DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 05/22/2024.
Claims 1-20 are pending in the case.
Claims 1, 16 and 20 are independent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7, 15-17, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sardari et al. (US 12,271,746 B1, filed on 10/20/2022, hereinafter “Sardari”).
Independent Claims 1, 16 and 20:
Sardari discloses a system comprising:
one or more processors coupled with memory [non-transitory computer-readable media] having processor readable instructions, such that, when executed, cause at least one processors to perform a method comprising (Sardari: Fig. 13, claim 8, column 52 lines 49-67 and column 53 lines 1-5.):
detect one or more interactions with elements of content generated by one or more applications, the one or more interactions associated with an account (The asset management service can detect contextual information regarding interactions between the user and the asset management service, Sardari: column 8 lines 12-15. The contextual information includes user interactions with applications, websites and previous user interface elements such as booklets, Sardari: column 2 lines 16-34 and column 3 lines 39-47. The contextual information is associated with a username (account), Sardari: column 8 lines 50-67 and column 9 lines 1-11.);
identify, based at least on the one or more interactions input into one or more models trained with machine learning on a plurality of interactions with a plurality of elements of content indicative of a plurality of characteristics, a characteristic associated with the account (The propensity metric for the user (characteristic associated with the account) is determined based on inputting the contextual information into a trained machine learning model, Sardari: column 19 lines 53-67 and column 20 lines 1-24. The models can be trained on the various types of contextual information that correspond to the various elements of content and are associated with pre-determined propensity metrics and corresponding propensity signals, Sardari: column 3 lines 10-66, column 13 lines 60-67 and column 14 lines 1-18.);
receive a first content for a payroll service to be displayed using a graphical user interface, the first content generated by an application of the one or more applications associated with the account (The user can access content particular to a service offered by the payment service and is associated with the user account, Sardari: claim 1. The service can be a payroll service, Sardari: column 30 lines 52-67 and column 33 lines 46-67. The user accesses the content via a software application, Sardari: column 7 lines 61-67 and column 8 lines 1-11. The software application is a user instance of an asset management resource that enables the user to access data associated with the user account, Sardari: claim 1, column 7 lines 56-67 and column 8 lines 1-2.);
generate, based at least on the first content, an arrangement of elements according to the characteristic (The layout of the user interface elements are personalized based on the propensity metric, Sardar: claim 1, column 8 lines 19-49.); and
display, on the graphical user interface, the arrangement of elements (Sardari: claim 1, abstract.).
Claims 2 and 17:
The rejection of claims 1 and 16 are incorporated. Sardari further teaches a system and method comprising the one or more processors to:
detect elements of the first content generated by the application responsive to a request associated with the account (The process for generating the customized UI is triggered in response to the user device accessing a user instance of the software application associated with the payment service, Sardari: column 7 lines 57-67 and column 8 lines 1-8. The process of customization can include determine which elements to add or remove based on the user’s experience level, Sardari: column 8 lines 25-46.); and
generate a second content comprising the arrangement of elements of the first content based at least on a setting for the arrangement of elements associated with the characteristic (Sardari: column 8 lines 25-46 and, column 9 lines 53-67 and column 10 lines 1-21.); and
display, on the graphical user interface, the customized first content responsive to the request (Sardari: abstract, claim 1, column 7 lines 57-67 and column 8 lines 1-8 and lines 25-46, column 9 lines 53-67 and column 10 lines 1-21.).
Claim 7:
The rejection of claim 1 is incorporated. Sardari further teaches a method comprising the one or more processors to:
determine that the first content corresponds to a first word count that satisfies a threshold for the characteristic corresponding to the word count (It can be determined that a particular content is associated with a threshold text length/quantity which is measured by the number of words (word count), Sardari: column 3 lines 66-67, column 9 lines 53-67m column 10 lines 1-21, column 25 lines 24-54.); and
generate, responsive to the determination, the arrangement of elements corresponding to a second word count (The personalization system can modify the content so that If the user has little time the text length is shortened, if the user prefers more detail, the text length is increased, Sardari: column 3 lines 66-67, column 9 lines 53-67m column 10 lines 1-21, column 25 lines 24-54.).
Claim 15:
The rejection of claim 1 is incorporated. Sardari further teaches a method comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a level of proficiency in a field (Sardari: column 4 lines 53-60, column 8 lines 12-49 and column 25 lines 1-54); and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold (Sardari: column 8 lines 12-49, column 17 lines 45-67, column 18 lines 1-10 and column 25 lines 1-54).
Claim 19:
The rejection of claim 16 is incorporated. Sardari further teaches a method comprising:
determining, by the one or more processors, that the first content satisfies a threshold for the characteristic, the threshold corresponding to at least one of: a value of a decay function corresponding to anxiety, a word count, a language, visual impairment, a hearing impairment, a neurocognitive challenge, a level of literacy or a level of proficiency in a field (It can be determined that a particular content is associated with a threshold text length/quantity which is measured by the number of words (word count), Sardari: column 3 lines 66-67, column 9 lines 53-67m column 10 lines 1-21, column 25 lines 24-54.); and
generating, by the one or more processors responsive to the determination, the arrangement of elements that does not satisfy the threshold for the characteristic (The personalization system can modify the content so that If the user has little time the text length is shortened, if the user prefers more detail, the text length is increased, Sardari: column 3 lines 66-67, column 9 lines 53-67m column 10 lines 1-21, column 25 lines 24-54.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of V et al. (US 12,260,077 B1, filed on 09/25/2023, hereinafter “V”) .
Claim 3:
The rejection of claim 1 is incorporated. Sardari further teaches a system comprising the one or more processors to:
generate, responsive to the characteristic, a setting for the arrangement of elements to accommodate the characteristic (Sardar: column 8 lines 12-49, column 9 lines 53-67, column 10 lines 1-21 and column 13 lines 38-43).
Sardari does not appear to expressly teach a system comprising the one or more processors to:
store the setting into a profile of the account, the profile including one or more settings for one or more characteristics.
However, V teaches a system comprising the one or more processors to:
store the setting into a profile of the account, the profile including one or more settings for one or more characteristics (The system can use machine learning to determine one or more neurodiversity categories for the user based on the interaction data, V: column 6 lines 48-63. The system then generates a plurality of user interface parameters (settings) for each determined neurodiversity category (characteristic), V: column 2 lines 31-45. The generated plurality of user interface parameters (settings) associated with each determined neurodiversity category (characteristic) is stored in association with the user identifier of the user (profile of the account), V: column 10 lines 47-58.).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors to:
store the setting into a profile of the account, the profile including one or more settings for one or more characteristics, as taught by V.
One would have been motivated to make such a combination in order to provide customization settings that can be used to generate interfaces that are better suited to the user and provide an efficient means for preserving the settings for later use (V: column 6 lines 48-63, column 2 lines 31-45, column 10 lines 47-58.).
Claim(s) 4, 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Russi-Vigoya et al. (US 2024/0311583 A1, filed on 03/16/2023, hereinafter “Russi”).
Claim 4:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors to:
detect, based at least on a portion of the first content input into the one or more models, that the portion of the first content corresponds to the characteristic; and
generate, responsive to the detection, the arrangement of elements using the one or more models and the portion of the first content.
However, Russi teaches a system comprising the one or more processors to:
detect, based at least on a portion of the first content input into the one or more models, that the portion of the first content corresponds to the characteristic (Russi: ¶ [0038]); and
generate, responsive to the detection, the arrangement of elements using the one or more models and the portion of the first content, as taught by Russi (Russi: ¶ [0038], [0041]).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors to:
detect, based at least on a portion of the first content input into the one or more models, that the portion of the first content corresponds to the characteristic; and
generate, responsive to the detection, the arrangement of elements using the one or more models and the portion of the first content, as taught by Russi.
One would have been motivated to make such a combination in order to better adapt the interface to the particular needs of the user (Russi: ¶ [0038], [0041]).
Claim 12:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a neurocognitive challenge; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold.
However, Russi teaches a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a neurocognitive challenge (Russi: ¶ [0038]); and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold (Russi: ¶ [0038], [0041]-[0044]).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a neurocognitive challenge; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold, as taught by Russi.
One would have been motivated to make such a combination in order to better adapt the interface to the particular needs of the user (Russi: ¶ [0038], [0041]).
Claim 13:
The rejection of claim 12 is incorporated. Sardari in view of Russi teaches a system wherein the neurocognitive challenge includes at least one of: attention deficit hyperactivity disorder (ADHD), dyslexia, autism spectrum disorder (ASD), intellectual disability (ID), specific language impairment (SLI), nonverbal learning disorder (NVLD), Tourette syndrome, obsessive-compulsive disorder, down syndrome, dyscalculia, dysgraphia, dyspraxia, intellectual disabilities, mental health conditions, Prader-Willi syndrome, sensory processing disorders, social anxiety, Williams syndrome and visual processing disorder (VPD) (Russi: ¶ [0011], [0036].).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Singh (US 2023/0360388 A1, published 11/09/2023, hereinafter “Singh”).
Claim 5:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors to:
monitor one or more actions on elements of content generated by the one or more applications on the graphical user interface (The contextual information comprises actions5 on user interface elements such as booklets are monitored, Sardari: column 2 lines 17-34, column 3 lines 34-44.).
Sardari does not appear to expressly teach a system comprising the one or more processors to:
detect the one or more interactions responsive to at least an action of the one or more actions input into the one or more models.
However, Singh teaches a system comprising the one or more processors to:
detect the one or more interactions responsive to at least an action of the one or more actions input into the one or more models (One or more actions can be monitored on elements of content, Singh: ¶ [0033], [0090]. Interactions can be detected responsive to inputting actions into a model, Singh: Fig. 6A, ¶ [0111], [0154].).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors to:
detect the one or more interactions responsive to at least an action of the one or more actions input into the one or more models, as taught by Singh.
One would have been motivated to make such a combination in order to provide a more effective means for determining user interactions (Singh: ¶ [0003], [0004].).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Buryak et al. (US 2014/0335483 A1, published 11/13/2014, hereinafter “Buryak”).
Claim 8:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors to:
identify that the first content is written in a first language;
identify that the characteristic corresponds to a second language; and
generate, based at least on the first content, the arrangement of elements of a second content corresponding to the first content and written in a second language.
However, Buryak teaches a system comprising the one or more processors to:
identify that the first content is written in a first language (Buryak: ¶ [0026]);
identify that the characteristic corresponds to a second language (Buryak: ¶ [0002]-[0004], [0026].); and
generate, based at least on the first content, the arrangement of elements of a second content corresponding to the first content and written in a second language (Buryak: ¶ [0026]).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors to:
identify that the first content is written in a first language;
identify that the characteristic corresponds to a second language; and
generate, based at least on the first content, the arrangement of elements of a second content corresponding to the first content and written in a second language, as taught by Buryak.
One would have been motivated to make such a combination in order to improve the user’s experience by providing content is a language that the user has higher proficiency in (Buryak: ¶ [0002]-[0004], [0026].).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Buryak and further in view of Trivedi et al. (US 2023/0107968 A1, published 04/06/2023, hereinafter “Trivedi”).
Claim 9:
The rejection of claim 8 is incorporated. Sardari in view of Buryak does not appear to expressly teach a system comprising the one or more processors to generate at least one of the arrangement of elements or the second content using the one or more models trained with machine learning in the first language and the second language.
However, Trivedi teaches a system comprising the one or more processors to generate at least one of the arrangement of elements or the second content using the one or more models trained with machine learning in the first language and the second language (Trivedi: ¶ [0085], [0089]).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari in view of Buryak to comprise the one or more processors to generate at least one of the arrangement of elements or the second content using the one or more models trained with machine learning in the first language and the second language, as taught by Trivedi.
One would have been motivated to make such a combination in order to provide an effective means for translating the content from one language to another (Trivedi: ¶ [0085], [0089]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of O’Reilly et al. (US 2022/0375367 A1, published 11/24/2022).
Claim 10:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to visual impairment; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold.
However, O’Reilly teaches a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to visual impairment (The system can check to see if the content has font sizes smaller than 7 based on a visual impairment criteria, O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064].); and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold (If the font size is less than 7, the font size is changed to be larger than 7, O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064], [0078].).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to visual impairment; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold, as taught by O’Reilly.
One would have been motivated to make such a combination in order to provide to improve the user’s experience by customizing the user interface to better address the user’s particular needs (O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064], [0078].).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of O’Reilly and further in view of Mese et al. (US 2021/0043109 A1, published 02/11/2021, hereinafter “Mese”).
Claim 11:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to at least one of a visual impairment or a hearing impairment; and
generate, responsive to the determination, the arrangement of elements to be sounded over a speaker according to a volume setting.
However, O’Reilly teaches a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to at least one of a visual impairment or a hearing impairment (The system can check to see if the content has font sizes smaller than 7 based on a visual impairment criteria, O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064].); and
generate, responsive to the determination, the arrangement of elements (If the font size is less than 7, the font size is changed to be larger than 7, O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064], [0078].).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to at least one of a visual impairment or a hearing impairment; and
generate, responsive to the determination, the arrangement of elements, as taught by O’Reilly.
One would have been motivated to make such a combination in order to provide to improve the user’s experience by customizing the user interface to better address the user’s particular needs (O’Reilly: Figs. 5A-6B and 9A, ¶ [0063]-[0064], [0078].).
Sardari in view of O’Reilly does not appear to expressly teach a system wherein generating the arrangement of elements comprises the arrangement of elements to be sounded over a speaker according to a volume setting.
However, Mese teaches a system wherein generating the arrangement of elements comprises the arrangement of elements to be sounded over a speaker according to a volume setting (In addition to increasing the size of text to assist the visually impaired user, the text can also be audibly presented to the user, Mese: ¶ [0011]. The audio output would necessarily be output at some volume level (volume setting) of the speakers, Mese: ¶ [0053].).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari in view of O’Reilly wherein generating the arrangement of elements comprises the arrangement of elements to be sounded over a speaker according to a volume setting, as taught by Mese.
One would have been motivated to make such a combination in order to provide more effectively present the content to visually impaired users (Mese: ¶ [0011], [0053]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardari in view of Liu et al. (US 2017/0046970 A1, published 02/16/2017, hereinafter “Liu”).
Claim 14:
The rejection of claim 1 is incorporated. Sardari does not appear to expressly teach a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a level of literacy; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold.
However, Liu teaches a system comprising the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a level of literacy (Liu: ¶ [0021]-[0022]); and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold (Liu: ¶ [0021]-[0022]).
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Sardari to comprise the one or more processors configured to:
determine that the first content satisfies a threshold for the characteristic corresponding to a level of literacy; and
generate, responsive to the determination, the arrangement of elements that does not satisfy the threshold, as taught by Liu.
One would have been motivated to make such a combination in order to better adapt the interface to the particular needs of the user (Liu: ¶ [0021]-[0022]).
Allowable Subject Matter
Claims 6 and 18 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.
Conclusion
Examiner has cited particular columns and line and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure.
Cramer et al., US 2025/0110599 A1 (“The present disclosure generally relates to an artificial intelligence and machine learning system for adapting a user interface to user inputs, and more particularly to systems and methods for using artificial intelligence and machine learning to provide a user interface adapting to a user's neurological condition, user experience, and emotional state.” [0001])
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL RODRIGUEZ whose telephone number is (571)272-3633. The examiner can normally be reached Monday-Friday 5:30 am - 2:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL RODRIGUEZ/Primary Examiner, Art Unit 2178
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