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
Claims 1-20 are pending in this application.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-2, 8-9, and 15-16 are rejected on the ground of nonstatutory double patenting over claims 1-2, 8-10 and 16-18 of Co-Pending Application No. 18/749,510. Although the claims at issue are not identical, they are not patentably distinct from each other because removing inherent and/or unnecessary limitations/step and rearranging the claims would be within the level of one of ordinary skill in the art. It is well settled that the omission of an element, e.g. “receiving at least one rating for at least one participant”, and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Omission of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
Instant Application No. 18/749,517
Co-Pending Application No. 18/749,510
1. An apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models; and
one or more rules and policies referencing a plurality of authorized communication operations by a workspace device interfacing with the apparatus; and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device configured to perform a plurality of communication operations with the workspace device;
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data;
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data;
determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and
determine whether the first communication operation at least partially matches the plurality of authorized communication operations; and
in response to determining that the first communication operation at least partially matches the plurality of authorized communication operations, present the first request summary as a first reset point to train the one or more machine learning models.
2. The apparatus of claim 1, wherein:
the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and
in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services.
1. An apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models; and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device;
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data;
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data;
in response to summarizing the first text data, determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and
map the first target operation to a first suggestion, the first suggestion comprising a first plurality action items to complete the first target operation; and
presenting the first suggestion to a workspace device.
2. The apparatus of claim 1, wherein:
the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and
in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services.
8. The apparatus of claim 7, wherein the processor is further configured to:
generate an overall communication summary comprising a plurality of datapoints indicating of the first request summary in relation to a first plurality of words identified in the first text data, the second request summary in relation to a second plurality of words identified in the second text data, the first suggestion corresponding to the first target operation, the second suggestion corresponding to the second target operation, and the third suggestion corresponding to the second target operation;
in response to generating the overall communication summary, execute the machine learning algorithm to structure the plurality of datapoints to train the one or more machine learning models; and
train the one or more machine learning models in accordance with a structured version of the plurality of datapoints.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claim 8 recites “obtaining first audio data from a user device configured to perform a plurality of communication operations with a workspace device; in response to receiving the first audio data, executing a machine learning algorithm to perform one or more operations comprising: transcribing the first audio data into first text data; summarizing the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data; determining a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and determining whether the first communication operation at least partially matches a plurality of authorized communication operations; and in response to determining that the first communication operation at least partially matches the plurality of authorized communication operations, presenting the first request summary as a first reset point to train one or more machine learning models”. These activities reflect perceiving information, analyzing and extracting meaning, deciding or selecting an operation based on that meaning, checking whether the operating matches authorized operations and if authorized, presenting the request summary as a reset point to train ML models.
[Abstract idea indicators]
Transcribing speech into text is the conversion of verbal content to written form—a task humans routinely perform mentally or with conventional tools.
Summarizing text to infer a purpose or intent is an activity of comprehension and extraction of meaning, i.e., a cognitive process.
Determining a target operation and mapping it to action items are decision --- making and planning steps that are mental processes.
Determining whether an operation matches authorized operations --- rule-based decision-making, which is a form of organizing human activity / mental process.
Presenting a summary as a reset point --- outputting information; no specific technical mechanism for training is recited.
These steps are information processing and decision-making — activities that can be performed in the human mind or with pen and paper, and that courts/USPTO treat as abstract ideas.
Even though the claim references machine learning and “rules/policies,” these are recited functionally, without technical detail about how they are implemented in a non-conventional way.
Conclusion for Step 2A, Prong One:Yes — the claim is “directed to” an abstract idea (mental processes + organizing human activity).
Step 2A, Prong Two: Integration into a practical application?
The claim must apply the abstract idea in a way that improves the functioning of a computer or another technology.
Here:
The claim applies the abstract idea in the context of communication operations between a user device and a workspace device.
However, the claim does not recite how the ML algorithm is implemented in a novel way, how the rules/policies are structured to improve system performance, or any specific technical solution to a technical problem.
The “workspace device” and “authorized operations” context is a field-of-use limitation — it confines the idea to a specific environment but doesn’t change the nature of the abstract idea.
Conclusion for Step 2A, Prong Two:No — the claim does not integrate the exception into a practical application that improves computer technology.
Step 2B: Inventive Concept
Now we ask: Do the additional claim elements (individually or in combination) amount to significantly more than the abstract idea?
Generic components: memory, processor, user device — standard computer hardware.
Machine learning algorithm: recited at a high level, with no specific architecture, training process, or unconventional application.
Rules and policies: generic data structures for authorization checks.
Training reset point: conceptually interesting, but claimed at a functional level without specific technical means.
The combination appears to be a generic computer implementation of an abstract workflow.
Conclusion for Step 2B:No inventive concept is apparent — the claim recites known computer components executing generic functions.
With respect to claims 1 and 15, the claim is similar to claim 8 and claims 1 and 15 recite additional element of “memory” and “processor”. The processor and memory are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions and being used as an applying) such that it amounts no more than mere instructions to apply the exception using a generic computer component as well. These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 2-7, 8-14, and 16-20, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, claims 1-20 are rejected
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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 8-11 and 15-18 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Darla et al., (US Pub. 2024/0386883 filed on 2023-05-16) in view of Sindhwani (US Pat. 10102855).
Regarding claim 1, Darla discloses: an apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models (Fig. 1, elements 130, 132, 134, [0054] evaluating data using a machine learning model); and
one or more [rules and policies] referencing a plurality of authorized communication operations by a workspace device interfacing with the apparatus ([0055] “customer account database 152 may include a customer's organizational information”); and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device configured to perform a plurality of communication operations with the workspace device (Fig. 1, [0039][0040] obtaining voice input from a customer 102);
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data (Fig. 1, [0040][0041] transcribing into text data by speech-to-text engine 122);
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044] tokenizing the text data and removing stop words, suffixes and prefixes that do not contribute to semantic content);
determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation (Fig. 1, item 130, 140 and Fig. 2, steps 220-250, [0045]-[0048] vectorizing the utterance tokens and identifying a predicted intent using a Machine Learning Engine and determining a query including the predicted intent); and
determine whether the first communication operation at least partially matches the plurality of authorized communication operations (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “receive intents from ML engine 130 and query engine 142 may use the intents as a lookup parameter to retrieve content from content repository 150… artifacts stored in content repository 150 may be indexed by intents”; [0033] “artifacts may include institutional knowledge such as instructions and content on how to accomplish tasks related to customer inquiries”); and
in response to determining that the first communication operation at least partially matches the plurality of authorized communication operations, present the first request summary as a first reset point to train the one or more machine learning models (Fig. 1, interface 144, Fig. 2, step 270, [0055][0056][0060][0063] displaying the artifact according to customer verifying information for an agent’s use via an interface; Fig. 1, [0040][0054] training machine learning models in accordance with record responses to IVR prompts and send the response to transcription platform and suggestions of products and/or services based on a state of a customer).
Darla does not explicitly teach the bracketed limitation however Sindhwani does explicitly teach including the bracketed limitation:
one or more [rules and policies] referencing a plurality of authorized communication operations by a workspace device interfacing with the apparatus (Col. 15, lines 38-65, “associated with a group account, and various individuals may have user accounts that are operating under the rules and configurations of the group account”).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the systems and methods for intent prediction and usage as taught by Darla with the method of storing and utilizing rules referencing a plurality of authorized communication as taught by Sindhwani to provide user convenience by allowing users to directly check the results/answers to their requests on their monitors and by providing better suggestions.
Regarding claim 2, Darla in view of Sindhwani discloses the apparatus of claim 1, and Darla further discloses: wherein: the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services (Fig. 1, contact management platform 110, [0039][0058][0059] receiving incoming contact from a customer and identifying authenticate and/or verify the customer).
Regarding claim 3, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein the processor is further configured to:
obtain second audio data and third audio data from the user device (Fig. 1, [0039][0040][0062][0063] obtaining voice input);
in response to receiving the second audio data and the third audio data, execute the machine learning algorithm to: transcribe the second audio data into second text data (Fig. 1, [0040][0041][0062] transcribing into text data by speech-to-text engine 122);
summarize the second text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044][0063] tokenizing and processing with an intent model);
in response to summarizing the second text data, determine a second target operation based on the second request summary and the first request summary, the second target operation being a second determined intent to perform a second communication operation (Fig. 2, steps 220-250, [0045]-[0048][0063] determining the intent from the intent model);
determine whether the second communication operation at least partially matches the plurality of authorized communication operations (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “The intent management platform may query a content repository using the intent as a lookup parameter and retrieve related content, such as KB artifacts. Content in the content repository may be indexed by intent”); and
in response to determining that the third communication operation at least partially matches the plurality of authorized communication operations, present the third request summary as a second reset point to train the one or more machine learning models (Fig. 1, interface 144, Fig. 2, step 270, [0055][0056][0060][0063] displaying the artifact according to customer verifying information for an agent’s use via an interface; Fig. 1, [0040][0054] training machine learning models in accordance with record responses to IVR prompts and send the response to transcription platform and suggestions of products and/or services based on a state of a customer).
Darla does not explicitly teach however Sindhwani does explicitly teach:
summarize the second text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data (Col. 25, line 33 – Col. 26, line 21, identifying request, ‘Play songs by Artist 1’; Col. 24, lines 4- 55, performing natural language understanding processing of speech input, determining a domain of an utterance. By determining the domain, and narrowing down which services and functionalities offered by an endpoint device may be relevant);
in response to summarizing the second text data, determine a second target operation based on the second request summary and the first request summary, the second target operation being a second determined intent to perform a second communication operation (Fig. 1, step 160, Col. 7, lines 11-21, Col. 25, line 33 – Col. 26, line 21, determining an intent of the utterance based on the first text data and various language models to play according to the request, ‘Play songs by Artist 1’);
determine whether the second communication operation at least partially matches the plurality of authorized communication operations (Col. 29, lines 54-64, accessing instruction database 280 to obtain suggestion associated with the intent, Col. 24, lines 50-67, “NLU system 260 may store an entity library including database entries for specific services available on a specific device or devices”); and
in response to determining that the first communication operation does not at least partially match the plurality of authorized communication operations, transcribe the third audio data into third text data (Col. 25, line 59 – Col. 26, line 65, if the query was “play songs by ‘Artist 1,’” after failing to determine an album name or song name called “songs” by “Artist 1,” NER system 272 may search the domain vocabulary for the word ‘songs’);
summarize the third text data into a third request summary, the third request summary being representative of a third predicted purpose associated with the third audio data; and in response to summarizing the third text data, determine a third target operation based on the third request summary and the first request summary, the third target operation being a third determined intent to perform a third communication operation (Col. 25, line 59 – Col. 26, line 65, “the object “songs” may correspond to some or all of the songs associated with a particular artist (e.g., “Artist 1”). In the alternative, generic words may be checked before the gazetteer information, or both may be tried, potentially producing two different results”); and
present the third request summary (Fig. 1, step 162, Col. 7, lines 22-45, presenting data which may be received from an instructions database).
The previous motivation statement as in claim 1 is still applied.
Regarding claim 4, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein the processor is further configured to:
obtain second audio data and third audio data from the user device (Fig. 1, [0039][0040][0062][0063] obtaining voice input);
in response to receiving the second audio data and the third audio data, execute the machine learning algorithm to:
transcribe the second audio data into second text data (Fig. 1, [0040][0041][0062] transcribing into text data by speech-to-text engine 122);
summarize the second text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044][0063] tokenizing and processing with an intent model);
in response to summarizing the second text data, determine a second target operation based on the second request summary and the first request summary, the second target operation being a second determined intent to perform a second communication operation (Fig. 2, steps 220-250, [0045]-[0048][0063] determining the intent from the intent model);
determine whether the second communication operation at least partially matches the plurality of authorized communication operations (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “The intent management platform may query a content repository using the intent as a lookup parameter and retrieve related content, such as KB artifacts. Content in the content repository may be indexed by intent”); and
in response to determining that the third communication operation at least partially matches the plurality of authorized communication operations, present the third request summary as a second reset point to train the one or more machine learning models (Fig. 1, interface 144, Fig. 2, step 270, [0055][0056][0060][0063] displaying the artifact according to customer verifying information for an agent’s use via an interface; Fig. 1, [0040][0054] training machine learning models in accordance with record responses to IVR prompts and send the response to transcription platform and suggestions of products and/or services based on a state of a customer).
Darla does not explicitly teach however Sindhwani does explicitly teach:
in response to determining that the first communication operation does not at least partially match the plurality of authorized communication operations, discard the second request summary; transcribe the third audio data into third text data (Fig. 1, step 160, Col. 7, lines 11-21, Col. 25, line 33 – Col. 26, line 21, determining an intent of the utterance based on the first text data and various language models to play according to the request, ‘Play songs by Artist 1’; Col. 25, line 33 – Col. 26, line 65, if the query was “play songs by ‘Artist 1,’” after failing to determine an album name or song name called “songs” by “Artist 1,” NER system 272 may search the domain vocabulary for the word ‘songs’; (Col. 29, lines 54-64, accessing instruction database 280 to obtain suggestion associated with the intent, Col. 24, lines 50-67, “NLU system 260 may store an entity library including database entries for specific services available on a specific device or devices”);
summarize the third text data into a third request summary, the third request summary being representative of a third predicted purpose associated with the third audio data; and in response to summarizing the third text data, determine a third target operation based on the third request summary and the first request summary, the third target operation being a third determined intent to perform a third communication operation (Col. 25, line 59 – Col. 26, line 65, the object “songs” may correspond to some or all of the songs associated with a particular artist (e.g., “Artist 1”) which is indicative of “a third target operation”. Alternatively generic words may be checked before the gazetteer information, or both may be tried, potentially producing two different results”; Col. 24, lines 4- 55, performing natural language understanding processing of speech input, determining a domain of an utterance. By determining the domain, and narrowing down which services and functionalities offered by an endpoint device may be relevant); and
present the third request summary (Fig. 1, step 162, Col. 7, lines 22-45, presenting data which may be received from an instructions database).
The previous motivation statement as in claim 1 is still applied.
Regarding claims 8-11, Claims 8-11 are the corresponding method claims to system claims 1-4. Therefore, claims 8-11 are rejected using the same rationale as applied to claims 1-4 above.
Regarding claims 15-18, Claims 15-18 are the corresponding medium claims to system claims 1-4. Therefore, claims 15-18 are rejected using the same rationale as applied to claims 1-4 above.
Allowable Subject Matter
Claims 5-7, 12-14 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and Double Patenting rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEONG-AH A. SHIN whose telephone number is (571)272-5933. The examiner can normally be reached 9 AM-3PM.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/Primary Examiner, Art Unit 2659