protocol is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
As per claim 6, this claim has similar generating and is rejected similarly to claim 1.
As per claim 7, this claim has similar generating (of indicators) and is rejected similarly to claim 1.
As per claim 8, this claim has similar generating (of indicators) and is rejected similarly to claim 1.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 9, this claim recites an additional abstract idea of registering. The registering is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. There are no other additional elements.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 10 also recites an additional element of prompting a user and augmenting/controlling the virtual object.
(Step 2A, prong 2) The judicial exception is not integrated into a practical application because the additional elements regarding prompting a user and augmenting/controlling the virtual object are considered insignificant extra-solution activity. These limitations are not considered improvements to the functioning of a technology or technical field.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the prompting a user and augmenting/controlling the virtual object are not considered significantly more than the judicial exception. The additional elements represent only well-understood, routine, conventional activity that can be performed on generic computer systems. Powderly et al (US20180307303A1; filed 4/17/2018) discloses how well-understood, routine, and conventional dynamically augmenting a virtual object is: paragraph 3: “Modern computing and display technologies have facilitated the development of systems for so called “virtual reality”, “augmented reality”, or “mixed reality” experiences… new environments where physical and virtual objects co-exist and interact in real time;” paragraph 52: “an interaction event, such as e.g., a task for selecting, moving, resizing or targeting a virtual object.” Powderly et al also discloses how well-understood, routine, and conventional prompting a user is: paragraph 226: “the wearable system can automatically provide a disambiguation prompt to the user.” The claims are not patent eligible.
As per claim 11, this claim has similar applying of a machine learning model and is rejected similarly to claim 1.
Claim 12 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claims 13-17 recite substantially similar limitations to claims 4-8 respectively and are thus rejected along the same rationales.
Claim 18 recites substantially similar limitations to claim 11 and is thus rejected along the same rationale.
Claim 19 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claim 20 recites substantially similar limitations to claim 4 and is thus rejected along the same rationale.
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)(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.
Claim(s) 1-10, 12-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Powderly et al (US20180307303A1; filed 4/17/2018).
With regards to claim 1, Powderly et al discloses a method for applying voice controls to a target virtual object in an artificial reality (XR) environment (Powderly et al, abstract: “use multiple inputs (e.g., gesture, head pose, eye gaze, voice, and/or environmental factors (e.g., location)) to determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on.”), the method comprising:
receiving voice input (Powderly et al, abstract: “use multiple inputs (e.g., gesture, head pose, eye gaze, voice…”);
selecting, for the voice input using a disambiguation layer, the target virtual object from among a plurality of potential virtual objects (Powderly et al, paragraph 156: “the voice command 1442 by itself (without indications from the user input device 466 or the cone cast 143) may cause confusion to the wearable system, because the wearable system may not know which object is associated with the word “that”;” paragraph 159: “The system can select, as the target object, the particular object in the environment that has the highest confidence score”) by:
determining, using the voice input, one or more user intentions (Powderly et al, paragraph 52: “For example, when a user says “move that there”, the wearable system can use head pose, eye gaze, hand gestures, along with other environmental factors (e.g., the user's location or the location of objects around the user), in combination with the voice command to determine which object should be moved (e.g., which object is “that”) and which destination is intended (e.g., “there”) in response to these multimodal inputs;” the “intentions” are interpreted as the commands interpreted from the user’s voice input) from a predefined set of user intentions (Powderly et al, paragraph 236: “In certain implementations, these pre-programmed words can also be referred to as “hotwords” or “carrier phrases,” which the system recognizes as indicating the user wants to take a particular action (e.g., “Call”) and which may alert the system to accept further input to complete the desired action”);
accessing a stored user interaction history with the one or more virtual objects (Powderly et al, Paragraph 192: "Besides the techniques described with reference to FIGS. 18A and 18B, the wearable system can assign confidence score to a virtual object based on historical analysis of the user's interactions. As an example, the wearable system can assign a higher confidence score to a virtual object with which the user frequently interacts."); and
obtaining the target virtual object as a highest ranked virtual object (Powderly et al, paragraph 159: “The system can select, as the target object, the particular object in the environment that has the highest confidence score.”) from among the potential virtual objects by applying an analytical model (Powderly et al, paragraph 188: “Multiple confidence scores can be calculated (for some or all of the various multimodal inputs) and then aggregated to determine a user interface operation or a target virtual object based on multimodal user inputs;” the “analytical model” is interpreted as the model that calculates the confidence scores for each virtual object) to the one or more user intentions and the user interaction history (Powderly et al, paragraph 192: “assign confidence score to a virtual object based on historical analysis of the user's interactions… As another example, one user may tend to move virtual objects using voice commands (e.g., “move that there”), whereas another user may prefer to use hand gestures (e.g., by reaching out and “grabbing” a virtual object and moving it to another position). The system can determine such user tendencies from the historical analysis. The system can determine such user tendencies from the historical analysis”);
accessing a set of candidate input types predefined for the target virtual object, wherein each candidate input type comprises a predefined structure (Powderly et al, paragraph 297: “A term like “edit” is sometimes referred to as a “hotword” or “carrier phrase,” and the system may include a number of pre-programmed (and optionally, user-settable) hotwords such as (in the editing context): edit, cut, copy, paste, bold, italic, delete, move, etc;” The pre-programmed hotwords are interpreted as a set of input types, which are programmed for a specific context. The target virtual object in this example would be the text being edited in the editing context; regarding the “predefined structure,” something like a string from the voice input could be interpreted as the structure – paragraph 50: “A voice recognition application can create an executable data string based on a user's voice as a direct input.” Paragraph 282 further describes how the strings are processed (e.g. parsing, processed with punctuation, and tokenizing);
applying a machine learning model (Powderly et al, Paragraph 169: "The central runtime server 1650 can identify the content of the speech by applying various speech recognition algorithms, such as, e.g., … machine learning algorithms (described with reference to FIGS. 7 and 9)"), to the set of candidate input types and 1) one or more portions of the voice input, and/or 2) the one or more user intentions, to select one of the candidate input types (Powderly et al, paragraph 297: “For example, within the context of speech input, the system might consult a limited command-specific vocabulary of terms to perform speech recognition on subsequently-received speech input.” The input types are interpreted as the commands);
generating, by the disambiguation layer using the voice input and determined user intentions, target virtual object input according to the predefined structure for the selected one of the candidate input types (Powderly et al, abstract: “determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on;” the use of the voice input and user intentions was previously cited from paragraph 52 above to determine the commands); and
providing, for the target virtual object, the target virtual object input such that a display of the target virtual object is dynamically generated and/or an existing display of the target virtual object is dynamically altered (Powderly et al, paragraph 52: “execution of an interaction event, such as e.g., a task for selecting, moving, resizing or targeting a virtual object”).
With regards to claim 2, which depends on claim 1, Powderly et al discloses identifying A) a direction for user focus (Powderly et al, paragraph 188: “FIGS. 18A and 18B illustrate examples of calculating confidence scores for objects within a user's FOV.”) and/or B) one or more proximity measures to one or more displayed virtual objects with respect to the user focus (Powderly et al, paragraph 216: “The wearable system can determine a subject of user's interactions as a function of the object's proximity to the user. FIG. 21 illustrates an example of identifying a target virtual object based on objects' locations.”), wherein the target virtual object is obtained as the highest ranked virtual object from among the potential virtual objects by applying the analytical model (Powderly et al, paragraph 159: “The system can select, as the target object, the particular object in the environment that has the highest confidence score.”) to 1) the one or more user intentions, 2) the user interaction history (Powderly et al, paragraph 192: “assign confidence score to a virtual object based on historical analysis of the user's interactions… As another example, one user may tend to move virtual objects using voice commands (e.g., “move that there”), whereas another user may prefer to use hand gestures (e.g., by reaching out and “grabbing” a virtual object and moving it to another position). The system can determine such user tendencies from the historical analysis. The system can determine such user tendencies from the historical analysis”), and 3) the direction for the user focus and/or the one or more proximity measures (Powderly et al, paragraph 188: “FIGS. 18A and 18B illustrate examples of calculating confidence scores for objects within a user's FOV.”).
With regards to claim 3, which depends on claim 1, Powderly et al discloses wherein the user focus comprises an input metric based on tracked user gaze and/or a ray projection via tracked user body movement (Powderly et al, paragraph 176: “Also, the system may assign confidence scores to objects in the user's environment, which may be the FOR, FOV, or field of fixation (see, e.g., FIG. 12A), depending on the context and the goals of the system at that point in time… The system may analyze (and, e.g., provide confidence scores) for just the movie selections in the user's field of fixation (based, e.g., on the user's eye gaze)”).
With regards to claim 4, which depends on claim 1, Powderly et al discloses dynamically controlling the target virtual object via executing one or more functions of one or more applications that manage the target virtual object (Powderly et al, Paragraph 52: "The interaction event can include causing an application associated with the virtual object to execute (e.g., if the target object is a media player, the interaction event can comprise causing the media player to play a song or video). Selecting the target virtual object can comprise executing an application associated with the target virtual object"),
wherein the one or more applications process the target virtual object input and dynamically control the target virtual object by: dynamically generating the display of the target virtual object and/or dynamically altering the existing display of the target virtual object (Powderly et al, paragraph 52: “execution of an interaction event, such as e.g., a task for selecting, moving, resizing or targeting a virtual object”).
With regards to claim 5, which depends on claim 4, Powderly et al discloses wherein the disambiguation layer secures the voice input from the one or more applications that manage the target virtual object to enforce a privacy protocol with respect to a user (Powderly et al, paragraph 229: “In some implementations, the input can be analyzed to determine if the input originated from the user. For example, the system can apply speaker recognition techniques to determine whether the command “take a picture” was said by the user A or the hijacker B.” paragraph 230: “Additionally or alternatively, to prevent security breaches and interruptions of a user's interactions with the wearable system, the wearable system can automatically set available direct input modes based on indirect inputs or require multiple modes of direct inputs before a command is issued.” Paragraph 234: “As another example, the wearable system may disable the voice command when the user is in a public park, thereby providing privacy to the user's interaction.” There are multiple ways to interpret “secures the voice input” without additional details. Any of the 3 provided citations should be sufficient for the rejection).
With regards to claim 6, which depends on claim 5, Powderly et al discloses generating, by the disambiguation layer using the voice input and the determined user intentions, one or more indicators that comprise the target virtual object input (Powderly et al, paragraph 50: “A voice recognition application can create an executable data string based on a user's voice as a direct input.”), wherein the one or more indicators comprise a transcript of at least a portion of the voice input (Powderly et al, paragraph 386: “receive, from the audio sensing device, speech data encoding an utterance of one or more words spoken by the user; obtain a transcription for the one or more words spoken by the user based at least on the received speech data; control the display to present a string of textual characters representative of the obtained transcription to the user”).
With regards to claim 7, which depends on claim 6, Powderly et al discloses wherein the one or more indicators are generated in accordance with the predefined structure for the selected one of the candidate input types (As noted in the rejection to claim 1, the predefined structure for the input is interpreted as a string; Powderly et al, paragraph 50: “A voice recognition application can create an executable data string based on a user's voice as a direct input.”).
With regards to claim 8, which depends on claim 6, Powderly et al discloses wherein the one or more indicators comprise at least one of the determined user intentions (Powderly et al, paragraph 52: “For example, when a user says “move that there”, the wearable system can use head pose, eye gaze, hand gestures, along with other environmental factors (e.g., the user's location or the location of objects around the user), in combination with the voice command to determine which object should be moved (e.g., which object is “that”) and which destination is intended (e.g., “there”) in response to these multimodal inputs;” the “intentions” are interpreted as the commands interpreted from the user’s voice input, and the commands are included in the “executable data string based on a user’s voice as direct input” (Powderly et al, paragraph 50)).
With regards to claim 9, which depends on claim 4, Powderly et al discloses wherein the set of candidate input types and their predefined structures are registered with the disambiguation layer by the one or more applications that manage the target virtual object (Powderly et al, paragraph 52: “The interaction event can include causing an application associated with the virtual object to execute (e.g., if the target object is a media player, the interaction event can comprise causing the media player to play a song or video). Selecting the target virtual object can comprise executing an application associated with the target virtual object”).
With regards to claim 10, which depends on claim 4, Powderly et al discloses in response to feedback from the one or more applications, prompting a user for additional voice input; and augmenting the target virtual object input using additional voice input received from the user, wherein the one or more applications process the augmented target virtual object input to dynamically control the target virtual object (Powderly et al, paragraph 268: “The wearable system can open a contacts dialogue with a prompt such as, e.g., “Who?” The user can try again with voice to specify;” paragraph 226: “the wearable system can automatically provide a disambiguation prompt to the user. The disambiguation prompt may request the user to select the desired task from: the interpretation of the primary input or alternative options based on the interpretation of the secondary input.”).
Claim 12 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claims 13-17 recite substantially similar limitations to claims 4-8 respectively and are thus rejected along the same rationales.
Claim 19 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claim 20 recites substantially similar limitations to claim 4 and is thus rejected along the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Powderly et al in view of Mairesse et al (US 9484021 B1; filed 3/30/2015).
With regards to claim 11, which depends on claim 1, Powderly et al discloses wherein the analytical model… and the target virtual object is obtained as the highest ranked virtual object from among the potential virtual objects… to 1) the one or more user intentions, 2) the user interaction history, and 3) the voice input or a transcript of the voice input (Powderly et al, paragraph 192: “assign confidence score to a virtual object based on historical analysis of the user's interactions… As another example, one user may tend to move virtual objects using voice commands (e.g., “move that there”), whereas another user may prefer to use hand gestures (e.g., by reaching out and “grabbing” a virtual object and moving it to another position). The system can determine such user tendencies from the historical analysis. The system can determine such user tendencies from the historical analysis”).
However, Powderly et al does not disclose the analytical model comprises another trained machine learning model… obtained… by applying the another trained machine learning model.
Mairesse et al teaches the analytical model comprises another trained machine learning model… obtained… by applying the another trained machine learning model (Mairesse et al, paragraph 48: “Each hypothesis may also be associated with a score. These hypotheses and respective scores are processed using a first trained machine learning model to determine (404) whether one hypothesis is sufficiently correct (i.e., its score is sufficiently high relative to the remaining hypotheses) that it should be executed, or whether further disambiguation is necessary to determine the correct hypothesis”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Powderly et al and Mairesse et al such that analytical model uses a machine learning model to determine the highest ranked object. Machine learning techniques allow a system to be trained to process new data based on the training process (Mairesse et al, paragraph 49: “Various machine learning techniques may be used to train and/or operate the first model and second model… provided data with consistent patterns, recognizing such patterns when presented with new and different data is within the capacity of today's systems”).
Claim 18 recites substantially similar limitations to claim 11 and is thus rejected along the same rationale.
Conclusion
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/B.C.A/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178