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 .
Status of Claims
The office action is being examined in response to the application filed by the Applicant on March 17, 2026.
Claims 1, 3, 19 and 20 have been amended and are hereby entered.
Claims 2, 8 and 12 - 13 were cancelled.
Claims 1, 3 - 7, 9 - 11 and 14 - 22 are pending and have been examined.
This action is made FINAL.
The Examiner would like to note that this application is now being handled by Examiner Ivonnemary Rivera González and the analysis provided by the previous Examiner have been incorporated and is maintained herein.
Response to Arguments
Applicant's arguments filed March 17, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments for Double-Patenting Rejection: Applicant did not take any action and/or did not file an Electronic-Terminal Disclosure or e-td to obviate the Obviousness-type Double Patenting (ODP) rejection. Applicant’s arguments have been considered, but are not persuasive to overcome the provisional ODP rejection. Thus, the outstanding ODP was updated according to the Applicant amendments and will be maintained.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 10-16: Applicant’s arguments directed to Step 2A prong 1 and Step 2A prong 2/ 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 2 and Step 2B starting in p. 13: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that the features in the new amended limitations in view of the specifications, “improve the accuracy and functioning of an embedding function by accounting for linguistic differences between group-based communication messages and patent applications and thus present an improvement to the technical field of machine learning.”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because these claim limitations for “receiving” user feedback and “updating” the embedding function used by the computer to map new messages to a second embedding that is within a third threshold distance of a patent application embedding, while considering the claims individually and as whole, are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f) and 2106.04(d)(I)). Further, such improvement is not reflected in the computer functioning, rather on the “embedding function” used by the computer (as asserted by the Applicant in p. 14 from Remarks) which is improving the abstract idea itself (i.e. mathematical calculations; see MPEP 2106.04(a)(2)(I)) for training the ML algorithm using stored information related to conversations and patent documents to determine patentable concepts. Finally, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept” (see MPEP 2106.05(f)(2); TLC communications).
Thus, in view to the reasons previously stated which further apply in Step 2B and in response to Applicant’s arguments in pp. 14 – 16 from Remarks, these limitations and their additional elements, individually and in combination, are not “significantly more” as these are recited in a high level of generality that cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application (see MPEP 2106.05), regardless of Applicant’s assertion that the claimed invention and its new additional elements for updating the “embedding function” used by the computer with receiving user feedback are “more than "well-understood, routine, conventional activity in the field" and add "unconventional steps that confine the claim to a particular useful application." Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 17 – 21: Applicant general allegations regarding the combination of Divine, McClusky, Sohail, and Paluri, maintained herein, not teaching the new amended limitations from the pending claims are not persuasive. Because the Applicant is focusing on each prior art teaching, rather than focusing on the BRI of the language of each claim limitation and how their corresponding limitation steps are different from the prior art teachings while considering the broadest reasonable interpretation (BRI) of each claim. Thus, under the BRI of the claim limitations pointed by the Applicant are still reasonably taught by at least this combination of references. Because the step of “receive” user feedback confirming that the one or more messages are associated with a patentable concept” including identification of patent applications was reasonably taught by Divine (See ¶0097 and ¶0100 – 103; Divine) and also Sohail reasonably taught the user feedback confirmation received as “training data” via “intents mapped to constraints through human labeling or through inferred labeling, e.g., based on user corrections to content items selected using the machine learning model or reactions to stories (e.g., stories that receive a high frequency of social media “likes” can imply that the constraint's uses were correctly selected)” (see C5; L11 – 19; Sohail).
As for the step of updating the embedding function to map the messages to a second embedding that is within a third threshold distance of a patent application embedding with the user feedback and correlations, it was still and reasonably taught by Sohail as the document understanding model training items can further provide relationships between natural language segment portions and relationships between natural language segment topics, allowing the trained document understanding model to generate such relationships between the topics for new natural language segments. In some implementations, a document understanding model can embed natural language segments into a semantic meaning space, allowing the model to identify one or more vectors in the semantic space corresponding to the natural language segment. For example, the machine learning model can be trained to map the phrases “female president,” “queen,” “matriarch,” and “empress” to similar areas in the semantic space” (see C24; L10 – 23; Sohail). Finally, Applicant’s arguments fail to comply with 37 CFR 1.111(b) as they amount to general allegations that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the combination of these references. Therefore, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
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.
At least the instant independent claims 1, 3 - 7, 9 - 11 and 14 - 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 20 of U.S. Patent No 11297030 B2 in view of McClusky (U.S. Patent No. 11392651 B1) in further view of Sohail (U.S. Patent No. 12033258 B1). Although the claims at issue are not identical, they are not patentably distinct from each other because they are not patentably distinct from each other because the differences between the claims are considered to be obvious as set forth below:
Instant claims
Co-pending or reference claims
(US 11297030 B2)
Claims 1, 19 and 20: An apparatus for patent-relevant message identification, comprising: (claim 19)
at least one processor;
at least one memory coupled with the at least one processor; and instructions stored in the at least one memory and executable by the at least one processor to cause the apparatus to:
generate a set of features corresponding to one or more messages posted to a group-based communication channel of a group-based communication system;
perform, using the set of features, an embedding of the one or more messages into an embedding space using an embedding function trained based at least in part on embeddings of portions of patent application documents into the embedding space
receive, based at least in part on performing the embedding, an output comprising an indication that the one or more messages are associated with a patentable concept, wherein the output is based at least in part on: a first embedding of the one or more messages being within a first threshold distance of a centroid of a plurality of patent application embeddings within the embedding space, the first embedding being greater than a second threshold distance from each patent application embedding of the plurality of patent application embeddings, and a weight associated with the group-based communication channel;
search one or more additional group-based communication channels of the group-based communication system for one or more additional messages associated with the patentable concept based at least in part on the output;
send, to a user device for display, the indication that the one or more messages and the one or more additional messages are associated with the patentable concept;
receive user feedback confirming that the one or more messages are associated with a patentable concept, the user feedback including an identification of a filed patent application corresponding to the patentable concept; and
update, in accordance with the user feedback and correlations between group-based communication messages and filed patent applications, the embedding function to map the one or more messages to a second embedding that is within a third threshold distance of a patent application embedding of the filed patent application within the embedding space
Claims 1, 8 and 14: A system comprising: (claim 8)
one or more processors;
a memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
receiving a first message from a first computing device associated with a first user;
determining a context associated with the first message, the context comprising one or more messages associated with at least one of the first user or another user;
determining, based at least in part on the first message and the context, a first embedding;
associating the first embedding with at least one of the first user or the a first channel;
receiving at least a portion of a second message from a second computing device associated with a second user;
determining a second embedding associated with at least the portion of the second message; and
in response to a determination that a distance between the first embedding and the second embedding satisfies a threshold distance criterion, causing the second computing device associated with the second user to display one or more recommended methods of communicating with the first user.
Consequently, for at least the pending claims 1, 19 and 20 in view of US Patent No. 11297030 B2 and its claims 1, 8 and 14, the differences between the claims are the recitation of the following limitations:
wherein the output is based at least in part on: …the first embedding being greater than a second threshold distance from each patent application embedding of the plurality of patent application embeddings,
and a weight associated with the group-based communication channel;
search one or more additional group-based communication channels of the group-based communication system for one or more additional messages associated with the patentable concept based at least in part on the output;
send, to a user device for display, the indication that the one or more messages and the one or more additional messages are associated with the patentable concept;
receive user feedback confirming that the one or more messages are associated with a patentable concept, the user feedback including an identification of a filed patent application corresponding to the patentable concept; and
update, in accordance with the user feedback and correlations between group-based communication messages and filed patent applications, the embedding function to map the one or more messages to a second embedding that is within a third threshold distance of a patent application embedding of the filed patent application within the embedding space
The US 11297030 B2 and its claims 1, 8 and 14 did not taught the steps indicated above. However, these instant application’s limitations were further evaluated by McClusky which at least taught the step of performing embeddings of the messages using an embedding function trained within the machine learning algorithm to associated technology inputs in various inputs and stored documents which was interpreted as “the Technology Element recognizer perform best on unprocessed natural language text that has been converted into vector word embeddings” (see C14; L8 – 55; McClusky) and to “train the Technology Element Recognizer, technical word embeddings are created from as large a variety of documents to be processed. It is appreciated that a larger variety of documents may enhance the training of the Technology Element Recognizer” (see C18; L10 – 24; McClusky). Similarly, McClusky taught the step of receiving an output based on a weight as an example wherein “contextual factors determine weights for selecting language providers or sets of contextual factors can be mapped to different language providers. As a more specific example, a work natural language interface can be mapped to an instant message language provider that searches a history of work conversations while a natural language interface used for social conversations can be mapped to a language provider that searches social media posts” (see C35; L28 – 38; McClusky). Finally, the step of “receive user feedback confirming that the one or more messages are associated with a patentable concept” including identification of patent applications filed was taught by McClusky as “a certain number of examples have been gathered, the Problem Element Recognizer Logic can then be retrained on the human-corrected output to improve performance” (see C26; L6 – 9; McClusky) and “for further refinement, a human can review the associated word for any word embeddings that are added in this method and then accept them for inclusion and add the word to the Technology Element ontology list” (see C19; L33 – 37; McClusky).
Therefore, it would have been obvious to one skilled in the art at the time of filing because would have provided to the instant application with the abilities of performing embeddings of the messages using an embedding function trained within the machine learning algorithm to associated technology inputs in various inputs and stored documents, receiving an output based on a weight and receiving user feedback confirming messages have a patentable concept, as taught by McClusky in order to identify technical features of documents and text to the known systems and techniques for identifying and capturing innovation, including applying analysis to collected innovation information for providing innovation insights wherein the analysis employs machine learning algorithms (as disclosed by Divine) to identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. One of ordinary skill in the art would have been motivated to apply the known technique of using machine learning and training algorithms using embeddings within the document in order to identify technical features of documents and text because it would identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. (see C1; L37 – 40; McClusky), see also MPEP 2143.I.G.
On the other hand, McClusky did not teach the step of receiving an output at least based on the first embedding being greater than a second threshold distance from each of a plurality of embeddings which was then taught by Sohail as the example wherein “In other implementations, constraints can be applied separately from the machine learning model, e.g., to limit the set of possible content items from which the machine learning model can select. For example, to select content items in some cases, the machine learning model can map the intent into an embedding space for content items and then process 500 can select one or more content items or classifications that have embeddings closest to the embedding for the intent and/or that are within a threshold distance” (see C26; L27 – 52; Sohail). Further, the step of “searching” additional communication channels for additional messages associated with the patentable concept was taught by Sohail as “content items can be selected using external output providers, connected through output provider interfaces 452 (e.g., APIs, search interfaces, database connections, etc.), which can receive an intent, constraints, and/or context signals, and return one or more content items” (see C21; L8 – 38; Sohail). Similarly, step of “receive user feedback confirming that the one or more messages are associated with a patentable concept” including identification of patent applications filed was taught by Sohail as “Training data for this machine learning model can use intents mapped to constraints through human labeling or through inferred labeling, e.g., based on user corrections to content items selected using the machine learning model or reactions to stories (e.g., stories that receive a high frequency of social media “likes” can imply that the constraint's uses were correctly selected)” (see C5; L11 – 19; Sohail). Finally, the step of “update” the embedding function to map the messages to a second embedding that is within a third threshold distance of a patent application embedding, it was at least taught as “the machine learning model can map the intent into an embedding space for content items and then process 500 can select one or more content items or classifications that have embeddings closest to the embedding for the intent and/or that are within a threshold distance. In some implementations, the mapping can use other or additional techniques such as category matching (e.g., selecting content items with metadata, such as category tags and/or relationships, that match the one or more intents). Such metadata can be applied to content items using machine learning techniques (e.g., object identification, keyword identifiers, emotion or mood identifiers, etc.) and/or can be supplied by human tagging of content items.” (see C26; L39 – 51; Sohail). But also, “the document understanding model training items can further provide relationships between natural language segment portions and relationships between natural language segment topics, allowing the trained document understanding model to generate such relationships between the topics for new natural language segments. In some implementations, a document understanding model can embed natural language segments into a semantic meaning space, allowing the model to identify one or more vectors in the semantic space corresponding to the natural language segment. For example, the machine learning model can be trained to map the phrases “female president,” “queen,” “matriarch,” and “empress” to similar areas in the semantic space” (see C24; L10 – 23; Sohail).
Thus, it would have been obvious to one of ordinary skill in the art before the earliest effective filing date because would have provided to the instant application and McClusky with the abilities of receiving an output at least based on the first embedding being greater than a second threshold distance from each of a plurality of embeddings, “searching” additional communication channels for additional messages associated with the patentable concept, receiving user feedback confirming messages have a patentable concept and “update” the embedding function to map the messages to a second embedding that is within a third threshold distance of a patent application embedding, as taught by Sohail. But also, to apply the known technique of embedding documents and conversations with embeddings to generated and associated information in real time (as disclosed by Sohail) to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms (as disclosed by the combination of Divine and McClusky) in order to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. Because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of embedding documents and conversations with embeddings to generated and associated information in real time to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items). See also MPEP § 2143(I)(D).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 - 7, 9 - 11 and 14 - 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 19, the most representative claim of the independent claims set 1, 19 and 20, as follows:
At Step 1: Claims 1, 3 - 7, 9 - 11,14 - 18 and 21 falls under statutory category of a method, claims 19 and 22 are directed to a process and claim 20 is considered an article of manufacture.
At Step 2A Prong 1: Claim 19 (representative of claim 1 and 20) recites an abstract idea in the following limitations:
…at least one processor;
at least one memory coupled with the at least one processor; and instructions stored in the at least one memory and executable by the at least one processor to cause the apparatus to:
generate a set of features corresponding to one or more messages posted to a group-based communication channel of a group-based communication system;
perform, using the set of features, an embedding of the one or more messages into an embedding space using an embedding function trained based at least in part on embeddings of portions of patent application documents into the embedding space
receive, based at least in part on performing the embedding, an output comprising an indication that the one or more messages are associated with a patentable concept, wherein the output is based at least in part on:
a first embedding of the one or more messages being within a first threshold distance of a centroid of a plurality of patent application embeddings within the embedding space,
the first embedding being greater than a second threshold distance from each patent application embedding of the plurality of patent application embeddings, and
a weight associated with the group-based communication channel;
search one or more additional group-based communication channels of the group-based communication system for one or more additional messages associated with the patentable concept based at least in part on the output;
send, to a user device for display, the indication that the one or more messages and the one or more additional messages are associated with the patentable concept;
receive user feedback confirming that the one or more messages are associated with a patentable concept, the user feedback including an identification of a filed patent application corresponding to the patentable concept; and
update, in accordance with the user feedback and correlations between group-based communication messages and filed patent applications, the embedding function to map the one or more messages to a second embedding that is within a third threshold distance of a patent application embedding of the filed patent application within the embedding space.
Generally, and as disclosed in the specification in ¶0001 and ¶0015, this claimed invention provides a “group-based communication system may provide access to a group-based communication platform, which may in turn support multiple group-based communication channels” for the “identification of patent-relevant messages in a group- based communication system using machine learning techniques.” However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) are/is recited in the representative claim 19 in the forms of “fundamental economic principles or practices” and “commercial or legal interactions”. Specifically, the abstract idea is recited in at least the steps of “generate a set of features corresponding to one or more messages posted…”, “perform, using the set of features, an embedding of the one or more messages into an embedding space…” wherein the embeddings are based on “portions of patent application documents” to “receive” an output that contains “patent application embeddings” with weights to then “search one or more additional group-based communication channels of the group-based communication system” for “additional messages associated with the patentable concept…”, “send” indication that certain messages are “associated with the patentable concept” and “receive” feedback confirming such indications in order to update and “map” the messages to a “second embedding” compared to an embedding related to “the filed patent application”. Because generating message features, to perform embeddings of messages to associate them with patentable concepts while receiving confirmation of such associations in order to map them to filed patent applications at least encompasses fundamental economic principles or practices related to property valuation and commercial/legal interactions related to agreements in the form of contracts and/or legal obligations (i.e. analyzing secured or confidential communications related to innovation and project analysis resulting in intellectual property (IP) protection or IP assets) for the purpose of protecting such property from other marketing/selling risks involves. The limitations, substantially comprising the body of the claim, recite standard processes found in standard practice in all industries that try to stay at the cutting edge of their respective technology. This is common practice when meetings and brainstorming events are used to identify potential innovation and patentable subject matter and then those ideas are then compared to what is out there in the public. This is standard in innovation practices or intellectual property protection and analysis. Because the limitations above closely follow the steps standard in interactions between people and businesses such as communicating about ideas, analyzing the ideas against the public information, and generating a result indicating that there is patentable subject matter within communications, and the steps of the claims involve organizing human activity, the claim recites an abstract idea consistent with the “organizing human activity” grouping set forth in the see MPEP 2106.04(a)(2)(II).
The steps of “receive, based at least in part on performing the embedding, an output comprising an indication that the one or more messages are associated with a patentable concept…” that are based on messages being “within a first threshold distance of a centroid of a plurality of patent application embeddings”, “first embedding being greater than a second threshold distance from each patent application embedding” and “a weight associated with the group-based communication channel”, to “search one or more additional group-based communication channels of the group-based communication system for one or more additional messages associated with the patentable concept…”, “send…the indication that the one or more messages and the one or more additional messages are associated with the patentable concept” and “receive user feedback confirming that the one or more messages are associated with a patentable concept…” fall under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because receiving message information, analyzing the information related to the messages and patent information (i.e. analyzing/associating embeddings of messages with patentable concepts that is given as an output) in order to send the user, the result of the analysis at least encompasses observation, evaluation and judgement. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when associating/analyzing such information as output, as well as the concept is merely claimed to be performed on a generic computer that uses an embedding function trained (i.e. machine learning (ML) model) and is merely using a computer as a tool to perform the concept of analyzing patentable concepts (see MPEP 2106.04(a)(2)(III)(B & C)).
As for steps of “perform, using the set of features, an embedding of the one or more messages into an embedding space using an embedding function trained based at least in part on embeddings of portions of patent application documents into the embedding space”, wherein the claimed “embedding function trained” is directed to using a ML model that “includes an embedding function” to “accurately map between group-based communication message language and patent application language” within an embedding space to determine messages that “are likely to be associated with a patentable concept based on a distance associated” with a message embedding and the patent application embeddings “satisfying a proximity threshold” that can also be “based on being within a threshold distance from a centroid of the patent application embeddings” (see ¶0030 and ¶0067 – 69 from Applicant disclosure and claims 17 – 18) and thus, this step encompass mathematical calculations (i.e. training the algorithm using stored information related to conversations and patent documents). See ¶0045, ¶0051 and ¶0059 from Applicant disclosure for further support.
At Step 2A Prong 2: For independent claims 1, 19 and 20, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of: a group-based communication channel(s) of a group-based communication system, using an embedding function trained (from claims 1, 19 and 20); an apparatus, at least one memory coupled with the at least one processor (from claim 19); at least one processor (from claims 19 and 20) and a non-transitory computer-readable medium (from claim 20). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, these limitations are recited as being performed by the computer that further uses an embedding function (i.e. ML model trained). The computer and the embedding function used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for performing an embedding of messages into an embedding (or vector) space. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform. Therefore, these elements do not themselves amount to an improvement to the interface or computer, to a technology or another technical field. This is consistent with Applicant’s disclosure which states that the computing device “The device 905 may be an example of a processing device, such as an application server, a database server, a cloud-based server or service, a worker server, a server cluster, a virtual machine, a container, a network device, a user device, or any combination of these or other computing devices.” and “The machine learning model330 may be trained by the group-based communication system or by some other machine learning system”. (See app. Spec. ¶0050 – 51 and ¶0102).
Finally, the steps of “receive, based at least in part on performing the embedding, an output…”, “send…the indication that the one or more messages and the one or more additional messages are associated with the patentable concept”, “receive user feedback confirming that the one or more messages are associated with a patentable concept…” and “update, in accordance with the user feedback and correlations between group-based communication messages and filed patent applications, the embedding function…” in the representative claim is really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Step 2B: For independent claims 1, 19 and 20, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a group-based communication channel(s) of a group-based communication system, using an embedding function trained (from claims 1, 19 and 20); an apparatus, at least one memory coupled with the at least one processor (from claim 19); at least one processor (from claims 19 and 20) and a non-transitory computer-readable medium (from claim 20). These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 3 - 7, 9 – 11, 14 – 18 and 22, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity, mental and mathematical processes. They describe additional limitations steps of:
Claims 3 - 7, 9 – 11, 14 – 18 and 22: further describes the abstract idea of the method for patent-relevant message identification and merely recites further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself. Thus, being directed to the abstract idea groups of “fundamental economic principles or practices” and “commercial or legal interactions” as it involves property valuation and handling agreements in the form of contracts and/or legal obligations (i.e. analyzing secured or confidential communications related to innovation and project analysis resulting in intellectual property (IP) protection or IP assets) for the purpose of protecting such property from other marketing/selling risks involves. Finally certain limitations require observation, evaluation and judgement as well as encompasses mathematical calculations (i.e. training the algorithm using stored information related to conversations and patent documents; see claims 17 – 18).
Step 2A Prong 2 and Step 2B: For dependent claims 3 - 7, 9 – 11, 14 – 18 and 22, these claims do not include additional elements. Rather, what is claimed simply further defines the same abstract idea that was set forth in independent claims 1, 19 and 20. Nothing additional is claimed that is not part of the abstract idea.
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.
Claims 1, 3 - 7, 9 - 11 and 14 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Divine (U.S. Pub No. 20180158159 A1) in view of McClusky (U.S. Patent No. 11392651 B1) in further view of Sohail (U.S. Patent No. 12033258 B1) and Paluri (U.S. Pub No. 20170132510 A1).
Regarding claims 1, 19 and 20:
This independent claim set is represented by claim 19
Divine teaches:
at least one processor; at least one memory coupled with the at least one processor; and instructions stored in the at least one memory and executable by the at least one processor to cause the apparatus to: (In ¶0226; Fig. 38 (3812 and 3813): teaches “Processor 3812 of the illustrated example includes a local memory 3813 (e.g., a cache)”.)
generate a set of features corresponding to one or more messages posted to a group-based communication channel of a group-based communication system; (In ¶0136 – 137; Fig. 3 (302 – 304) 1; Fig. 14: teaches that upon the system collects “audio and video data” via an “office room” that have “a button in the room called “collect innovation”” which is pressed by the user, and format and populate the data as a “disclosure submission” (see ¶0134), “the system can identify joint inventors that discussed the idea in person, via email, via instant message, and other digital discussion mechanisms” wherein “one field of a pre-populated innovation disclosure could disclose the technical problem in need of solving. This can be determined from project documentation, customer request emails or submissions, or overheard from product testing or feature discussions” which is directed to the set of features generated. See ¶0061 and ¶0082 – 84 for more details of the type of data sources (i.e. “public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes”, etc.) and type of user communications (i.e. from “a virtual meeting system”, “email and chat services”, “calendar systems”, etc.) the system can collect/pull from to include in its database. Refer to ¶0162 for Fig. 14 details regarding the “pre-populated disclosure” and “innovation” data collected from “online group meeting teleconference recording, audio discussion transcript (could be from a group meeting room, phone call, casual conversation near a recording device, etcetera), document file, an Office Action issued by a patent office of a parent case related to this idea, an email, a presentation file, an image file, a patent database, and other sources.” See ¶0181 – 182 also for other types of data collected.)
receive, based at least in part on performing the embedding, an output comprising an indication that the one or more messages are associated with a patentable concept… [see Sohail] (In ¶0163; Figs. 15 – 16, 19 – 20 and 31: teaches receiving an output wherein “FIG. 15 shows a user interface screen 1500 for display of innovation ideas to an inventor” wherein the “innovation capture engine 402 has previously created an innovation database for the project quantum”. See ¶0167 wherein “the innovation insights engine can perform a full check of any potential innovation for submission that has not received user feedback, and then provide the innovations to the innovators or others for review” and another example wherein “a chief technology officer may want to see a full year innovation summary on demand, thereby giving the system a trigger to provide such a report immediately”. Refer to ¶0172 – 175 for user notifications to “review innovation related to the project”, showing an “innovation report 2000 generated by the innovation insights engine” as well as other reports that it can prepare for the user and ¶0206 – 207 for another “exemplary innovation insight 3100 related to patent pool licensing” as a report.)
send, to a user device for display, the indication that the one or more messages and the one or more additional messages are associated with the patentable concept; (In ¶0163; Figs. 15 and 19: teaches “FIG. 15 shows a user interface screen 1500 for display of innovation ideas to an inventor, according to an embodiment. The innovation capture engine 402 has previously created an innovation database for the project quantum”. Further, in ¶0172 – 175, “An innovation database is created and all innovation is tracked, with regular user interface notifications to the users, such as shown in FIG. 19. FIG. 19 also displays information as to how the innovation idea was determined, including the data source types used in collecting the innovation information”. Refer to ¶0206 – 207 for an example wherein “Once the system understands the current licensing deals of the organization, the innovation insights engine can provide a report of whether or not the organization can utilize ideas certain patents” which is another example of sending indication that the messages are associated to a patentable concept.)
receive user feedback confirming that the one or more messages are associated with a patentable concept, the user feedback including an identification of a filed patent application corresponding to the patentable concept; and (In ¶0097; Figs. 8 – 9 and 11: teaches an example wherein “the innovation collection engine acquiring direct input is group input. At the end of a meeting, the system can summarize the meeting notes and ask the group which ideas may be innovative, before they leave the room. This is one way to get a strong source of innovation feedback for the innovative collection engine to use in its analysis and rating of the data and ideas coming from that meeting. Similarly, user interface screens can be presented for projects, product releases, and so forth, specifically asking for direct input”. Further, in ¶0100 – 103, “the system learns through feedback loops which keywords, and which individuals, are more indicative of true innovation. For example, if person A says something is “game changing” leading to an idea being submitted for consideration and then the idea is declined for protection at the intellectual property committee review, this can be fed back in as historical data for the system to know about that person, that keyword, and that idea. Thus, the deep learning AI algorithm related to innovation identification can be continuously improved. Alternatively, if person B communicates that something is “valuable to the customer”, it is submitted, and it is then approved for patent filing, such feedback to the innovation identification module, and algorithms, provides indications to more highly rank that person, that keyword, and that idea.” Another example is given in Fig. 8 and ¶0114, wherein “the user of the system within the organization, without any additional innovation effort, can review the auto-generated digital innovation disclosure, edit the innovation disclosure as needed, and then approve it for intellectual property protection.” See ¶0122 for user feedback loop details further shown in Fig. 9 and see ¶0131 for details of Fig. 11.)
Divine teaches that its system can use machine learning and an “adaptive artificial intelligence system” to measure and adjust the weighs from keyword inputs based on their relevance/importance, when determining potential innovations (see ¶0090 and Fig. 6; Divine) as well as it can collect and organize such inputs from “conversations” or other types of electronic communications (see ¶0061, ¶0162 and Fig. 14; Divine). However, Divine does not explicitly teach the abilities of specifically performing embeddings of the messages using an embedding function trained within the machine learning algorithm to associated technology inputs in various inputs and stored documents. However, McClusky teaches:
perform, using the set of features, an embedding of the one or more messages into an embedding space using an embedding function trained based at least in part on embeddings of portions of patent application documents into the embedding space (In C14; L8 – 55: teaches that “the text of sections interpreted as having positive tone and/or negative tone in the documents are analyzed with Technology Element recognizers to identify Technology Elements in those sections (or proximate to those sections)”, wherein such ““Technology Elements” refers to subsets of a document (most commonly a set of characters, word, set of words, clause, or individual sentence) that names and/or describes the systems, materials, technical processes, associated attributes, and scalars for a given technology”. Further, “the Technology Element recognizer perform best on unprocessed natural language text that has been converted into vector word embeddings.” Refer to C18; L10 – 24 wherein “To train the Technology Element Recognizer, technical word embeddings are created from as large a variety of documents to be processed. It is appreciated that a larger variety of documents may enhance the training of the Technology Element Recognizer. Various techniques for word embeddings are known in the art including GLOVE, FastText, W2V, Part-of-speech-tagged W2V. Any of these word embeddings processes are compatible with the systems and methods of the present disclosure. Training of the Technology Element Recognizer may also start with an initial ontology of Technology Elements created based on external sources, e.g., WordNet, MatWeb, or supplemented with manual input (i.e. additional knowledge and experience gained from speaking with experts in various technologies).” Further, it is disclosed in C19; L20 – 36 that “Based on the structure of the sentence, pieces of the sentence are analyzed to determine if they are a Technology Element. In exemplary embodiments, to determine if they are a Technology Element, the word embeddings for these words or n-grams are then compared to the word embeddings for the initial taxonomy of Technology Elements. Word embeddings within a certain distance of any of the word embeddings in the list of terms for a Technology Element are then recorded and the name of the Technology Element can be substituted into the text of the document or added to a keyed Technology Element field. For further refinement, a human can review the associated word for any word embeddings that are added in this method and then accept them for inclusion and add the word to the Technology Element ontology list.” For more details see C21; L30 – 45, C22; L40 – 49, C23; L39 – 47 and C24; L37 – 44 for examples of the use of “hierarchical word embedding algorithm” and “hierarchical clustering algorithm”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine to provide the abilities of specifically performing embeddings of the messages using an embedding function trained within the machine learning algorithm to associated technology inputs in various inputs and stored documents, as taught by McClusky in order to identify technical features of documents and text to the known systems and techniques for identifying and capturing innovation, including applying analysis to collected innovation information for providing innovation insights wherein the analysis employs machine learning algorithms (as disclosed by Divine) to identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. One of ordinary skill in the art would have been motivated to apply the known technique of using machine learning and training algorithms using embeddings within the document in order to identify technical features of documents and text because it would identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. (see C1; L37 – 40; McClusky), see also MPEP 2143.I.G.
Divine teaches searching through communication channels as it “can pull in public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes” (see ¶0061; Divine) and due to the assignment of “innovation related data” to “many types of sources”, “the innovation insights engine can retrieve all the information for the idea and the originating source” (see ¶0162; Divine). Divine further teaches the application of weights to outputs based on “relevance and importance to the specific entity or situation” that can be further “tweaked manually and/or be automatically altered through an adaptive artificial intelligence system” (see ¶0090 – 93; Divine), application of weights to “direct input of innovation if the user providing the feedback” (see ¶0096; Divine) as well as assigning “weighing values to the keywords analysis” (see ¶00101; Divine), and weights that are applied to “the amount of resources analysis” that “can also be changed based on the type of resource and the individuals involved” (see ¶0104; Divine). Further, McClusky teaches the use of embedding spaces and use of threshold hold distances between patent and or technology information within the inputted information (see C16; L30 – 44 and C30; L56 – 67; McClusky) as well as the words embeddings can be compared to the “word embeddings for the initial taxonomy of Technology Elements” and when the “Word embeddings within a certain distance of any of the word embeddings in the list of terms for a Technology Element are then recorded and the name of the Technology Element can be substituted into the text of the document or added to a keyed Technology Element field” (see C19; L25 – 33; McClusky). However, neither Divine or McClusky explicitly teach the abilities of having an output that is specifically based, at least in part on: a first embedding of messages within a first threshold distance of a plurality of embeddings wherein the first embedding being greater than a second threshold distance from each embedding as well as a weight associated to the communication channel, to then search additional communication channels for additional messages associated with the patentable concept, and update the embedding function to map the messages to a second embedding that is within a third threshold distance of a patent application embedding. However, Sohail teaches:
…wherein the output is based at least in part on: a first embedding of the one or more messages being within a first threshold distance of a centroid of a plurality of patent application embeddings within the embedding space, the first embedding being greater than a second threshold distance from each patent application embedding of the plurality of patent application embeddings, and a weight associated with the group-based communication channel; (In C21; L8 – 38: teaches that the “Content item mapper 446 can augment the conversation based on the intents from intent generator 442. In some cases, for example, selecting content items can be performed using a machine learning model that maps an intent into an embedding space and selects content items with a corresponding embedding within a threshold distance of the intent embedding”. Further, “In addition or alternatively, content item selection can be based on other mapping algorithms, such as matching phrases or topics of the intents to keywords, categories, identified objects, etc. of the content items. In yet further implementations, content items can be selected using external output providers, connected through output provider interfaces 452 (e.g., APIs, search interfaces, database connections, etc.), which can receive an intent, constraints, and/or context signals, and return one or more content items. In some cases, the output providers are selected based on determining that the output provider matches the current conversation, the intent, and/or the natural language provider”. As for a first embedding being greater than a second threshold distance from each embedding, this is taught in C26; L27 – 52 wherein “In other implementations, constraints can be applied separately from the machine learning model, e.g., to limit the set of possible content items from which the machine learning model can select. For example, to select content items in some cases, the machine learning model can map the intent into an embedding space for content items and then process 500 can select one or more content items or classifications that have embeddings closest to the embedding for the intent and/or that are within a threshold distance. In some implementations, the mapping can use other or additional techniques such as category matching (e.g., selecting content items with metadata, such as category tags and/or relationships, that match the one or more intents). Such metadata can be applied to content items using machine learning techniques (e.g., object identification, keyword identifiers, emotion or mood identifiers, etc.) and/or can be supplied by human tagging of content items.”)
search one or more additional group-based communication channels of the group-based communication system for one or more additional messages associated with the patentable concept based at least in part on the output; (In C21; L8 – 38; Fig. 4 (452): teaches that “content items can be selected using external output providers, connected through output provider interfaces 452 (e.g., APIs, search interfaces, database connections, etc.), which can receive an intent, constraints, and/or context signals, and return one or more content items”.)
update, in accordance with the user feedback and correlations between group-based communication messages and filed patent applications, the embedding function to map the one or more messages to a second embedding that is within a third threshold distance of a patent application embedding of the filed patent application within the embedding space. (In C26; L39 – 51: teaches that “the machine learning model can map the intent into an embedding space for content items and then process 500 can select one or more content items or classifications that have embeddings closest to the embedding for the intent and/or that are within a threshold distance. In some implementations, the mapping can use other or additional techniques such as category matching (e.g., selecting content items with metadata, such as category tags and/or relationships, that match the one or more intents). Such metadata can be applied to content items using machine learning techniques (e.g., object identification, keyword identifiers, emotion or mood identifiers, etc.) and/or can be supplied by human tagging of content items.” Also, in C24; L10 – 23, “In some implementations, the document understanding model training items can further provide relationships between natural language segment portions and relationships between natural language segment topics, allowing the trained document understanding model to generate such relationships between the topics for new natural language segments. In some implementations, a document understanding model can embed natural language segments into a semantic meaning space, allowing the model to identify one or more vectors in the semantic space corresponding to the natural language segment. For example, the machine learning model can be trained to map the phrases “female president,” “queen,” “matriarch,” and “empress” to similar areas in the semantic space” which is directed to updating the embedding function with user feedback and correlations to further map new messages to second embedding that is within another threshold distance.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine and McClusky to provide the abilities of having an output that is specifically based, at least in part on: a first embedding of messages within a first threshold distance of a plurality of embeddings wherein the first embedding being greater than a second threshold distance from each embedding as well as a weight associated to the communication channel, to then search additional communication channels for additional messages associated with the patentable concept, and update the embedding function to map the messages to a second embedding that is within a third threshold distance of a patent application embedding, as taught by Sohail. But also, to apply the known technique of embedding documents and conversations with embeddings to generated and associated information in real time (as disclosed by Sohail) to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms (as disclosed by the combination of Divine and McClusky) in order to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. Because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of embedding documents and conversations with embeddings to generated and associated information in real time to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items). See also MPEP § 2143(I)(D).
Finally, neither Divine, McClusky or Sohail explicitly teach the ability of determining a first threshold distance, specifically of a centroid of a plurality of embeddings. However, Paluri teaches:
a first embedding of the one or more messages being within a first threshold distance of a centroid of a plurality of patent application embeddings… (In ¶0055: teaches that “the first point may be located proximate to one or more clusters in the embedding space, and search algorithms may be applied in each of the proximate clusters to identify one or more points in each of the proximate clusters that are within a threshold distance of a centroid of the respective proximate cluster”. Further in ¶0055 and ¶0060, “a search algorithm may be applied to identify one or more second points that are within a threshold distance of the first point in the particular cluster” and to “determine that one or more second points in clusters 620, 630, and 650 are within a threshold distance of first point 310, and the second content items corresponding to the one or more second points in clusters 620, 630, and 650 may be identified as similar to the first content item”, respectively.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine and McClusky and Sohail to provide the ability of determining a first threshold distance, specifically of a centroid of a plurality of embeddings, as taught by Paluri, to the known method and system for monitoring conversations to identify content and context related to intellectual property or assets wherein embeddings are used to map the information and threshold distances are used to determine the closeness of information to specific other data or inputs (as disclosed by the combination of Divine, McClusky, and Sohail) to map content items to embeddings in a multi-dimensional embedding space. One of ordinary skill in the art would have been motivated to apply the known technique of determining a threshold distance of a centroid of a plurality of embeddings because it would map content items to embeddings in a multi-dimensional embedding space (see Paluri ¶0004).
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of determining a threshold distance of a centroid of a plurality of embeddings (as disclosed by Paluri) to the known method and system for monitoring conversations to identify content and context related to intellectual property or assets wherein embeddings are used to map the information and threshold distances are used to determine the closeness of information to specific other data or inputs (as disclosed by the combination of Divine, McClusky, and Sohail) to map content items to embeddings in a multi-dimensional embedding space, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of determining a threshold distance of a centroid of a plurality of embeddings to the known method and system for monitoring conversations to identify content and context related to intellectual property or assets wherein embeddings are used to map the information and threshold distances are used to determine the closeness of information to specific other data or inputs to map content items to embeddings in a multi-dimensional embedding space). See also MPEP § 2143(I)(D).
Regarding claim 3:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine further teaches:
wherein the user feedback comprises a first indication of whether the one or more messages support a second patent application, a second indication of whether the second patent application is relevant to an organization, a third indication of a prior art concern for the second patent application, a fourth indication of whether the one or more messages support a trade secret, or any combination thereof. (In ¶0097; Figs. 8 – 9 and 11: teaches that an example wherein “the innovation collection engine acquiring direct input is group input. At the end of a meeting, the system can summarize the meeting notes and ask the group which ideas may be innovative, before they leave the room. This is one way to get a strong source of innovation feedback for the innovative collection engine to use in its analysis and rating of the data and ideas coming from that meeting. Similarly, user interface screens can be presented for projects, product releases, and so forth, specifically asking for direct input”. Further, in ¶0167, “the innovation insights engine can perform a full check of any potential innovation for submission that has not received user feedback, and then provide the innovations to the innovators or others for review”. Finally, in ¶0100 – 103, “the system learns through feedback loops which keywords, and which individuals, are more indicative of true innovation. For example, if person A says something is “game changing” leading to an idea being submitted for consideration and then the idea is declined for protection at the intellectual property committee review, this can be fed back in as historical data for the system to know about that person, that keyword, and that idea. Thus, the deep learning AI algorithm related to innovation identification can be continuously improved. Alternatively, if person B communicates that something is “valuable to the customer”, it is submitted, and it is then approved for patent filing, such feedback to the innovation identification module, and algorithms, provides indications to more highly rank that person, that keyword, and that idea.”)
Regarding claim 4:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
McClusky at least teaches the “word embeddings within a certain distance of any of the word embeddings in the list of terms for a Technology Element are then recorded and the name of the Technology Element can be substituted into the text of the document or added to a keyed Technology Element field” and that “a human can review the associated word for any word embeddings that are added in this method and then accept them for inclusion and add the word to the Technology Element ontology list” (see C19; L20 – 36; McClusky). But, Divine further teaches:
wherein sending the indication that the one or more messages and the one or more additional messages are associated with the patentable concept further comprises: sending, to the user device for display, an indication of one or more patent applications associated with the one or more messages based at least in part on the embedding space. (In ¶0061; Figs. 15 and 19: teaches “system can pull in public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes all could be included in such an innovation database” and the system can “identify such feedback as related to a certain product or service based on the keywords of the product or service name or nickname”. See ¶0163 wherein “FIG. 15 shows a user interface screen 1500 for display of innovation ideas to an inventor” and see ¶0172 – 175, wherein “An innovation database is created and all innovation is tracked, with regular user interface notifications to the users, such as shown in FIG. 19. FIG. 19 also displays information as to how the innovation idea was determined, including the data source types used in collecting the innovation information”. Refer to ¶0206 – 207 for an example wherein “Once the system understands the current licensing deals of the organization, the innovation insights engine can provide a report of whether or not the organization can utilize ideas certain patents” which is another example of sending indication that the messages are associated to a patentable concept.)
Regarding claim 5:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine further teaches:
wherein sending the indication that the one or more messages and the one or more additional messages are associated with the patentable concept further comprises: sending, to the user device for display, an indication of one or more suggested inventors associated with the one or more messages and the patentable concept based at least in part on the output. (In ¶0182; Figs. 11 and 14: teaches an example wherein “One patent related insight the system can provide is to identify inventors. This can take the form of identifying joint inventors on a single idea or proposing to add additional inventors and their ideas into the discussion. As an example for identifying joint inventors, one inventor may have part of the idea in a schematic drawing and another inventor communicates another part of the idea in an email, the system can collect both ideas into the innovation collection engine, identify them as related, and assign them to the same innovation database. Then, when a pre-populated disclosure is generated, the innovation insights engine puts both inventors as inventors. This can help capture all the inventors on an innovation idea.” See ¶0131 for details of Fig. 11 and ¶0161 for details of Fig. 14.)
Regarding claim 6:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Sohail teaches the set of features generated from text as the “document understanding model 436 can map the phrases from the natural language segment into an embedding space and produce corresponding semantic identifiers that are mapped in the embedding space within a threshold distance of the phrase” (see C19; L17 – 51; Sohail). But Divine further teaches:
further comprising: receiving, from a second user device, a new message posted to the group-based communication channel, wherein the one or more messages comprises the new message and the set of features is generated in response to the new message being posted to the group-based communication channel. (In ¶0061; Fig. 9; Fig. 12 (1202 – 1204); Fig. 14: receiving new messages posted in a communication channel still satisfied as the “system can pull in public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes all could be included in such an innovation database. The system can identify such feedback as related to a certain product or service based on the keywords of the product or service name or nickname”. Further, in ¶0136 -137, upon the system collects “audio and video data” via an “office room” that have “a button in the room called “collect innovation”” which is pressed by the user, and format and populate the data as a “disclosure submission” (see ¶0134), “the system can identify joint inventors that discussed the idea in person, via email, via instant message, and other digital discussion mechanisms” wherein “one field of a pre-populated innovation disclosure could disclose the technical problem in need of solving. This can be determined from project documentation, customer request emails or submissions, or overheard from product testing or feature discussions”.)
Regarding claim 7:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Sohail teaches the set of features generated from text as the “document understanding model 436 can map the phrases from the natural language segment into an embedding space and produce corresponding semantic identifiers that are mapped in the embedding space within a threshold distance of the phrase” and “In some implementations, the machine learning models of the document understanding model 436 can also use parts-of-speech tags, from parts-of-speech tagger 438, as further input for determining the embedding of the natural angered segment phrases.” (see C19; L17 – 51; Sohail). But Divine further teaches:
further comprising: receiving, from a second user device, a user input tagging a message as potentially patent relevant, wherein the one or more messages comprises the message and the set of features is generated in response to the user input tagging the message as potentially patent relevant. (In ¶0085: teaches that “Files are also stored in folders, tagged, categorized, and have other metadata that helps the innovation collection engine and innovation insights engine draw connections and conclusions.” See ¶0198 wherein “Alerts can then be provided for tagging various innovation ideas as part of a portfolio.”)
Regarding claim 9:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine further teaches:
further comprising: retrieving a corpus comprising at least the portions of the patent application documents, wherein the portions of the patent application documents comprise abstracts of the patent application documents, summaries of the patent application documents, detailed descriptions of the patent application documents, claims of the patent application documents, art units of the patent application documents, prosecution statuses of the patent application documents, or any combination thereof. (In ¶0187; Fig. 6 (604): teaches “One patent related insight the system can provide is prior art identification. This means identifying relevant, previous technologies to an innovation idea. As discussed above, the innovation collection engine can pull information in from internal patent databases and product databases relating to the organizations own prior public technologies. And the innovation collection engine can pull information in from public and paid databases”. Thus, such prior art identification is part of the “prior art analysis” that the “innovation collection engine can poll” from “online services that specialize in innovation documentation, such as trade journals, conference websites, and patent services that do patent landscapes and prior art searching. This polling of online services can be done through whatever mechanisms the online service requires, such as keyword searching, technology filtering, and the like. The prior art analysis can then compare the current technology ideas within the organization against the located prior art for potential innovation identification as shown in FIG. 6” (see ¶0105 – 109).)
Regarding claim 10:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 9.
Divine further teaches:
further comprising: filtering the patent application documents from a plurality of patent application documents based at least in part on a type of patent. (In ¶0107 – 109: teaches “For an external example of prior art analysis, the innovation collection engine can poll online services that specialize in innovation documentation, such as trade journals, conference websites, and patent services that do patent landscapes and prior art searching. This polling of online services can be done through whatever mechanisms the online service requires, such as keyword searching, technology filtering, and the like. The prior art analysis can then compare the current technology ideas within the organization against the located prior art for potential innovation identification as shown in FIG. 6.” Further, “For the industrial designs of new products, they can be analyzed versus the prior art by the innovation collection engine and then the system can recommend whether the industrial design is potential innovation for intellectual property protection.”)
Regarding claim 11:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine further teaches:
wherein the output further indicates a type of patent associated with the one or more messages. (In ¶0109; Figs. 15, 19 and 21: teaches an example wherein “For the industrial designs of new products, they can be analyzed versus the prior art by the innovation collection engine and then the system can recommend whether the industrial design is potential innovation for intellectual property protection”. Refer to ¶0163 for details of Fig. 15 wherein the system “previously created an innovation database for the project quantum” and displays innovation ideas for patentability related to the information stored. See ¶0172 – 175, ¶0203 and ¶0206 – 207 for more user notifications and reports.)
Regarding claim 14:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Sohail teaches the set of features generated from text as the “document understanding model 436 can map the phrases from the natural language segment into an embedding space and produce corresponding semantic identifiers that are mapped in the embedding space within a threshold distance of the phrase” and “the mapping can use other or additional techniques such as category matching (e.g., selecting content items with metadata, such as category tags and/or relationships, that match the one or more intents). Such metadata can be applied to content items using machine learning techniques (e.g., object identification, keyword identifiers, emotion or mood identifiers, etc.) and/or can be supplied by human tagging of content items.” (see C19; L17 – 51 and C26; L44 – 51; Sohail). But Divine further teaches:
wherein the set of features comprises a first indication of one or more users associated with the one or more messages, a second indication of the group-based communication channel, a third indication of a type of the group- based communication channel, a fourth indication of a quantity of users corresponding to the group-based communication channel, a fifth indication of a message length for at least one message of the one or more messages, a sixth indication of a channel description for the group- based communication channel, a seventh indication of whether a file is attached to at least one message of the one or more messages, an eight indication of a quantity of replies for at least one message of the one or more messages, or any combination thereof. (In ¶0162; Figs. 4 and 14: teaches these set of features comprising different indications as “One of the benefits of the pre-populated disclosure shown in FIG. 14 is the right-most column that gives source of data. Because the innovation collection engine receives data from many types of sources and is able to assign innovation related data to a single innovation database for the idea, the innovation insights engine can retrieve all the information for the idea and the originating source. FIG. 14 shows examples of sources of innovation data such as an online group meeting teleconference recording, audio discussion transcript (could be from a group meeting room, phone call, casual conversation near a recording device, etcetera), document file, an Office Action issued by a patent office of a parent case related to this idea, an email, a presentation file, an image file, a patent database, and other sources”. Refer to ¶0061 – 65 wherein “One innovation data store may include the collected information from any teams and groups as long as the topic of the innovation was related to a specific technology area, such as user interfaces. This may include many sketches saved, along with innovation ideas proposed on digital white boards and shared between colleagues through a cloud service. One innovation data store may include innovation information related to a specific project, making it easy for managers to track the various improvements overall to a project” and different innovation insights that further identify the collected information as shown in Fig. 4.)
Regarding claim 15:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine further teaches:
wherein the one or more messages comprise a first message posted to the group-based communication channel and one or more replies to the first message within the group-based communication channel. (In ¶0061; Fig. 9; Fig. 12 (1202 – 1204); Fig. 14: teaches the messages posted in a communication channel with replies as the “system can pull in public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes all could be included in such an innovation database. The system can identify such feedback as related to a certain product or service based on the keywords of the product or service name or nickname”. Further, in ¶0136 -137, upon the system collects “audio and video data” via an “office room” that have “a button in the room called “collect innovation”” which is pressed by the user, and format and populate the data as a “disclosure submission” (see ¶0134), “the system can identify joint inventors that discussed the idea in person, via email, via instant message, and other digital discussion mechanisms” wherein “one field of a pre-populated innovation disclosure could disclose the technical problem in need of solving. This can be determined from project documentation, customer request emails or submissions, or overheard from product testing or feature discussions”. See ¶0162 wherein “FIG. 14 shows examples of sources of innovation data such as an online group meeting teleconference recording, audio discussion transcript (could be from a group meeting room, phone call, casual conversation near a recording device, etcetera), document file, an Office Action issued by a patent office of a parent case related to this idea, an email, a presentation file, an image file, a patent database, and other sources.”)
Regarding claim 16:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine teaches searching through communication channels as it “can pull in public tweets, emails, feedback databases, sales team emails, management discussions and product planning meeting notes” (see ¶0061; Divine) and due to the assignment of “innovation related data” to “many types of sources”, “the innovation insights engine can retrieve all the information for the idea and the originating source” (see ¶0162; Divine). Further, McClusky teaches the use of embedding spaces and use of threshold hold distances between patent and or technology information within the inputted information (see C16; L30 – 44 and C30; L56 – 67; McClusky). However, neither Divine or McClusky explicitly teach the ability of having embeddings specifically from messages that are based on text from a file. Thus, Sohail further teaches:
wherein the first embedding of the one or more messages is based at least in part on text from a file corresponding to a message of the one or more messages. (In C20; L22 – 24: teaches “intent generator 442 can receive a natural language segment which it can map to one or more intent semantic identifiers in an embedding space” wherein the “natural language segment” corresponds to “input modalities (e.g., text, audio, video) and various graphical user interfaces (“GUIs”) or audio interfaces, e.g., for displaying video, images, or text or playing sound” via “natural language interfaces” that are “video chat applications, social media platforms, artificial reality systems, telephone or other audio conversation systems, instant message applications, etc.” and can “integrate selected content items into existing aspects of the conversation, e.g., by providing an overlay on a video chat, interjecting an image or video into a text stream of an instant message conversation, or adding an object attached to a speaker in an artificial reality system.” (see C2; L56 – 66 and C3; L1 – 33).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine and McClusky to provide the ability of having embeddings specifically from messages that are based on text from a file, as taught by Sohail. But also, to apply the known technique of embedding documents and conversations with embeddings to generated and associated information in real time (as disclosed by Sohail) to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms (as disclosed by the combination of Divine and McClusky) in order to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. Because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of embedding documents and conversations with embeddings to generated and associated information in real time to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items). See also MPEP § 2143(I)(D).
Regarding claim 17:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine teaches that its system can use machine learning and an “adaptive artificial intelligence system” to measure and adjust the weighs from keyword inputs based on their relevance/importance, when determining potential innovations (see ¶0090 and Fig. 6; Divine) as well as it can collect and organize such inputs from “conversations” or other types of electronic communications (see ¶0061, ¶0162 and Fig. 14; Divine). However, neither Divine or McClusky explicitly teach the ability of further training an embedding function based on messages that are tagged as patent relevant. Thus, Sohail further teaches:
wherein the embedding function is further trained based at least in part on a plurality of messages tagged as patent relevant from the group-based communication system. (In C19; L17 – 51: teaches that “In some implementations, the machine learning models of the document understanding model 436 can also use parts-of-speech tags, from parts-of-speech tagger 438, as further input for determining the embedding of the natural angered segment phrases”. Further, “Parts-of-speech tagger 438 can analyze a natural language segment and identify how various phrases within the natural language segment are used and how they relate to other phrases within the natural language segment. For example, parts of speech tagger 438 can identify a subject, an action, modifiers, etc.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine and McClusky to provide the ability of further training an embedding function based on messages that are tagged as patent relevant, as taught by Sohail. But also, to apply the known technique of embedding documents and conversations with embeddings to generated and associated information in real time (as disclosed by Sohail) to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms (as disclosed by the combination of Divine and McClusky) in order to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. Because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of embedding documents and conversations with embeddings to generated and associated information in real time to the known method and system for generating and associated patent information and documents to conversations and messages using machine learning algorithms and tags to identify constraints for mapping the intents to content items or context signals for selecting appropriate content items). See also MPEP § 2143(I)(D).
Regarding claim 18:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claim 1.
Divine teaches that its system can use machine learning and an “adaptive artificial intelligence system” to measure and adjust the weighs from keyword inputs based on their relevance/importance, when determining potential innovations (see ¶0090 and Fig. 6; Divine) as well as it can collect and organize such inputs from “conversations” or other types of electronic communications (see ¶0061, ¶0162 and Fig. 14; Divine). However, Divine does not explicitly teach the ability of further training the embedding function with additional embeddings of portions of non-patent literature. However, McClusky further teaches:
wherein the embedding function is further trained based at least in part on additional embeddings of portions of non-patent literature into the embedding space. (In C18; L10 – 24: teaches that “To train the Technology Element Recognizer, technical word embeddings are created from as large a variety of documents to be processed. It is appreciated that a larger variety of documents may enhance the training of the Technology Element Recognizer” wherein such variety of documents can include “patents, patent applications, technical papers or journal articles, news articles, web pages, Facebook posts, Instagram posts, Twitter tweets, company annual reports or financial disclosure forms, product reviews, press releases, market reports, PowerPoint presentations, conference proceedings or presentations, earnings calls, product specification sheets, product announcements, blog posts, whitepapers, etc.” (see C6; L15 – 22). Refer to C14; L8 – 55 also for more details.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Divine to provide the ability of further training the embedding function with additional embeddings of portions of non-patent literature, as taught by McClusky in order to identify technical features of documents and text (including Non-Patent literature or NPL) to the known systems and techniques for identifying and capturing innovation, including applying analysis to collected innovation information for providing innovation insights wherein the analysis employs machine learning algorithms (as disclosed by Divine) to identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. One of ordinary skill in the art would have been motivated to apply the known technique of using machine learning and training algorithms using embeddings within the document in order to identify technical features of documents and text (including NPL) because it would identifying unmet customer needs in a specific technical area and matching available technologies with the unmet needs. (see C1; L37 – 40; McClusky), see also MPEP 2143.I.G.
Regarding claims 21 – 22:
The combination of Divine, McClusky, Sohail, and Paluri, as shown in the rejection above, discloses the limitations of claims 1 and 19, respectively.
Paluri teaches that “Information may be pushed to a client system 130 as notifications, or information may be pulled from client system 130 responsive to a request received from client system 130. Authorization servers may be used to enforce one or more privacy settings of the users of social-networking system 160. A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by social-networking system 160 or shared with other systems (e.g., third-party system 170), such as, for example, by setting appropriate privacy settings” (see ¶0027; Paluri). But Divine further teaches:
further comprising: triggering messaging to one or more first users associated with the one or more additional messages to request that at least one first user of the one or more first users grants a second user access to the one or more additional messages based at least in part on maintaining a permission setting for the second user, the second user being associated with the user device. (In ¶0143: teaches an example wherein “pre-populated innovation disclosure could disclose what information, if any, has been disclosed to third parties” as “email, text, and telephone communications” detected and require an innovator wanting to present an idea in public to “submit the idea for approval of such disclosure. If the system detects such sharing, it can check if protection for the idea has already been filed and whether an agreement has been signed with the third party around confidentiality, such as a non-disclosure agreement. Warnings can be provided to the user in cases when issues may arise, as noted further below”.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fields (U.S. Pub No. 20240291785 A1) is pertinent because it “generally relates to using artificial intelligence (AI) and/or machine learning (ML) to (i) identify patentable ideas from company emails; (ii) scrape emails to determine questions associated with a scheduled meeting; (iii) scrape emails to determine connections between company employees, ideas, teams, or projects; (iv) scrape emails to assess employee satisfaction; and/or (v) analyze incoming emails and generate automatic responses.”
Lenchner (U.S. Pub No. 20190114395 A1) is pertinent because it is “a method for determining ownership of intellectual property data, again by a processor, is provided. Communications provided by one or more contributors relating to the intellectual property data are tracked using one or more immutable ledgers. The communications maintained in the one or more immutable ledgers may be analyzed to identify a degree of contribution by the one or more contributors to the intellectual property. A degree of ownership may be assigned to the intellectual property data for the one or more contributors according to the analyzed content.”
Graeser (U.S. Pub No. 20220114340 A1) is pertinent because it “relates to a system and method for an automatic search tool, and in particular to such a system and method which analyzes an idea description from a user and searches through prior art, such as patents, automatically.”
LEPELTIER (U.S. Pub No. 20170075877 A1) is pertinent because it is “a computer-implemented method of handling a text expressed in a natural language comprising creating a second text or patent claim sentence from a first text or patent claim sentence and timestamping said second text or patent claim sentence”
Walker (U.S. Pub No. 20150026079 A1) is pertinent because it is “systems, methods and articles of manufacture (e.g., non-transitory computer-readable media) which provide for, in accordance with some embodiments, (i) identifying, for a given product, relevant patents; and (ii) arranging a package of such relevant patents to be offered for license or other terms to an entity associated with such product (e.g., a supplier, designer, manufacturer or seller of the product or a component of the product, referred to as a “product provider” herein)”
Dugan (U.S. Pub No. 20180225280 A1) is pertinent because it “relates to methods, techniques, and systems for text classification, such as improved techniques for the collection of training data and the presentation of classification results” wherein the “text classification system 100 (“TCS”)” functions to “learn one or more classification functions and evaluate text in light of those functions. In the described embodiment, the TCS 100 focuses on learning legal rules based on the application of those rules to patent claims.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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