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 .
Claims 1 – 20 are pending in this Office Correspondence.
Priority
This Application is a Continuation of Application 18/821,146, filed 08/30/2024 is a Continuation of 18/409,071, filed 01/10/2024 ,now U.S. Patent # 12147453.
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 - 10 and 11 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: The claims 1 and 11 recite a “an apparatus and a method for receive a plurality of entity profiles . . .; generate exploitation data . . . ; determine collaboration data . . .; classifying the plurality of operational data. . . ; determining an exploitation rank . . . ; plotting a continuum score . . . ; and determining the collaboration data . . . “ the claim(s) recites a series of steps and, therefore, is a process.
Step 2A, Prong One:
Claim 1 recites " generate exploitation data..."; "determine collaboration data..."; "classifying the operational data..."; "determining an exploitation rank..."; "plotting a continuum score..."; and "determining the collaboration data...". These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "computer systems", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements "receive a plurality of entity profiles..." this limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); this limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The memory and processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations "receive a plurality of entity profiles..." are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claims 1 and 11 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 2 – 10 and 12 – 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitation of “receiving the plurality of entity profiles comprises: displaying, using a chatbot, to an entity and at a graphical user interface data structure, a plurality of questions; and receiving information regarding a corresponding entity profile as a function of displaying the plurality of questions”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 12, since they also recite limitations that further elaborate on the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of claim 2. The claim recites the additional limitation of “the trained exploitation machine-learning model was trained using exploitation training data, wherein the exploitation training data comprised a plurality of data entries comprising operational data inputs correlated to exploitation data outputs”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 13, since they also recite limitations that further elaborate on the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitation of “generate the plurality of collaboration categories as a function of criteria defined by the exploitation data”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 14, since they also recite limitations that further elaborate on the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “retrieve the plurality of collaboration categories from a database as a function of criteria defined by the exploitation data”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 15, since they also recite limitations that further elaborate on the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “i classify the operational data within the plurality of collaboration categories as assets or liabilities”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 16, since they also recite limitations that further elaborate on the abstract idea.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “determining the exploitation rank comprises: generating, for each entity participating in a collaboration, a corresponding exploitation rank, wherein the corresponding exploitation rank represents an amount of attributes contributed by an entity; and generating, for each attribute contributed by the entity, an attribute-specific exploitation rank, wherein the attribute-specific exploitation rank is used to normalize the operational data”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 17, since they also recite limitations that further elaborate on the abstract idea.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitation of “determining the exploitation rank comprises generating, for each attribute of an entity, an attribute quantifier, wherein: the attribute quantifier is generated as a function of the plurality of collaboration categories and one or more of the demand data and the exploitation data; and the attribute quantifier assigns an importance value to a corresponding collaboration category as a function of an impact on entity performance”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 18, since they also recite limitations that further elaborate on the abstract idea.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional limitation of “display collaboration data within a graphical user interface data structure”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 19, since they also recite limitations that further elaborate on the abstract idea.
Claim 10 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 10 recites the same abstract idea of claim 9. The claim recites the additional limitation of “receive, using the graphical user interface data structure, a digital signature from an entity, wherein the digital signature indicates a willingness of an entity to opt into a collaboration”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 20, since they also recite limitations that further elaborate on the abstract idea.
Therefore, claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Double Patenting
Claim 1 – 20 of this application is patentably indistinct from claims 1 – 16 of Application No. 18/409,071, now U.S. Patent 12,147,453. Pursuant to 37 CFR 1.78(f) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
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).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
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The subject matter claimed in the instant application is fully disclosed in the co-pending application and is covered by the co-pending application since the co-pending application and the application are claiming common subject matter, as follows:
Instant application 19/375,372
Co-pending Application 18/409,071
USP 12,147,453
1. An apparatus for the generation of exploitation data, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to:
receive a plurality of entity profiles from a plurality of entities, wherein each entity profile corresponds to an entity of the plurality of entities;
generate exploitation data using a trained exploitation machine-learning model as a function of operational data and demand data of the plurality of entity profiles; and
determine collaboration data as a function of the exploitation data, wherein determining the collaboration data comprises:
classifying the operational data into a plurality of collaboration categories;
determining an exploitation rank for each collaboration category of the plurality of collaboration categories;
plotting, for each exploitation rank, a continuum score representing a degree to which a corresponding operational trait of the entity is an asset or a liability; and
determining the collaboration data as a function of a comparison between a first continuum score corresponding to a first entity and a second continuum score corresponding to a second entity.
2. The apparatus of claim 1, wherein receiving the plurality of entity profiles comprises: displaying, using a chatbot, to an entity and at a graphical user interface data structure, a plurality of questions; and receiving information regarding a corresponding entity profile as a function of displaying the plurality of questions.
3. The apparatus of claim 1, wherein the trained exploitation machine-learning model was trained using exploitation training data, wherein the exploitation training data comprised a plurality of data entries comprising operational data inputs correlated to exploitation data outputs.
4. The apparatus of claim 1, wherein the at least a processor is further configured to generate the plurality of collaboration categories as a function of criteria defined by the exploitation data.
5. The apparatus of claim 1, wherein the at least a processor is further configured to retrieve the plurality of collaboration categories from a database as a function of criteria defined by the exploitation data.
6. The apparatus of claim 1, wherein the at least a processor is further configured to classify the operational data within the plurality of collaboration categories as assets or liabilities.
7. The apparatus of claim 1, wherein determining the exploitation rank comprises: generating, for each entity participating in a collaboration, a corresponding exploitation rank, wherein the corresponding exploitation rank represents an amount of attributes contributed by an entity; and generating, for each attribute contributed by the entity, an attribute-specific exploitation rank, wherein the attribute-specific exploitation rank is used to normalize the operational data.
8. The apparatus of claim 1, wherein determining the exploitation rank comprises generating, for each attribute of an entity, an attribute quantifier, wherein: the attribute quantifier is generated as a function of the plurality of collaboration categories and one or more of the demand data and the exploitation data; and the attribute quantifier assigns an importance value to a corresponding collaboration category as a function of an impact on entity performance.
9. The apparatus of claim 1, wherein the at least a processor is further configured to display collaboration data within a graphical user interface data structure.
10. The apparatus of claim 9, wherein the at least a processor is further configured to receive, using the graphical user interface data structure, a digital signature from an entity, wherein the digital signature indicates a willingness of an entity to opt into a collaboration.
11. A method for the generation of exploitation data, wherein the method comprises: receiving, using at least a processor, a plurality of entity profiles identifying demand data from a plurality of entities, wherein each of the plurality of entity profiles comprises a plurality of operational data, wherein the plurality of entity profiles additionally comprises a first entity profile and a second entity profile; identifying, using the at least a processor, free zone data as a function of the plurality of entity profiles;
determining, using the at least a processor, collaboration data as a function of the free zone data, wherein determining the collaboration data comprises: classifying the plurality of operational data into a plurality of collaboration categories; determining an exploitation rank as a function of the exploitation data and the classification of the plurality of operational data into the plurality of collaboration categories; plotting a plurality of graphical data as a function of the exploitation rank, wherein the plurality of graphical data comprises: a second graphical datum associated with the second entity profile; a first graphical datum associated with the first entity profile; and determining the collaboration data as a function of a comparison of the first graphical datum and the second graphical datum; and displaying the collaboration data using a display device.
12. The method of claim 11, wherein the demand data comprises a demand scope.
13. The method of claim 12, wherein free zone data contains an identification of a target business opportunity.
14. The method of claim 11, wherein the method further comprises identifying, using the at least a processor, the demand data using a web crawler.
15. The method of claim 11, wherein the method further comprises determining, using the at least a processor, the collaboration data comprises a comparison of the first graphical datum and the second graphical datum using a fuzzy inference set.
16. The method of claim 11, wherein the method further comprises identifying, using the at least a processor, a plurality of operational capabilities as a function of the plurality of operational.
17. The method of claim 11, wherein the method further comprises generating, using the at least a processor, an attribute quantifier as a function of the plurality of collaboration categories, wherein the attribute quantifier is configured to an importance value to each collaboration category of the plurality of collaboration categories.
18. The method of claim 11, wherein plotting the plurality graphical data comprises plotting the exploitation rank along a continuum.
19. The method of claim 11, wherein generating the exploitation data comprises generating the exploitation data using an exploitation machine-learning model.
20. The method of claim 19, wherein generating the exploitation data using the exploitation machine-learning model comprises: training the exploitation machine-learning model using exploitation training data, wherein the exploitation training data contains a plurality of data entries containing operational data inputs correlated to exploitation data outputs; and generating the exploitation data as a function of the operational data and the demand data using a trained exploitation machine-learning model.
1. An apparatus for the generation of exploitation data, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory comprising instructions configuring the at least a processor to:
receive a plurality of entity profiles from a plurality of entities, wherein each of the plurality of entity profiles comprises a plurality of operational data, wherein the plurality of entity profiles additionally comprises a first entity profile and a second entity profile;
identify demand data as a function of the plurality of entity profiles;
generate, using a trained exploitation machine-learning model, exploitation data as a function of the operational data and the demand data, wherein the trained exploitation machine-learning model is trained using exploitation training data, wherein the exploitation training data contains a plurality of data entries containing operational data inputs correlated to exploitation data outputs;
determine collaboration data as a function of the exploitation data, wherein determining the collaboration data comprises:
classifying the plurality of operational data into a plurality of collaboration categories;
determining an exploitation rank as a function of the exploitation data and the classification of the plurality of operational data into the plurality of collaboration categories;
plotting a plurality of graphical data as a function of the exploitation rank, wherein the plurality of graphical data comprises:
a first graphical datum associated with the first entity profile; and
a second graphical datum associated with the second entity profile;
determining the collaboration data as a function of a comparison of the first graphical datum and the second graphical datum; and
display the collaboration data using a display device.
2. The apparatus of claim 1, wherein the demand data is indicative of one or more free zones where demand has not been addressed.
3. The apparatus of claim 2, wherein the one or more free zones comprises an identification of a target group.
4. The apparatus of claim 1, wherein identifying the demand data comprises identifying demand data using a web crawler.
5. The apparatus of claim 1, wherein determining the collaboration data comprises a comparison of the first graphical datum and the second graphical datum using a fuzzy inference set.
6. The apparatus of claim 1, wherein the memory further instructs the processor to identify a plurality of operational capabilities as a function of the plurality of operational data.
7..The apparatus of claim 1, wherein the memory further instructs the processor to generate an attribute quantifier as a function of the plurality of collaboration categories, wherein the attribute quantifier is configured to assign an importance value to each collaboration category of the plurality of collaboration categories.
8. The apparatus of claim 1, wherein plotting the plurality graphical data comprises plotting the exploitation rank along a continuum.
9. A method for the generation of exploitation data, wherein the method comprises: receiving, using at least a processor, a plurality of entity profiles from a plurality of entities, wherein each of the plurality of entity profiles comprises a plurality of operational data, wherein the plurality of entity profiles additionally comprises a first entity profile and a second entity profile; identifying, using the at least a processor, demand data as a function of the plurality of entity profiles;
generating, using a trained exploitation machine-learning model, exploitation data as a function of the operational data and the demand data, wherein the trained exploitation machine-learning model is trained using exploitation training data, wherein the exploitation training data contains a plurality of data entries containing operational data inputs correlated to exploitation data outputs;
determining, using the at least a processor, collaboration data as a function of the exploitation data, wherein determining the collaboration data comprises: classifying the plurality of operational data into a plurality of collaboration categories; determining an exploitation rank as a function of the exploitation data and the classification of the plurality of operational data into the plurality of collaboration categories; plotting a plurality graphical data as a function of the exploitation rank, wherein the plurality of graphical data comprises: a second graphical datum associated with the second entity profile; a first graphical datum associated with the first entity profile; and determining the collaboration data as a function of a comparison of the first graphical datum and the second graphical datum; and displaying the collaboration data using a display device.
10. The method of claim 9, wherein the demand data is indicative of one or more free zones where demand has not been addressed.
11. The method of claim 10, wherein the one or more free zones comprises an identification of a target group.
12. The method of claim 9, wherein the method further comprises identifying, using the at least a processor, the demand data using a web crawler.
13. The method of claim 9, wherein the method further comprises determining, using the at least a processor, the collaboration data comprises a comparison of the first graphical datum and the second graphical datum using a fuzzy inference set.
14. The method of claim 9, wherein the method further comprises identifying, using the at least a processor, a plurality of operational capabilities as a function of the plurality of operational data.
15. The method of claim 9, wherein the method further comprises generating, using the at least a processor, an attribute quantifier as a function of the plurality of collaboration categories, wherein the attribute quantifier is configured to an importance value to each collaboration category of the plurality of collaboration categories.
16. The method of claim 9, wherein plotting the plurality graphical data comprises plotting the exploitation rank along a continuum.
Claims 1 – 20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1 – 16 of co-pending application 18/409,071, now U.S. Patent 12,147,453. Although the conflicting claims are not identical, they are not patentably distinct from each other because of corresponding language that recites virtually all of the same elements and functions claimed in the claim 1 of instant application and claim 1 of the copending invention, e.g., “classifying the operational data into a plurality of collaboration categories; determining an exploitation rank for each collaboration category of the plurality of collaboration categories; plotting, for each exploitation rank, a continuum score representing a degree to which a corresponding operational trait of the entity is an asset or a liability; and
determining the collaboration data as a function of a comparison between a first continuum score corresponding to a first entity and a second continuum score corresponding to a second entity.”
The claimed differences would be obvious to a programmer of ordinary skill because the instant claims are merely broader and/or alternate variations of the claims recited in the co-pending application.
Because the instant claims merely add/modify the additional elements from the set of elements and functions claimed in the parent application, such modifications would be readily apparent to a programmer of ordinary skill.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to omit/add/modify the additional elements of claim 1 to arrive at the claim 1 of the instant application because the person would have realized that the remaining element would perform the same functions as before.
It would have been obvious to modify instant claims in order to providing more flexibility, immediacy of information transfer, increase productivity and enhance business intelligence for an enterprise.
Examiner Notes
The examiner has considered the applicant's claims in light of the disclosure. However, the examiner respectfully reminds the applicant that during prosecution before the USPTO, claims are to be given their broadest reasonable interpretation, and the scope of a claim cannot be narrowed by reading disclosed limitations into the claim. See In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997). The Office must apply the broadest reasonable meaning to the claim language, taking into account any definitions presented in the specification. In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citing In re Bass, 314 F.3d 575,577(Fed. Cir. 2002)); “[i]t is the claims that measure the invention.” SRIInt’l v. Matsushita Elec. Corp. of Am., 775 F.2d 1107, 1121 (Fed. Cir. 1985) (enbanc). Written description may not be read into a claim when the claim language is broader than the embodiment. SuperGuide Corp. v. DirecTV Enters, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (citing Electro Med. Sys. S.A. v. Cooper Life Sci., Inc., 34 F.3d 1048, 1054 (Fed. Cir. 1994))
Note that “limitations appearing in the specification will not be read into the claims, and … interpreting what is meant by a word in a claim is not to be confused with adding an extraneous limitation appearing in the specification, which is improper.” Intervet Am., v. Kee-Vet Labs., 887 F.2d 1050, 1053, 12 USPQ2d 1474 1476 (fed. Cir. 1989).
“The ordinary and customary meaning of a claim term is the meaning that the term would have to a person of ordinary skill in the art in question at the time of the invention, i.e., as of the effective filing date of the patent application.” Phillips v. AWH Corp,. 415 F.3d 1303, 1313, 75 USPQ2d 1321, 1326 (fed. Cir. 2005).
“One purpose for examining the specification is to determine if the patentee has limited the scope of the claims.’… For example, an inventor may choose to be his own lexicographer is he defines the specific terms used to describe the invention’ with reasonable clarity, deliberateness, and precision.” Such a definition may appear in the written description, … or in the prosecution history, …” Teleflex, Inc. v. Ficosa N. Am Corp., 299 F.3d 1313, 1325, 63 USPQ2d 1374, 1381 (Fed. Cir. 2002).
Prior art pertinent to the disclosed invention is also cited and Applicants are reminded that they must consider all cited art under Rule 111(c) when amending the claims to conform with 35 U.S.C. 112.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Listed prior art could be used as an obviousness type Office correspondence.
Hanna (USPGPUB 2021/0383261), which discloses a machine learning system has a data store comprising entity data corresponding to an entity. A computing device is in communication with the data store. The computing device receives data describing an aspect of a position for the entity, and generates metadata for the position based on the data describing the aspect of the position, where the metadata comprises a set of skills and tasks associated with the position. A set of physical proximity scores is determined for each of the skills and the tasks based on metadata and distribution of capacity across the set of task locations. A collaboration score is generated for position.
Ferreira Moreno (USPGPUB 2019/0197478), which discloses a processor for receiving a request to initiate a first collaborative session and initializing a set of sensor devices. The processor detects a contribution from a participant in the first collaborative session using one of the sensor devices, classifies the contribution by processing the contribution using a cognitive computing technique and identifies a trait profile corresponding to the participant. The processor updates the trait profile based on the classified contribution and selects the participant to participate in a second collaborative session based on the updated trait profile.
Richter (U.S. Patent 11,863,676), which receive a data collection from a user, where the data collection consists of a set of user specific data objects. The processor assesses a set of user categories as a function of the data collection. The processor identifies a value function as a function of the user specific data objects and the user categories. The processor optimizes the value function to generate a ranked set of user-specific data objects. The processor generates a recommendation for a non-fungible token (NFT) as a function of the ranked set of user-specific data objects. The processor generates the NFT as a function of the recommendation.
Rathja (USPGPUB 2021/0026897), which discloses sharing collaborative documents; and
Colin (USPGPUB 2022/0263877), which discloses a method for facilitating automatic generation and management of collaborative media objects.
Claims 1 – 20 could be allowed when overcoming the above informality.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
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August 22, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161
Prosecution Insights