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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. 11,935,527. Although the claims at issue are not identical, they are not patentably distinct from each other because In re Karlson, 136 USPQ 184 (1963): “Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before”.
11,935,527
19/049,549
1. A method implemented by one or more processors, the method comprising: generating a class proficiency measure that is specific to a class of interactions with an automated assistant and that is specific to a particular user, wherein generating the class proficiency measure comprises:
processing instances of data for past assistant interactions, based on the past assistant interactions being of the class and being initiated by the particular user; determining that the class proficiency measure satisfies a threshold; in response to determining that the class proficiency measure satisfies the threshold: activating, for the particular user, one or more capabilities specific to the class, wherein the one or more capabilities are, prior to the activating, dormant for the particular user and the particular assistant device, wherein the one or more capabilities specific to the class comprise one or both of: a particular intent for the class of interactions, and a particular parameter for an already activated intent for the class of interactions; in response to determining that the class proficiency measure fails to satisfy the threshold: maintaining the one or more capabilities as dormant for the particular user.
2. The method of claim 1, further comprising, in response to the activating or prior to the activating: causing a notification of the one or more capabilities to be proactively rendered at an assistant device that is associated with the particular user.
3. The method of claim 2, wherein causing the notification to be proactively rendered occurs prior to the activating, and wherein the activating is further in response to receiving affirmative user interface input responsive to the notification being rendered.
4. The method of claim 1, wherein the activating comprises: mapping, in one or more computer readable media, the one or more capabilities to a user identifier of the particular user.
5. The method of claim 4, wherein the mapping causes one or more additional models or one or more extended grammars to be utilized in assistant requests that include the user identifier.
6. The method of claim 1, wherein the one or more capabilities specific to the class comprise the particular intent for the class of interactions.
7. The method of claim 1, wherein the one or more capabilities specific to the class comprise the particular parameter for an already activated intent for the class of interactions.
8. The method of claim 1, wherein processing the instances of data for past assistant interactions, based on the past assistant interactions being of the class and being initiated by the particular user and comprises: processing the instances of data to generate individual metrics for the class; and generating the class proficiency measure as a function of the individual metrics.
9. The method of claim 8, wherein generating the class proficiency measure as the function of the individual metrics comprises processing the individual metrics utilizing a trained machine learning model to generate output, and determining the class proficiency measure based on the output.
10. The method of claim 8, wherein processing the instances of data to generate the individual metrics for the class comprises two or more of: generating a frequency of interactions metric, of the individual metrics, based on a frequency quantity of the past interactions, indicated by the data, over a given time period; generating a success ratio metric, of the individual metrics, based on comparison of: a successful quantity of the past interactions, indicated by the data, that were successful to a total quantity of the past interactions indicated by the data or an unsuccessful quantity of the past interactions, indicated by the data, that were not successful; generating an efficiency metric, of the individual metrics, based on comparison of: a particular duration measure that is based on duration of the past interactions indicated by the data to a population-based duration measure that is based on duration of other past interactions, of the class of interactions, from a population of users; generating a clarification metric, of the individual metrics, based on a clarification quantity of the past interactions, indicated by the data, in which the automated assistant had to prompt for clarification; generating a rarity metric, of the individual metrics, based on comparison of: a particular measure that is based on usage of certain intents or certain parameters in the past interactions indicated by the data and a population-based measure that is based on usage of the certain intents or certain parameters in the other past interactions, of the class of interactions, from the population of users; or generating a breadth metric, of the individual metrics, based on breadths of user commands indicated by the data.
11. A method implemented by one or more processors, the method comprising: generating, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular user, wherein generating the associated class proficiency measures comprises: processing corresponding instances of data for corresponding past assistant interactions, based on the corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular user; selecting, based on the associated class proficiency measures, one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions; assigning, in one or more computer readable media the particular user in association with the selected subset of classes of interactions; subsequent to the assigning: processing audio data that is detected via one or more microphones of the particular assistant device and that captures a spoken utterance of the particular user; determining, based on the processing, that the spoken utterance is directed to a particular class of interactions; based on the spoken utterance being provided by the particular user and/or being detected via the microphones of the particular client device, determining whether the particular class of interactions is included in the selected subset of classes of interactions; when it is determined that the particular class of interactions is not included in the selected subset of classes of interactions: prompting the particular user for verification prior to fulfilling the spoken utterance; and fulfilling the spoken utterance only when affirmative user interface input is received responsive to the prompting.
12. The method of claim 11, further comprising, in response to the selecting: causing a notification to be proactively rendered at an assistant device that is associated with the particular user, wherein the notification indicates that assistant interactions that fall outside of the selected particular class of interactions will require additional confirmation from the user prior to fulfillment.
13. The method of claim 12, wherein causing the notification to be proactively rendered occurs prior to the assigning, and wherein the assigning is further in response to receiving affirmative user interface input responsive to the notification being rendered.
14. The method of claim 11, wherein processing instances of data for past assistant interactions, based on the past assistant interactions being of the class and being initiated by the particular user comprises: processing the instances of data to generate individual metrics for the class; and generating the associated class proficiency measure as a function of the individual metrics.
15. The method of claim 14, wherein processing the instances of data to generate the individual metrics for the class comprises two or more of: generating a success ratio metric, of the individual metrics, based on comparison of: a successful quantity of the past interactions, indicated by the data, that were successful to a total quantity of the past interactions indicated by the data or an unsuccessful quantity of the past interactions, indicated by the data, that were not successful; generating an efficiency metric, of the individual metrics, based on comparison of: a particular duration measure that is based on duration of the past interactions indicated by the data to a population-based duration measure that is based on duration of other past interactions, of the class of interactions, from a population of users; or generating a clarification metric, of the individual metrics, based on a clarification quantity of the past interactions, indicated by the data, in which the automated assistant had to prompt for clarification.
1. A method implemented by one or more processors, the method comprising: generating, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein generating each of the associated class proficiency measures comprises: processing, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; selecting a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: causing the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
2. The method of claim 1, further comprising: causing, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
3. The method of claim 2, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
4. The method of claim 3, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
5. The method of claim 4, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
6. The method of claim 4, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
7. The method of claim 1, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include those utilized in one or more of speech recognition, natural language understanding, or fulfillment.
8. The method of claim 1, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein generating each of the associated class proficiency measures further comprises: processing, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
9. A system comprising: memory storing instructions; and one or more processors operable to execute the instructions to: generate, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein in generating each of the associated class proficiency measures, one or more of the processors are to: process, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; select a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: cause the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
10. The system of claim 9, wherein one or more of the processors are further operable to execute the instructions to: cause, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
11. The system of claim 10, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
12. The system of claim 11, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
13. The system of claim 12, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
14. The system of claim 12, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
15. The system of claim 9, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include those utilized in one or more of speech recognition, natural language understanding, or fulfillment.
16. The system of claim 9, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein in generating each of the associated class proficiency measures, one or more of the processors are further operable to execute the instructions to: process, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
17. A non-transitory computer readable storage medium configured to store instructions that, when executed by one or more processors, cause one or more of the processors to: generate, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein in generating each of the associated class proficiency measures, one or more of the processors are to: process, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; select a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: cause the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
18. The non-transitory computer readable storage medium of claim 17, wherein one or more of the processors are further operable to execute the instructions to: cause, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
19. The non-transitory computer readable storage medium of claim 18, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
20. The non-transitory computer readable storage medium of claim 17, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein in generating each of the associated class proficiency measures, one or more of the processors are further operable to execute the instructions to: process, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,223,960 in view of Zhu U.S. PAP 2020/0235833 A1.
Regarding claim 1 teaches “causing the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.”
Meanwhile the Patent ‘960 claims include similar recitations except for “in response to selecting the first set: causing the particular assistant device to purge, from on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions not included in the first set.”
Zhu teaches the modem termination system may request additional information from the modems or instruct the terminal devices to perform additional monitoring or analysis. In some examples, the modem termination system may send or activate additional classification models stored at the terminal devices to perform additional monitoring to aid in diagnosing network issues, see par. [0069]
12,223,960
19/049,549
1. A method implemented by one or more processors, the method comprising: generating, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein generating each of the associated class proficiency measures comprises: processing corresponding instances of data for corresponding past assistant interactions, based on the corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device; selecting a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; in response to selecting the first set: causing the particular assistant device to purge, from on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions not included in the first set.
2. The method of claim 1, further comprising: causing, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
3. The method of claim 2, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
4. The method of claim 3, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
5. The method of claim 4, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
6. The method of claim 4, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
7. The method of claim 1, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include those utilized in one or more of speech recognition, natural language understanding, or fulfillment.
8. The method of claim 1, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein generating each of the associated class proficiency measures further comprises: processing the corresponding instances of data for the corresponding past assistant interactions, based on the corresponding past assistant interactions being initiated by the particular user.
9. A method implemented by one or more processors, the method comprising: generating a class proficiency measure that is specific to a class of interactions with an automated assistant and that is specific to one or both of: (i) a particular user, and (ii) a particular assistant device, wherein generating the class proficiency measure comprises: processing instances of data for past assistant interactions to generate individual metrics for the class, wherein processing the instances of data for the past assistant interactions is based on the past assistant interactions being of the class and being initiated by one or both of (i) the particular user and (ii) the particular assistant device, and generating the class proficiency measure as a function of the individual metrics; assigning, in one or more computer readable media, one or both of (i) the particular user and (ii) the particular assistant device in association with a selected subset of classes of interactions; subsequent to the assigning: processing audio data that is detected via one or more microphones of the particular assistant device and that captures a spoken utterance of the particular user; determining, based on the processing, that the spoken utterance is directed to the class of interactions; and in response to the spoken utterance being directed to the class of interactions, performing one or both of: providing a shortened response to the spoken utterance in lieu of a longer response, and bypassing providing a confirmation message before fulfilling a request embodied in the spoken utterance.
10. The method of claim 9, wherein providing the shortened response to the spoken utterance, in lieu of the longer response, is performed in response to the spoken utterance being directed to the class of interactions.
11. The method of claim 10, wherein bypassing providing the confirmation message before fulfilling the request is also performed in response to the spoken utterance being directed to the class of interactions.
12. The method of claim 9, wherein bypassing providing the confirmation message before fulfilling the request is performed in response to the spoken utterance being directed to the class of interactions.
13. The method of claim 9, wherein the class proficiency measure is specific to both of (i) the particular user, and (ii) the particular assistant device.
14. An assistant device, comprising: one or more microphones; one or more processors operable to execute instructions to: generate, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to the assistant device, wherein in generating each of the associated class proficiency measures one or more of the processors are to: process corresponding instances of data for corresponding past assistant interactions, based on the corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the assistant device; select a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; in response to selecting the first set: purge, from on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions not included in the first set.
15. The assistant device of claim 14, wherein one or more of the processors are further operable to execute the instructions to: in response to selecting the first set, download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
16. The assistant device of claim 15, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
17. The assistant device of claim 16, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
18. The assistant device of claim 17, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
19. The assistant device of claim 17, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
20. The assistant device of claim 14, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein in generating each of the associated class proficiency measures one or more of the processors are further to: process the corresponding instances of data for the corresponding past assistant interactions, based on the corresponding past assistant interactions being initiated by the particular user.
1. A method implemented by one or more processors, the method comprising: generating, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein generating each of the associated class proficiency measures comprises: processing, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; selecting a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: causing the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
2. The method of claim 1, further comprising: causing, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
3. The method of claim 2, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
4. The method of claim 3, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
5. The method of claim 4, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
6. The method of claim 4, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
7. The method of claim 1, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include those utilized in one or more of speech recognition, natural language understanding, or fulfillment.
8. The method of claim 1, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein generating each of the associated class proficiency measures further comprises: processing, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
9. A system comprising: memory storing instructions; and one or more processors operable to execute the instructions to: generate, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein in generating each of the associated class proficiency measures, one or more of the processors are to: process, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; select a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: cause the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
10. The system of claim 9, wherein one or more of the processors are further operable to execute the instructions to: cause, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
11. The system of claim 10, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
12. The system of claim 11, wherein the one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset, include one or more second grammars utilized in one or both of speech recognition and natural language processing.
13. The system of claim 12, wherein the one or more first grammars are utilized in biasing of speech recognition and wherein the one or more second grammars are also utilized in biasing of speech recognition.
14. The system of claim 12, wherein the one or more first grammars define first intents and first parameters for the first intents and wherein the one or more second grammars define second intents and second parameters for the second intents.
15. The system of claim 9, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include those utilized in one or more of speech recognition, natural language understanding, or fulfillment.
16. The system of claim 9, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein in generating each of the associated class proficiency measures, one or more of the processors are further operable to execute the instructions to: process, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
17. A non-transitory computer readable storage medium configured to store instructions that, when executed by one or more processors, cause one or more of the processors to: generate, for each of a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant device, wherein in generating each of the associated class proficiency measures, one or more of the processors are to: process, based on corresponding past assistant interactions being for a corresponding one of the disparate classes of interactions and being initiated by the particular assistant device, corresponding instances of data for the corresponding past assistant interactions; select a first set of one or more of the disparate classes of interactions for inclusion in a subset of classes of interactions based on their associated class proficiency measures satisfying a threshold; and in response to selecting the first set: cause the particular assistant device to store, in on-device storage of the particular assistant device, one or more machine learning models or grammars that are specific to any of the disparate classes of interactions that are included in the first set.
18. The non-transitory computer readable storage medium of claim 17, wherein one or more of the processors are further operable to execute the instructions to: cause, in response to selecting the first set, the particular assistant device to download and store, in the on-device storage, one or more other machine learning models or other grammars that are specific to at least some of the one or more disparate classes of interactions included in the subset.
19. The non-transitory computer readable storage medium of claim 18, wherein the one or more machine learning models or grammars, that are specific to any of the disparate classes of interactions not included in the first set, include one or more first grammars utilized in one or both of speech recognition and natural language processing.
20. The non-transitory computer readable storage medium of claim 17, wherein the associated class proficiency measures that are generated are further specific to a particular user of the particular assistant device, and wherein in generating each of the associated class proficiency measures, one or more of the processors are further operable to execute the instructions to: process, based on the corresponding past assistant interactions being initiated by the particular user, the corresponding instances of data for the corresponding past assistant interactions.
Allowable Subject Matter
Claims 1-20 are allowed.
Regarding Independent claims 1 and 14, the cited art to Novitchenko do not anticipate at least the amended version of claim 1. For example, Novitchenko's "electronic device" that "determines that the suggestion criteria are satisfied" fails to teach or suggest "generating, for a plurality of disparate classes of interactions with an automated assistant, an associated class proficiency measure that is specific to a particular assistant … Also causing the particular assistant device to purge, from on-device storage of the particular assistant device one or more machine learning models or grammars that are specific to any of the disparate classes of interactions not included in the first set” Novitchenko's "context data" that "can indicate... a number of times the user has previously performed a particular task without using the digital assistant" fails to teach or suggest "maintaining the one or more capabilities as dormant for the particular user" responsive to "determining that the class proficiency measure fails to satisfy the threshold". A new search was performed and no art was found which teaches a proficiency measure that is based on a particular user where the capabilities are formant if it falls below a threshold.
The dependent claims 2-8, 15-20 further narrow the scope of the allowable parent claim and are therefore allowable for the same reasons.
With regards to independent claims 9 Novitchenko fail to teach " generating a class proficiency measure that is specific to a class of interactions with an automated assistant and that is specific to one or both of: (i) a particular user, and (ii) a particular assistant device". Brown ‘996 also fails to teach or suggest at least the above feature. For example, Brown's "virtual assistant" that "may prompt the user for input regarding what task the user is requesting to be performed" fails to teach or suggest "generating a class proficiency method". Brown's "virtual assistant" that "may update the task map" fails to teach or suggest "uses a user’s rating of an action by the virtual assistant, and the comments in the comment section, see par. [0124]." In contrast the applicants invention generates a proficiency measure using an engine not a user provided rating (see par. [0022]. And the proficiency ratings are with respect to classes of interactions not particular interactions like in Brown. For these reasons and because the claims are directed to similar subject matter to parent application U.S. Patent No.11.935.527 which are very similar in scope the claims are found to be allowable.
Dependent claims 10-13 further narrow the scope of the allowable independent claims and are allowed for the same reasons.
A new search was made and no art was found which teaches the above mentioned limitations.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pertinent prior art available on form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Ortiz-Sanchez whose telephone number is (571)270-3711. The examiner can normally be reached Monday- Friday 9AM-6PM.
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, Bhavesh Mehta can be reached at 571-272-7453. 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.
/MICHAEL ORTIZ-SANCHEZ/Primary Examiner, Art Unit 2656
