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 statement (IDS) submitted on 6/10/25 and 8/11/26 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 2, 7, 8, 9, 14, 15, 16 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2016/0352656 by Galley et al. (Galley).
With respect to claim 1, Galley teaches a method comprising:
detecting, at a workplace assistant client application on a client device (Paragraph 48 – functionalities implemented via application program), at least one received message associated with a remote user from among a plurality of remote users; (Paragraph 77, 81, 1 – message received from a user of plural users)
accessing a plurality of channels communicatively coupled to the workplace assistant client application; (Paragraph 86, 89-94, 122, 128 - channels include linguistic data from any source of conversational data, and non-linguistic data from sensor data and one or more databases of various types of user data)
submitting the at least one received message and additional information from the plurality of channels to predictive models to provide a multichannel context for the at least one received message; (Paragraph 122-124, Fig. 5 – the message, linguistic data and non-linguistic data (additional information from channels) is provided to a machine learning model to generate a context-sensitive response)
and generating, using output from the predictive models, a response message based at least in part on the multichannel context of the received message. (Paragraph 122-124, 132, Fig. 7 – the message, linguistic data and non-linguistic data (additional information from channels) is provided to a machine learning model to generate a context-sensitive response, set of example responses show in fig. 7)
With respect to claim 2, Galley teaches the method of claim 1, further comprising displaying, on the client device, the response message with a context panel indicating at least a portion of the multichannel context. (See Fig. 7 – 702 context, 706 response)
With respect to claim 7, Galley teaches the method of claim 1, further comprising: accessing metadata including at least one of relationship, presence, relevance, group membership, active channels, mentions, or previous interactions; and generating the response message based at least in part based on the metadata. (Paragraph 84, 86, 89-94, 122, 128).
Claims 8, 9, 14, 15, 16 and 20 are similar in scope to claims 1, 2 and 7 and are rejected based on 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) 3-4, 10, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galley in view of US 2025/0131278 by Millen et al. (Millen).
With respect to claim 3, Galley teaches the method of claim 1, wherein the predictive models further comprise an intent detection model (Fig. 6, 7 – model detects intent as expressed by context that correspond to messages and responses). While Galley teaches general usage of machine learning models, Galley does not explicitly disclose a large language model and an urgency model.
Millen teaches using various machine learning models to assist in generating actions such as generating message data. This includes the use of an LLM and urgency models. (See Paragraphs 20, 22, 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the machine learning models of Galley include LLM and urgency models as in Millen. Using known machine learning models for the predictable result of assisting in generating message responses as desired in Galley would have been obvious.
With respect to claim 4, Galley as modifed teaches the method of claim 3, further comprising submitting the at least one received message and additional information to at least one of the large language model, the intent detection model, or the urgency model stored on the client device. (Based on the combination of claim 3, the message and additional information would be sent to the combined machine learning models as seen in Fig. 5 of Galley).
Claims 10, 11 are similar in scope to claims 3 and 4 and are rejected based on the same rationale.
Claim(s) 5, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galley and Millen and in further view of US 2024/0184566 by Gabel et al. (Gabel).
With respect to claim 5, Galley as modified teaches the method of claim 4, wherein at least one of the large language model, the intent detection model, or the urgency model is trained at least in part using generic data and at least in part using user data stored in association with the workplace assistant client application. (Galley Paragraph 86, 89-94, 122, 128 - channels include linguistic data from any source of conversational data, and non-linguistic data from sensor data and one or more databases of various types of user data). While Galley implies the security of collected user data (Paragraph 189 – consent to collect user data), Galley does not explicitly disclose using secured user data.
Gabel teaches learning models may be trained in part using generic data and in part using secured user data (Paragraphs 61-62 – large scale learning using general data combined with training on user data that is possibly private/confidential).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the training in Galley include both generic and secured data as in Gabel. One would be motivated to have this as it is beneficial to train with both types of data (Gable Paragraph 61).
Claim 12 is similar in scope to claim 5 and is rejected based on the same rationale.
Claim(s) 6, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galley in view of US 2023/0126090 by Nieuwegiessen (Nieuwegiessen).
With respect to claim 6, Galley teaches the method of claim 1, and discloses wherein the plurality of channels include conversational data sources including social media sources and other sources. (Paragraph 37-40, 64 – galley’s primary example of sources of conversational data is social media but also indicates generically one or more sources of conversational data).
While it is implied that “one or more sources of conversational data” may include sources such as email, chat and teleconferencing, such sources are not explicitly stated by Galley. Nieuwegiessen explicitly teaches a response generating system that includes email, chat and teleconferencing channels that can provide message data (Paragraph 2, 24-27, 29-31, Fig. 1 – any disparate electronic communication channel with data types including text, video and audio)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the conversational data sources of Galley include email, chat and teleconferencing as taught in Nieuwegiessen. Using known sources of conversational data to provide conversational data as desired in Galley for generating responses would have been obvious.
Claim 13 is similar in scope to claim 6 and is rejected based on the same rationale.
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galley in view of Millen and in view of Nieuwegiessen.
With respect to claim 17, Galley teaches the non-transitory computer-readable medium of claim 15, wherein the predictive models further comprise an intent detection model (Fig. 6, 7 – model detects intent as expressed by context that correspond to messages and responses). While Galley teaches general usage of machine learning models, Galley does not explicitly disclose a large language model and an urgency model.
Millen teaches using various machine learning models to assist in generating actions such as generating message data. This includes the use of an LLM and urgency models. (See Paragraphs 20, 22, 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the machine learning models of Galley include LLM and urgency models as in Millen. Using known machine learning models for the predictable result of assisting in generating message responses as desired in Galley would have been obvious.
Galley further teaches wherein the plurality of channels include conversational data sources including social media sources and other sources (Paragraph 37-40, 64 – galley’s primary example of sources of conversational data is social media but also indicates generically one or more sources of conversational data).
While it is implied that “one or more sources of conversational data” may include sources such as email, chat and teleconferencing, such sources are not explicitly stated by Galley. Nieuwegiessen explicitly teaches a response generating system that includes email, chat and teleconferencing channels that can provide message data (Paragraph 2, 24-27, 29-31, Fig. 1 – any disparate electronic communication channel with data types including text, video and audio)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the conversational data sources of Galley include email, chat and teleconferencing as taught in Nieuwegiessen. Using known sources of conversational data to provide conversational data as desired in Galley for generating responses would have been obvious.
With respect to claim 18, Galley as modified teaches the non-transitory computer-readable medium of claim 17, wherein the code is executable for causing the processor to submit the at least one received message and additional information to at least one of the large language model, the intent detection model, or the urgency model stored on the client device. (Based on the combination of claim 17, the message and additional information would be sent to the combined machine learning models as seen in Fig. 5 of Galley).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galley, Millen, and Nieuwegiessen as applied to claim 18 above, and further in view of Gabel.
With respect to claim 19, Galley as modified teaches the non-transitory computer-readable medium of claim 18 wherein the code is executable for causing the processor to train at least one of the large language model, the intent detection model, or the urgency model at least in part using generic data and user data stored in association with the workplace assistant client application. (Galley Paragraph 86, 89-94, 122, 128 - channels include linguistic data from any source of conversational data, and non-linguistic data from sensor data and one or more databases of various types of user data). While Galley implies the security of collected user data (Paragraph 189 – consent to collect user data), Galley does not explicitly disclose using secured user data.
Gabel teaches learning models may be trained in part using generic data and in part using secured user data (Paragraphs 61-62 – large scale learning using general data combined with training on user data that is possibly private/confidential).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the training in Galley include both generic and secured data as in Gabel. One would be motivated to have this as it is beneficial to train with both types of data (Gable Paragraph 61).
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 1-20 of U.S. Patent No. 12,348,476. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claims 1-20 of U.S. patent 12,348,476 contain every element of claims 1-20 of the instant application and thus anticipate the claims of the instant application. Claims 1-20 of the instant application therefore are not patently distinct from the earlier patent claims and as such are unpatentable over obvious-type double patenting. A later patent/application claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim. "A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim." In re Lonqi, 759 F.2d at 896, 225 USPQ at 651.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R LAZARO whose telephone number is (571)272-3986. The examiner can normally be reached M-F 8-4:30.
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, Emmanuel Moise can be reached at 571-272-3865. 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.
/DAVID R LAZARO/Primary Examiner, Art Unit 2455
Prosecution Insights